From 8ee257a685acb341de4a391c96d8b1f25a29038d Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Mon, 31 Jul 2023 23:38:57 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- .../Direct_Access_SWOT_sim_Oceanography.html | 2 +- .../OISSS_L4_multimission_monthly_v1.html | 2 +- ...-2438e8b8-d128-41b5-b06d-53fb2efee43a.png} | Bin ...-419d3300-7fdc-404c-afae-9e97155fb347.png} | Bin .../SWOTHR_s3Access.html | 5726 ++++++++++------- .../figure-html/cell-10-output-2.png | Bin 0 -> 23542 bytes .../figure-html/cell-16-output-2.png | Bin 0 -> 50800 bytes .../figure-html/cell-19-output-1.png | Bin 0 -> 139286 bytes .../figure-html/cell-23-output-1.png | Bin 0 -> 82837 bytes .../figure-html/cell-30-output-2.png | Bin 209832 -> 0 bytes search.json | 4 +- sitemap.xml | 168 +- 13 files changed, 3429 insertions(+), 2475 deletions(-) rename notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/{f88cfef8-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png => e6c1e407-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png} (100%) rename notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/{f88cfef8-2-419d3300-7fdc-404c-afae-9e97155fb347.png => e6c1e407-2-419d3300-7fdc-404c-afae-9e97155fb347.png} (100%) create mode 100644 notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access_files/figure-html/cell-10-output-2.png create mode 100644 notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access_files/figure-html/cell-16-output-2.png create mode 100644 notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access_files/figure-html/cell-19-output-1.png create mode 100644 notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access_files/figure-html/cell-23-output-1.png delete mode 100644 notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access_files/figure-html/cell-30-output-2.png diff --git a/.nojekyll b/.nojekyll index 9ce298df..5377b8b9 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -cc7950bb \ No newline at end of file +66ca663c \ No newline at end of file diff --git a/external/Direct_Access_SWOT_sim_Oceanography.html b/external/Direct_Access_SWOT_sim_Oceanography.html index 13a11a28..bdae560d 100644 --- a/external/Direct_Access_SWOT_sim_Oceanography.html +++ b/external/Direct_Access_SWOT_sim_Oceanography.html @@ -828,7 +828,7 @@

-
#opens granules and loads into 
+
#opens granules and load into xarray dataset
 ds = xr.open_mfdataset(earthaccess.open(karin_results), combine='nested', concat_dim="num_lines", decode_times=False, engine='h5netcdf')
 ds

 

diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html b/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
index de0858b3..fe5342e3 100644
--- a/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
+++ b/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
@@ -742,7 +742,7 @@ 
Direct S3 Data Access tutorial (Multi-Mission Optimally Interp
 
 
 
This tutorial only works in a jupyterhub hosted at AWS US-WEST-2.

-
image.pngimage.png

+
image.pngimage.png

 
    
 
  • User guide: http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdf
    • 
 
  • DOI 10.5067/SMP10-4UMCS
    • 
diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/f88cfef8-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png b/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/e6c1e407-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
similarity index 100%
rename from notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/f88cfef8-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
rename to notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/e6c1e407-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/f88cfef8-2-419d3300-7fdc-404c-afae-9e97155fb347.png b/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/e6c1e407-2-419d3300-7fdc-404c-afae-9e97155fb347.png
similarity index 100%
rename from notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/f88cfef8-2-419d3300-7fdc-404c-afae-9e97155fb347.png
rename to notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/e6c1e407-2-419d3300-7fdc-404c-afae-9e97155fb347.png
diff --git a/notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html b/notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html
index 45ab185d..55fd7e9c 100644
--- a/notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html
+++ b/notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html
@@ -717,7 +717,6 @@ 
    On this page
    
   
  • Earthdata Login
    • 
   

   
  • Single File Access
    • 
-  
  • Multi-File
    • 
   
 
    Edit this page
    Report an issue
    
     
    
@@ -753,7 +752,7 @@ 
    Requirement:
    
 
    
 
    Learning Objectives:
    
 
      
-
    • Access all 6 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine
      • 
+
    • Access all 5 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine
      • 
 
    • Visualize accessed data
      • 
 
    
 
    
@@ -789,13 +788,7 @@ 
    
 
    DOI: https://doi.org/10.5067/KARIN-2RAS1
    
 
-
      
-
    1. KaRIn Sea Surface Height from GLORYSs - SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1
      2. 
-
    
-
    
-
    DOI: https://doi.org/10.5067/KARIN-2GLC1
    
-
    
-
    Notebook Authors: Cassie Nickles and Zoë Walschots, NASA PO.DAAC (July 2023)
    
+
    Notebook Author: Cassie Nickles, NASA PO.DAAC (Aug 2022)
    
 
 
    
 
    Libraries Needed
    
@@ -805,20 +798,17 @@ 
    Libraries Needed
    
 import requests
 import s3fs
 import netCDF4 as nc
-from netCDF4 import Dataset
-import h5netcdf
-import xarray as xr
-import pandas as pd
-import geopandas as gpd
-import numpy as np
-import matplotlib.pyplot as plt
-import hvplot.xarray
-import cartopy.crs as ccrs
-import cartopy.feature as cfeat
-import shapefile as shp
-import zipfile
-import earthaccess
-from earthaccess import Auth, DataCollections, DataGranules, Store
    +import h5netcdf +import xarray as xr +import pandas as pd +import geopandas as gpd +import numpy as np +import matplotlib.pyplot as plt +import hvplot.xarray +import shapefile as shp +import zipfile +import earthaccess +from earthaccess import Auth, DataCollections, DataGranules, Store
    -
    +
    @@ -3281,42 +3832,30 @@

    Multi-File

    fill: currentColor; } 
    <xarray.Dataset>
-Dimensions:                                (num_lines: 286144, num_pixels: 71,
-                                            num_sides: 2)
+Dimensions:                                (points: 489673, complex_depth: 2)
 Coordinates:
-    latitude                               (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    longitude                              (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    latitude_nadir                         (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
-    longitude_nadir                        (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
-Dimensions without coordinates: num_lines, num_pixels, num_sides
-Data variables: (12/91)
-    time                                   (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
-    time_tai                               (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
-    ssh_karin                              (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    ssh_karin_uncert                       (num_lines, num_pixels) float32 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    ssha_karin                             (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    ssh_karin_2                            (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
+    latitude                               (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
+    longitude                              (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
+Dimensions without coordinates: points, complex_depth
+Data variables: (12/49)
+    azimuth_index                          (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
+    range_index                            (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
+    interferogram                          (points, complex_depth) float32 dask.array<chunksize=(489673, 2), meta=np.ndarray>
+    power_plus_y                           (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
+    power_minus_y                          (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
+    coherent_power                         (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
     ...                                     ...
-    simulated_error_karin                  (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    simulated_error_timing                 (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    simulated_error_baseline_dilation      (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    simulated_error_roll                   (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    simulated_error_phase                  (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-    simulated_error_orbital                (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
-Attributes: (12/32)
-    Conventions:                CF-1.7
-    title:                      Level 2 Low Rate Sea Surface Height Data Prod...
-    institution:                CNES/JPL
-    source:                     Simulate product
-    history:                    2021-09-10 10:37:14Z : Creation
-    platform:                   SWOT
-    ...                         ...
-    right_last_longitude:       157.4108182844071
-    right_last_latitude:        77.03307806541929
-    wavelength:                 0.008385803020979
-    orbit_solution:             POE
-    ellipsoid_semi_major_axis:  6378137.0
-    ellipsoid_flattening:       0.003352810664781205
    -ssha_karin +x_factor_plus_y
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -sea surface height anomaly +X factor for plus_y channel power
    units :
    -m +1
    valid_min :
    --1000000 +0.0
    valid_max :
    -1000000 +999999.0
    comment :
    -Sea surface height anomaly from the KaRIn measurement = ssh_karin - mean_sea_surface_cnescls - solid_earth_tide - ocean_tide_fes – internal_tide_hret - pole_tide - dac. +X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).
    @@ -3999,67 +4514,63 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -ssh_karin_2 +x_factor_minus_y
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -sea surface height -
    -
    standard_name :
    -
    -sea surface height above reference ellipsoid +X factor for minus_y channel power
    units :
    -m +1
    valid_min :
    --15000000 +0.0
    valid_max :
    -150000000 +999999.0
    comment :
    -Fully corrected sea surface height measured by KaRIn. The height is relative to the reference ellipsoid defined in the global attributes. This value is computed using model-based estimates for wet troposphere effects on the KaRIn measurement (e.g., model_wet_tropo_cor and sea_state_bias_cor_2). +X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).
    @@ -4082,63 +4593,63 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -ssha_karin_2 +water_frac
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -sea surface height anomaly +water fraction
    units :
    -m +1
    valid_min :
    --1000000 +-1000.0
    valid_max :
    -1000000 +10000.0
    comment :
    -Sea surface height anomaly from the KaRIn measurement = ssh_karin_2 - mean_sea_surface_cnescls - solid_earth_tide - ocean_tide_fes – internal_tide_hret - pole_tide - dac. +Noisy estimate of the fraction of the pixel that is water.
    @@ -4161,71 +4672,63 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -ssha_karin_qual +water_frac_uncert
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -sea surface height quality flag -
    -
    standard_name :
    -
    -status_flag -
    -
    flag_meanings :
    -
    -good bad +water fraction uncertainty
    -
    flag_values :
    +
    units :
    -[0 1] +1
    valid_min :
    -0 +0.0
    valid_max :
    -1 +999999.0
    comment :
    -Quality flag for the SSHA from KaRIn. +Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).
    @@ -4248,51 +4751,67 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -polarization_karin +classification
    -(num_lines, num_sides) +(points)
    -object +float32
    -dask.array<chunksize=(9868, 2), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -polarization for each side of the KaRIn swath +classification +
    +
    flag_meanings :
    +
    +land land_near_water water_near_land open_water land_near_dark_water dark_water_edge dark_water +
    +
    flag_values :
    +
    +[ 1 2 3 4 22 23 24] +
    +
    valid_min :
    +
    +1 +
    +
    valid_max :
    +
    +24
    comment :
    -H denotes co-polarized linear horizontal, V denotes co-polarized linear vertical. +Flags indicating water detection results.
    @@ -4315,67 +4834,63 @@

    Multi-File

    Bytes -4.37 MiB -154.19 kiB +1.87 MiB +1.87 MiB Shape -(286144, 2) -(9868, 2) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -object numpy.ndarray +float32 numpy.ndarray - +
    -swh_karin +false_detection_rate
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -significant wave height from KaRIn -
    -
    standard_name :
    -
    -sea_surface_wave_significant_height +false detection rate
    units :
    -m +1
    valid_min :
    -0 +0.0
    valid_max :
    -25000 +1.0
    comment :
    -Significant wave height from KaRIn volumetric correlation. +Probability of falsely detecting water when there is none.
    @@ -4398,17 +4913,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -4416,45 +4931,45 @@

    Multi-File

    - +
    -swh_karin_uncert +missed_detection_rate
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -1-sigma uncertainty on significant wave height from KaRIn +missed detection rate
    units :
    -m +1
    valid_min :
    -0 +0.0
    valid_max :
    -25000 +1.0
    comment :
    -1-sigma uncertainty on significant wave height from KaRIn. +Probability of falsely detecting no water when there is water.
    @@ -4477,17 +4992,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -4495,33 +5010,29 @@

    Multi-File

    - +
    -sig0_karin +prior_water_prob
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -normalized radar cross section (sigma0) from KaRIn -
    -
    standard_name :
    -
    -surface_backwards_scattering_coefficient_of_radar_wave +prior water probability
    units :
    @@ -4529,15 +5040,15 @@

    Multi-File

    valid_min :
    --1000.0 +0.0
    valid_max :
    -10000000.0 +1.0
    comment :
    -Normalized radar cross section (sigma0) from KaRIn in real, linear units (not decibels). The value may be negative due to noise subtraction. The value is corrected for instrument calibration and atmospheric attenuation. Radiometer measurements provide the atmospheric attenuation (sig0_cor_atmos_rad). +Prior probability of water occurring.
    @@ -4560,17 +5071,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -4578,45 +5089,53 @@

    Multi-File

    - +
    -sig0_karin_uncert +bright_land_flag
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -1-sigma uncertainty on sigma0 from KaRIn +bright land flag
    -
    units :
    +
    standard_name :
    -1 +status_flag +
    +
    flag_meanings :
    +
    +not_bright_land bright_land bright_land_or_water +
    +
    flag_values :
    +
    +[0 1 2]
    valid_min :
    -0.0 +0
    valid_max :
    -1000.0 +2
    comment :
    -1-sigma uncertainty on sigma0 from KaRIn. +Flag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice). Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.
    @@ -4639,17 +5158,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -4657,49 +5176,45 @@

    Multi-File

    - +
    -sig0_karin_2 +layover_impact
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -normalized radar cross section (sigma0) from KaRIn -
    -
    standard_name :
    -
    -surface_backwards_scattering_coefficient_of_radar_wave +layover impact
    units :
    -1 +m
    valid_min :
    --1000.0 +-999999.0
    valid_max :
    -10000000.0 +999999.0
    comment :
    -Normalized radar cross section (sigma0) from KaRIn in real, linear units (not decibels). The value may be negative due to noise subtraction. The value is corrected for instrument calibration and atmospheric attenuation. A meteorological model provides the atmospheric attenuation (sig0_cor_atmos_model). +Estimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.
    @@ -4722,17 +5237,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -4740,53 +5255,45 @@

    Multi-File

    - +
    -wind_speed_karin +eff_num_rare_looks
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -wind speed from KaRIn -
    -
    standard_name :
    -
    -wind_speed -
    -
    source :
    -
    -TBD +effective number of rare looks
    units :
    -m/s +1
    valid_min :
    -0 +0.0
    valid_max :
    -65000 +999999.0
    comment :
    -Wind speed from KaRIn computed from sig0_karin. +Effective number of independent looks taken to form the rare interferogram.
    @@ -4809,17 +5316,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -4827,53 +5334,45 @@

    Multi-File

    - +
    -wind_speed_karin_2 +height
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -wind speed from KaRIn +height above reference ellipsoid
    -
    standard_name :
    +
    units :
    -wind_speed +m
    -
    source :
    +
    valid_min :
    -TBD +-1500.0
    -
    units :
    +
    valid_max :
    -m/s -
    -
    valid_min :
    -
    -0 -
    -
    valid_max :
    -
    -65000 +15000.0
    comment :
    -Wind speed from KaRIn computed from sig0_karin_2. +Height of the pixel above the reference ellipsoid.
    @@ -4896,17 +5395,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -4914,53 +5413,45 @@

    Multi-File

    - +
    -swh_karin_qual +cross_track
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -quality flag for significant wave height from KaRIn. -
    -
    standard_name :
    -
    -status_flag -
    -
    flag_meanings :
    -
    -good bad +approximate cross-track location
    -
    flag_values :
    +
    units :
    -[0 1] +m
    valid_min :
    -0 +-75000.0
    valid_max :
    -1 +75000.0
    comment :
    -Quality flag for significant wave height from KaRIn. +Approximate cross-track location of the pixel.
    @@ -4983,71 +5474,63 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -sig0_karin_qual +pixel_area
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -quality flag for sigma0 from KaRIn. -
    -
    standard_name :
    -
    -status_flag -
    -
    flag_meanings :
    -
    -good bad +pixel area
    -
    flag_values :
    +
    units :
    -[0 1] +m^2
    valid_min :
    -0 +0.0
    valid_max :
    -1 +999999.0
    comment :
    -Quality flag for sigma0 from KaRIn. +Pixel area.
    @@ -5070,63 +5553,63 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -num_pt_avg +inc
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -number of samples averaged +incidence angle
    units :
    -1 +degrees
    valid_min :
    -0 +0.0
    valid_max :
    -289 +999999.0
    comment :
    -Number of native unsmoothed, beam-combined KaRIn samples averaged. +Incidence angle.
    @@ -5149,17 +5632,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5167,57 +5650,45 @@

    Multi-File

    - +
    -swh_model +phase_noise_std
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -significant wave height from wave model -
    -
    standard_name :
    -
    -sea_surface_wave_significant_height -
    -
    source :
    -
    -European Centre for Medium-Range Weather Forecasts -
    -
    institution :
    -
    -ECMWF +phase noise standard deviation
    units :
    -m +radians
    valid_min :
    -0 +-999999.0
    valid_max :
    -30000 +999999.0
    comment :
    -Significant wave height from model. +Estimate of the phase noise standard deviation.
    @@ -5240,17 +5711,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5258,53 +5729,45 @@

    Multi-File

    - +
    -mean_wave_direction +dlatitude_dphase
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -mean sea surface wave direction -
    -
    source :
    -
    -Meteo France Wave Model (MF-WAM) -
    -
    institution :
    -
    -Meteo France +sensitivity of latitude estimate to interferogram phase
    units :
    -degree +degrees/radian
    valid_min :
    -0 +-999999.0
    valid_max :
    -36000 +999999.0
    comment :
    -Mean sea surface wave direction. +Sensitivity of the latitude estimate to the interferogram phase.
    @@ -5327,17 +5790,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5345,57 +5808,45 @@

    Multi-File

    - +
    -mean_wave_period_t02 +dlongitude_dphase
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -sea surface wind wave mean period -
    -
    standard_name :
    -
    -sea_surface_wave_significant_period -
    -
    source :
    -
    -Meteo France Wave Model (MF-WAM) -
    -
    institution :
    -
    -Meteo France +sensitivity of longitude estimate to interferogram phase
    units :
    -s +degrees/radian
    valid_min :
    -0 +-999999.0
    valid_max :
    -100 +999999.0
    comment :
    -Sea surface wind wave mean period from model spectral density second moment. +Sensitivity of the longitude estimate to the interferogram phase.
    @@ -5418,17 +5869,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5436,57 +5887,45 @@

    Multi-File

    - +
    -wind_speed_model_u +dheight_dphase
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -u component of model wind -
    -
    standard_name :
    -
    -eastward_wind -
    -
    source :
    -
    -European Centre for Medium-Range Weather Forecasts -
    -
    institution :
    -
    -ECMWF +sensitivity of height estimate to interferogram phase
    units :
    -m/s +m/radian
    valid_min :
    --30000 +-999999.0
    valid_max :
    -30000 +999999.0
    comment :
    -Eastward component of the atmospheric model wind vector at 10 meters. +Sensitivity of the height estimate to the interferogram phase.
    @@ -5509,17 +5948,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5527,57 +5966,45 @@

    Multi-File

    - +
    -wind_speed_model_v +dheight_droll
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -v component of model wind -
    -
    standard_name :
    -
    -northward_wind -
    -
    source :
    -
    -European Centre for Medium-Range Weather Forecasts -
    -
    institution :
    -
    -ECMWF +sensitivity of height estimate to spacecraft roll
    units :
    -m/s +m/degrees
    valid_min :
    --30000 +-999999.0
    valid_max :
    -30000 +999999.0
    comment :
    -Northward component of the atmospheric model wind vector at 10 meters. +Sensitivity of the height estimate to the spacecraft roll.
    @@ -5600,17 +6027,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5618,53 +6045,45 @@

    Multi-File

    - +
    -wind_speed_rad +dheight_dbaseline
    -(num_lines, num_sides) +(points)
    float32
    -dask.array<chunksize=(9868, 2), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -wind speed from radiometer -
    -
    standard_name :
    -
    -wind_speed -
    -
    source :
    -
    -Advanced Microwave Radiometer +sensitivity of height estimate to interferometric baseline
    units :
    -m/s +m/m
    valid_min :
    -0 +-999999.0
    valid_max :
    -65000 +999999.0
    comment :
    -Wind speed from radiometer measurements. +Sensitivity of the height estimate to the interferometric baseline.
    @@ -5687,17 +6106,17 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +1.87 MiB +1.87 MiB Shape -(286144, 2) -(9868, 2) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5705,53 +6124,45 @@

    Multi-File

    - +
    -distance_to_coast +dheight_drange
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -distance to coast -
    -
    source :
    -
    -MODIS/GlobCover -
    -
    institution :
    -
    -European Space Agency +sensitivity of height estimate to range (delay)
    units :
    -m +m/m
    valid_min :
    -0 +-999999.0
    valid_max :
    -21000 +999999.0
    comment :
    -Approximate distance to the nearest coast point along the Earth surface. +Sensitivity of the height estimate to the range (delay).
    @@ -5774,17 +6185,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5792,45 +6203,45 @@

    Multi-File

    - +
    -heading_to_coast +darea_dheight
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -heading to coast +sensitivity of pixel area to reference height
    units :
    -degrees +m^2/m
    valid_min :
    -0 +-999999.0
    valid_max :
    -35999 +999999.0
    comment :
    -Approximate compass heading (0-360 degrees with respect to true north) to the nearest coast point. +Sensitivity of the pixel area to the reference height.
    @@ -5853,17 +6264,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -5871,61 +6282,45 @@

    Multi-File

    - +
    -ancillary_surface_classification_flag +illumination_time
    -(num_lines, num_pixels) +(points)
    -float32 +datetime64[ns]
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -surface classification +time of illumination of each pixel (UTC)
    standard_name :
    -status_flag -
    -
    source :
    -
    -MODIS/GlobCover -
    -
    institution :
    -
    -European Space Agency -
    -
    flag_meanings :
    -
    -open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin -
    -
    flag_values :
    -
    -[0 1 2 3 4 5 6] +time
    -
    valid_min :
    +
    tai_utc_difference :
    -0 +[Value of TAI-UTC at time of first record]
    -
    valid_max :
    +
    leap_second :
    -6 +YYYY-MM-DD hh:mm:ss
    comment :
    -7-state surface type classification computed from a mask built with MODIS and GlobCover data. +Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.
    @@ -5948,79 +6343,55 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +3.74 MiB +3.74 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +datetime64[ns] numpy.ndarray - +
    -dynamic_ice_flag +illumination_time_tai
    -(num_lines, num_pixels) +(points)
    -float32 +datetime64[ns]
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -dynamic ice flag +time of illumination of each pixel (TAI)
    standard_name :
    -status_flag -
    -
    source :
    -
    -EUMETSAT Ocean and Sea Ice Satellite Applications Facility -
    -
    institution :
    -
    -EUMETSAT -
    -
    flag_meanings :
    -
    -no_ice probable_ice ice -
    -
    flag_values :
    -
    -[0 1 2] -
    -
    valid_min :
    -
    -0 -
    -
    valid_max :
    -
    -2 +time
    comment :
    -Dynamic ice flag for the location of the KaRIn measurement. +Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].
    @@ -6043,71 +6414,63 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +3.74 MiB +3.74 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +datetime64[ns] numpy.ndarray - +
    -rain_flag +eff_num_medium_looks
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -rain flag +effective number of medium looks
    -
    standard_name :
    -
    -status_flag -
    -
    flag_meanings :
    -
    -no_rain probable_rain rain -
    -
    flag_values :
    +
    units :
    -[0 1 2] +1
    valid_min :
    -0 +0.0
    valid_max :
    -2 +999999.0
    comment :
    -Flag indicates that signal is attenuated, probably from rain. +Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).
    @@ -6130,17 +6493,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -6148,57 +6511,45 @@

    Multi-File

    - +
    -rad_surface_type_flag +sig0
    -(num_lines, num_sides) +(points)
    float32
    -dask.array<chunksize=(9868, 2), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -radiometer surface type flag -
    -
    standard_name :
    -
    -status_flag -
    -
    source :
    -
    -Advanced Microwave Radiometer -
    -
    flag_meanings :
    -
    -open_ocean coastal_ocean land +sigma0
    -
    flag_values :
    +
    units :
    -[0 1 2] +1
    valid_min :
    -0 +-999999.0
    valid_max :
    -2 +999999.0
    comment :
    -Flag indicating the validity and type of processing applied to generate the wet troposphere correction (rad_wet_tropo_cor). A value of 0 indicates that open ocean processing is used, a value of 1 indicates coastal processing, and a value of 2 indicates that rad_wet_tropo_cor is invalid due to land contamination. +Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.
    @@ -6221,17 +6572,17 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +1.87 MiB +1.87 MiB Shape -(286144, 2) -(9868, 2) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -6239,49 +6590,45 @@

    Multi-File

    - +
    -sc_altitude +phase_unwrapping_region
    -(num_lines) +(points)
    float64
    -dask.array<chunksize=(9868,), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -altitude of KMSF origin -
    -
    standard_name :
    -
    -height_above_reference_ellipsoid +phase unwrapping region index
    units :
    -m +1
    valid_min :
    -0 +-1
    valid_max :
    -2000000000 +99999999
    comment :
    -Altitude of the KMSF origin. +Phase unwrapping region index.
    @@ -6304,17 +6651,17 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +3.74 MiB +3.74 MiB Shape -(286144,) -(9868,) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -6322,45 +6669,45 @@

    Multi-File

    - +
    -orbit_alt_rate +instrument_range_cor
    -(num_lines) +(points)
    float32
    -dask.array<chunksize=(9868,), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -orbital altitude rate with respect to mean sea surface +instrument range correction
    units :
    -m/s +m
    valid_min :
    --3500 +-999999.0
    valid_max :
    -3500 +999999.0
    comment :
    -Orbital altitude rate with respect to the mean sea surface. +Term that incorporates all calibration corrections applied to range before geolocation.
    @@ -6383,17 +6730,17 @@

    Multi-File

    Bytes -1.09 MiB -38.55 kiB +1.87 MiB +1.87 MiB Shape -(286144,) -(9868,) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -6401,45 +6748,45 @@

    Multi-File

    - +
    -cross_track_angle +instrument_phase_cor
    -(num_lines) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868,), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -cross-track angle from true north +instrument phase correction
    units :
    -degrees +radians
    valid_min :
    -0 +-999999.0
    valid_max :
    -359999999 +999999.0
    comment :
    -Angle with respect to true north of the cross-track direction to the right of the spacecraft velocity vector. +Term that incorporates all calibration corrections applied to phase before geolocation.
    @@ -6462,67 +6809,63 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +1.87 MiB +1.87 MiB Shape -(286144,) -(9868,) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -sc_roll +instrument_baseline_cor
    -(num_lines) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868,), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -roll of the spacecraft -
    -
    standard_name :
    -
    -platform_roll_angle +instrument baseline correction
    units :
    -degrees +m
    valid_min :
    --1799999 +-999999.0
    valid_max :
    -1800000 +999999.0
    comment :
    -KMSF attitude roll angle; positive values move the +y antenna down. +Term that incorporates all calibration corrections applied to baseline before geolocation.
    @@ -6545,51 +6888,47 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +1.87 MiB +1.87 MiB Shape -(286144,) -(9868,) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -sc_pitch +instrument_attitude_cor
    -(num_lines) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868,), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -pitch of the spacecraft -
    -
    standard_name :
    -
    -platform_pitch_angle +instrument attitude correction
    units :
    @@ -6597,15 +6936,15 @@

    Multi-File

    valid_min :
    --1799999 +-999999.0
    valid_max :
    -1800000 +999999.0
    comment :
    -KMSF attitude pitch angle; positive values move the KMSF +x axis up. +Term that incorporates all calibration corrections applied to attitude before geolocation.
    @@ -6628,67 +6967,71 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +1.87 MiB +1.87 MiB Shape -(286144,) -(9868,) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -sc_yaw +model_dry_tropo_cor
    -(num_lines) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868,), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -yaw of the spacecraft +dry troposphere vertical correction
    -
    standard_name :
    +
    source :
    +
    +European Centre for Medium-Range Weather Forecasts +
    +
    institution :
    -platform_yaw_angle +ECMWF
    units :
    -degrees +m
    valid_min :
    --1799999 +-3.0
    valid_max :
    -1800000 +-1.5
    comment :
    -KMSF attitude yaw angle relative to the nadir track. The yaw angle is a right-handed rotation about the nadir (downward) direction. A yaw value of 0 deg indicates that the KMSF +x axis is aligned with the horizontal component of the Earth-relative velocity vector. A yaw value of 180 deg indicates that the spacecraft is in a yaw-flipped state, with the KMSF -x axis aligned with the horizontal component of the Earth-relative velocity vector. +Equivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.
    @@ -6711,142 +7054,71 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +1.87 MiB +1.87 MiB Shape -(286144,) -(9868,) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -velocity_heading +model_wet_tropo_cor
    -(num_lines) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868,), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -heading of the spacecraft Earth-relative velocity vector +wet troposphere vertical correction
    -
    units :
    +
    source :
    -degrees -
    -
    valid_min :
    -
    -0 -
    -
    valid_max :
    -
    -359999999 -
    -
    comment :
    -
    -Angle with respect to true north of the horizontal component of the spacecraft Earth-relative velocity vector. A value of 90 deg indicates that the spacecraft velocity vector pointed due east. Values between 0 and 90 deg indicate that the velocity vector has a northward component, and values between 90 and 180 deg indicate that the velocity vector has a southward component. +European Centre for Medium-Range Weather Forecasts
    -
    -
    -
    - ---- - - - - - - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ArrayChunk
    Bytes2.18 MiB77.09 kiB
    Shape(286144,)(9868,)
    Dask graph29 chunks in 59 graph layers
    Data typefloat64 numpy.ndarray
    -
    -
    -orbit_qual -
    -
    -(num_lines) -
    -
    -float32 -
    -
    -dask.array<chunksize=(9868,), meta=np.ndarray> -
    - -
    -
    -
    long_name :
    +
    institution :
    -orbit quality flag +ECMWF
    -
    standard_name :
    +
    units :
    -status_flag +m
    valid_min :
    -0 +-1.0
    valid_max :
    -1 +0.0
    comment :
    -Orbit quality flag. +Equivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.
    @@ -6869,17 +7141,17 @@

    Multi-File

    Bytes -1.09 MiB -38.55 kiB +1.87 MiB +1.87 MiB Shape -(286144,) -(9868,) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -6887,49 +7159,53 @@

    Multi-File

    - +
    -latitude_avg_ssh +iono_cor_gim_ka
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -weighted average latitude of samples used to compute SSH +ionosphere vertical correction
    -
    standard_name :
    +
    source :
    -latitude +Global Ionosphere Maps +
    +
    institution :
    +
    +JPL
    units :
    -degrees_north +m
    valid_min :
    --80000000 +-0.5
    valid_max :
    -80000000 +0.0
    comment :
    -Latitude of measurement [-80,80]. Positive latitude is North latitude, negative latitude is South latitude. This value may be biased away from a nominal grid location if some of the native, unsmoothed samples were discarded during processing. +Equivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.
    @@ -6952,67 +7228,63 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -longitude_avg_ssh +height_cor_xover
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -weighted average longitude of samples used to compute SSH -
    -
    standard_name :
    -
    -longitude +height correction from KaRIn crossovers
    units :
    -degrees_east +m
    valid_min :
    -0 +-10.0
    valid_max :
    -359999999 +10.0
    comment :
    -Longitude of measurement. East longitude relative to Greenwich meridian. This value may be biased away from a nominal grid location if some of the native, unsmoothed samples were discarded during processing. +Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.
    @@ -7035,47 +7307,55 @@

    Multi-File

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    -cross_track_distance +geoid
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -cross track distance +geoid height +
    +
    standard_name :
    +
    +geoid_height_above_reference_ellipsoid +
    +
    source :
    +
    +EGM2008 (Pavlis et al., 2012)
    units :
    @@ -7083,15 +7363,15 @@

    Multi-File

    valid_min :
    --75000.0 +-150.0
    valid_max :
    -75000.0 +150.0
    comment :
    -Distance of sample from nadir. Negative values indicate the left side of the swath, and positive values indicate the right side of the swath. +Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).
    @@ -7114,17 +7394,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -7132,45 +7412,49 @@

    Multi-File

    - +
    -x_factor +solid_earth_tide
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -radiometric calibration X factor as a composite value for the X factors of the +y and -y channels +solid Earth tide height +
    +
    source :
    +
    +Cartwright and Taylor (1971) and Cartwright and Edden (1973)
    units :
    -1 +m
    valid_min :
    -0.0 +-1.0
    valid_max :
    -1e+20 +1.0
    comment :
    -Radiometric calibration X factor as a linear power ratio. +Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.
    @@ -7193,17 +7477,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -7211,53 +7495,53 @@

    Multi-File

    - +
    -sig0_cor_atmos_model +load_tide_fes
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -two-way atmospheric correction to sigma0 from model +geocentric load tide height (FES)
    source :
    -European Centre for Medium-Range Weather Forecasts +FES2014b (Carrere et al., 2016)
    institution :
    -ECMWF +LEGOS/CNES
    units :
    -1 +m
    valid_min :
    -1.0 +-0.2
    valid_max :
    -10.0 +0.2
    comment :
    -Atmospheric correction to sigma0 from weather model data as a linear power multiplier (not decibels). sig0_cor_atmos_model is already applied in computing sig0_karin_2. +Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.
    @@ -7280,17 +7564,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -7298,49 +7582,53 @@

    Multi-File

    - +
    -sig0_cor_atmos_rad +load_tide_got
    -(num_lines, num_pixels) +(points)
    float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -two-way atmospheric correction to sigma0 from radiometer data +geocentric load tide height (GOT)
    source :
    -Advanced Microwave Radiometer +GOT4.10c (Ray, 2013) +
    +
    institution :
    +
    +GSFC
    units :
    -1 +m
    valid_min :
    -1.0 +-0.2
    valid_max :
    -10.0 +0.2
    comment :
    -Atmospheric correction to sigma0 from radiometer data as a linear power multiplier (not decibels). sig0_cor_atmos_rad is already applied in computing sig0_karin. +Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.
    @@ -7363,17 +7651,17 @@

    Multi-File

    Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -7381,45 +7669,49 @@

    Multi-File

    - +
    -doppler_centroid +pole_tide
    -(num_lines, num_sides) +(points)
    float32
    -dask.array<chunksize=(9868, 2), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -doppler centroid estimated by KaRIn +geocentric pole tide height +
    +
    source :
    +
    +Wahr (1985) and Desai et al. (2015)
    units :
    -1/s +m
    valid_min :
    --30000 +-0.2
    valid_max :
    -30000 +0.2
    comment :
    -Doppler centroid (in hertz or cycles per second) estimated by KaRIn. +Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust).
    @@ -7442,17 +7734,17 @@

    Multi-File

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    Multi-File

    - +
    -phase_bias_ref_surface +ancillary_surface_classification_flag
    -(num_lines, num_pixels) +(points)
    -float64 +float32
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    long_name :
    -height of reference surface used for phase bias calculation -
    -
    units :
    -
    -m +surface classification
    -
    valid_min :
    +
    standard_name :
    --15000000 +status_flag
    -
    valid_max :
    +
    source :
    -150000000 +MODIS/GlobCover
    -
    comment :
    +
    institution :
    -Height (relative to the reference ellipsoid) of the reference surface used for phase bias calculation during L1B processing. +European Space Agency
    -
    -
    -
    - ---- - - - - - - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ArrayChunk
    Bytes155.00 MiB5.35 MiB
    Shape(286144, 71)(9868, 71)
    Dask graph29 chunks in 59 graph layers
    Data typefloat64 numpy.ndarray
    -
    -
    -obp_ref_surface -
    -
    -(num_lines, num_pixels) -
    -
    -float64 -
    -
    -dask.array<chunksize=(9868, 71), meta=np.ndarray> -
    - -
    -
    -
    long_name :
    +
    flag_meanings :
    -height of reference surface used by on-board-processor +open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin
    -
    units :
    +
    flag_values :
    -m +[0 1 2 3 4 5 6]
    valid_min :
    --15000000 +0
    valid_max :
    -150000000 +6
    comment :
    -Height (relative to the reference ellipsoid) of the reference surface used by the KaRIn on-board processor. +7-state surface type classification computed from a mask built with MODIS and GlobCover data.
    @@ -7600,71 +7829,67 @@

    Multi-File

    Bytes -155.00 MiB -5.35 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
    -rad_tmb_187 +pixc_qual
    -(num_lines, num_sides) +(points)
    float32
    -dask.array<chunksize=(9868, 2), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
    -
    long_name :
    -
    -radiometer main beam brightness temperature at 18.7 GHz -
    standard_name :
    -toa_brightness_temperature +status_flag
    -
    source :
    +
    flag_meanings :
    -Advanced Microwave Radiometer +good bad
    -
    units :
    +
    flag_values :
    -K +[0 1]
    valid_min :
    -13000 +0
    valid_max :
    -25000 +1
    comment :
    -Main beam brightness temperature measurement at 18.7 GHz. Value is unsmoothed (along-track averaging has not been performed). +Quality flag for pixel cloud data
    @@ -7687,17 +7912,17 @@

    Multi-File

    Bytes -2.18 MiB -77.09 kiB +1.87 MiB +1.87 MiB Shape -(286144, 2) -(9868, 2) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -7705,401 +7930,529 @@

    Multi-File

    - + -
    -rad_tmb_238
    -
    -(num_lines, num_sides) +
    • description :
      cloud of geolocated interferogram pixels
      interferogram_size_azimuth :
      2923
      interferogram_size_range :
      4575
      looks_to_efflooks :
      1.75
      • -
      -float32
      -
      -dask.array<chunksize=(9868, 2), meta=np.ndarray> +
      +
      plt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)
+plt.colorbar().set_label('Height (m)')
      +
      +

      - -
      -
      -
      long_name :
      -
      -radiometer main beam brightness temperature at 23.8 GHz -
      -
      standard_name :
      -
      -toa_brightness_temperature -
      -
      source :
      -
      -Advanced Microwave Radiometer -
      -
      units :
      -
      -K -
      -
      valid_min :
      -
      -13000 -
      -
      valid_max :
      -
      -25000 -
      -
      comment :
      -
      -Main beam brightness temperature measurement at 23.8 GHz. Value is unsmoothed (along-track averaging has not been performed). -
      -
      -
      -
      - ---- - - - - - - -
      - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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      Shape(286144, 2)(9868, 2)
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      -
      -
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      -
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      -
      -float32 -
      -
      -dask.array<chunksize=(9868, 2), meta=np.ndarray> -
      - -
      -
      -
      long_name :
      -
      -radiometer main beam brightness temperature at 34.0 GHz -
      -
      standard_name :
      -
      -toa_brightness_temperature -
      -
      source :
      -
      -Advanced Microwave Radiometer -
      -
      units :
      -
      -K -
      -
      valid_min :
      -
      -15000 -
      -
      valid_max :
      -
      -28000 -
      -
      comment :
      -
      -Main beam brightness temperature measurement at 34.0 GHz. Value is unsmoothed (along-track averaging has not been performed). -
      -
      -
      -
      - ---- - - - - - - -
      - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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      Bytes2.18 MiB77.09 kiB
      Shape(286144, 2)(9868, 2)
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      Data typefloat32 numpy.ndarray
      -
      -
      -rad_water_vapor -
      -
      -(num_lines, num_sides) -
      -
      -float32 -
      -
      -dask.array<chunksize=(9868, 2), meta=np.ndarray> -
      - -
      -
      -
      long_name :
      -
      -water vapor content from radiometer -
      -
      standard_name :
      -
      -atmosphere_water_vapor_content -
      -
      source :
      -
      -Advanced Microwave Radiometer -
      -
      units :
      -
      -kg/m^2 -
      -
      valid_min :
      -
      -0 -
      -
      valid_max :
      -
      -15000 -
      -
      comment :
      -
      -Integrated water vapor content from radiometer measurements. -
      -
      -
      - ---- - - - - - - -
      - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      ArrayChunk
      Bytes2.18 MiB77.09 kiB
      Shape(286144, 2)(9868, 2)
      Dask graph29 chunks in 59 graph layers
      Data typefloat32 numpy.ndarray
      +

      4. Water Mask Pixel Cloud Vector Attribute NetCDF

      +
      +
      vector_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))
      +
      +
      Granules found: 1
      -
      -rad_cloud_liquid_water
      -
      -(num_lines, num_sides) +
      +
      ds_PIXCVEC = xr.open_mfdataset(earthaccess.open([vector_results[0]]), decode_cf=False,  engine='h5netcdf')
+ds_PIXCVEC
      +
      +
       Opening 1 granules, approx size: 0.0 GB
      -
      -float32 +
      +
      -
      -dask.array<chunksize=(9868, 2), meta=np.ndarray> +
      +
      - -
      -
      -
      long_name :
      -
      -liquid water content from radiometer -
      -
      standard_name :
      -
      -atmosphere_cloud_liquid_water_content -
      -
      source :
      -
      -Advanced Microwave Radiometer -
      -
      units :
      -
      -kg/m^2 -
      -
      valid_min :
      -
      -0 -
      -
      valid_max :
      -
      -2000 -
      -
      comment :
      -
      -Integrated cloud liquid water content from radiometer measurements. -
      -
      +
      +
      -
      - ---- - - - - - + + - - + + - + - +
      - - - - - - +
      + +
      + + + + + + + + + + + + + + +
      <xarray.Dataset>
+Dimensions:               (points: 489673, nchar_reach_id: 11,
+                           nchar_node_id: 14, nchar_lake_id: 10,
+                           nchar_obs_id: 13)
+Dimensions without coordinates: points, nchar_reach_id, nchar_node_id,
+                                nchar_lake_id, nchar_obs_id
+Data variables:
+    azimuth_index         (points) int32 dask.array<chunksize=(489673,), meta=np.ndarray>
+    range_index           (points) int32 dask.array<chunksize=(489673,), meta=np.ndarray>
+    latitude_vectorproc   (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
+    longitude_vectorproc  (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
+    height_vectorproc     (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
+    reach_id              (points, nchar_reach_id) |S1 dask.array<chunksize=(489673, 11), meta=np.ndarray>
+    node_id               (points, nchar_node_id) |S1 dask.array<chunksize=(489673, 14), meta=np.ndarray>
+    lake_id               (points, nchar_lake_id) |S1 dask.array<chunksize=(489673, 10), meta=np.ndarray>
+    obs_id                (points, nchar_obs_id) |S1 dask.array<chunksize=(489673, 13), meta=np.ndarray>
+    ice_clim_f            (points) int8 dask.array<chunksize=(489673,), meta=np.ndarray>
+    ice_dyn_f             (points) int8 dask.array<chunksize=(489673,), meta=np.ndarray>
+Attributes: (12/36)
+    Conventions:                                 CF-1.7
+    title:                                       Level 2 KaRIn high rate pixe...
+    institution:                                 CNES
+    source:                                      Simulation
+    history:                                     2021-04-14 18:11:49Z: Creation
+    platform:                                    SWOT
+    ...                                          ...
+    xref_input_l2_hr_pixc_vec_river_file:        /work/ALT/swot/swotdev/desro...
+    xref_static_river_db_file:                   
+    xref_static_lake_db_file:                    /work/ALT/swot/swotpub/BD/BD...
+    xref_l2_hr_lake_tile_config_parameter_file:  /work/ALT/swot/swotdev/desro...
+    ellipsoid_semi_major_axis:                   6371008.771416667
+    ellipsoid_flattening:                        0.0
      ArrayChunk
      ++++ + + + - +
      + + + + + + - - + + - - + + - + - +
      ArrayChunk
      Bytes2.18 MiB77.09 kiB1.87 MiB1.87 MiB
      Shape(286144, 2)(9868, 2)(489673,)(489673,)
      Dask graph29 chunks in 59 graph layers1 chunks in 2 graph layers
      Data typefloat32 numpy.ndarrayint32 numpy.ndarray
      -
      -mean_sea_surface_cnescls +range_index
      -(num_lines, num_pixels) +(points)
      -float64 +int32
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
      -
      long_name :
      -
      -mean sea surface height (CNES/CLS) -
      -
      source :
      +
      _FillValue :
      -CNES_CLS_15 +2147483647
      -
      institution :
      +
      long_name :
      -CNES/CLS +rare interferogram range index
      units :
      -m +1
      valid_min :
      --1500000 +0
      valid_max :
      -1500000 +999999 +
      +
      coordinates :
      +
      +longitude_vectorproc latitude_vectorproc
      comment :
      -Mean sea surface height above the reference ellipsoid. The value is referenced to the mean tide system, i.e. includes the permanent tide (zero frequency). +Rare interferogram range index (indexed from 0).
      @@ -8122,71 +8475,71 @@

      Multi-File
      Bytes155.00 MiB5.35 MiB1.87 MiB1.87 MiB
      Shape(286144, 71)(9868, 71)(489673,)(489673,)
      Dask graph29 chunks in 59 graph layers1 chunks in 2 graph layers
      Data typefloat64 numpy.ndarrayint32 numpy.ndarray
      - +
      -mean_sea_surface_cnescls_uncert +latitude_vectorproc
      -(num_lines, num_pixels) +(points)
      -float32 +float64
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
      -
      long_name :
      +
      _FillValue :
      -mean sea surface height accuracy (CNES/CLS) +9.969209968386869e+36
      -
      source :
      +
      long_name :
      -CNES_CLS_15 +height-constrained geolocation latitude
      -
      institution :
      +
      standard_name :
      -CNES/CLS +latitude
      units :
      -m +degrees_north
      valid_min :
      -0 +-80.0
      valid_max :
      -10000 +80.0
      comment :
      -Accuracy of the mean sea surface height (mean_sea_surface_cnescls). +Height-constrained geodetic latitude of the pixel. Units are in degrees north of the equator.
      @@ -8209,71 +8562,71 @@

      Multi-File

      Bytes -77.50 MiB -2.67 MiB +3.74 MiB +3.74 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +float64 numpy.ndarray - +
      -mean_sea_surface_dtu +longitude_vectorproc
      -(num_lines, num_pixels) +(points)
      float64
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
      -
      long_name :
      +
      _FillValue :
      -mean sea surface height (DTU) +9.969209968386869e+36
      -
      source :
      +
      long_name :
      -DTU18 +height-constrained geolocation longitude
      -
      institution :
      +
      standard_name :
      -DTU +longitude
      units :
      -m +degrees_east
      valid_min :
      --1500000 +-180.0
      valid_max :
      -1500000 +180.0
      comment :
      -Mean sea surface height above the reference ellipsoid. The value is referenced to the mean tide system, i.e. includes the permanent tide (zero frequency). +Height-constrained geodetic longitude of the pixel. Positive=degrees east of the Greenwich meridian. Negative=degrees west of the Greenwich meridian.
      @@ -8296,17 +8649,17 @@

      Multi-File

      Bytes -155.00 MiB -5.35 MiB +3.74 MiB +3.74 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -8314,37 +8667,33 @@

      Multi-File

      - +
      -mean_sea_surface_dtu_uncert +height_vectorproc
      -(num_lines, num_pixels) +(points)
      float32
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
      -
      long_name :
      +
      _FillValue :
      -mean sea surface height accuracy (DTU) +9.96921e+36
      -
      source :
      -
      -DTU18 -
      -
      institution :
      +
      long_name :
      -DTU +height above reference ellipsoid
      units :
      @@ -8352,15 +8701,19 @@

      Multi-File

      valid_min :
      -0 +-1500.0
      valid_max :
      -10000 +15000.0 +
      +
      coordinates :
      +
      +longitude_vectorproc latitude_vectorproc
      comment :
      -Accuracy of the mean sea surface height (mean_sea_surface_dtu) +Height-constrained height of the pixel above the reference ellipsoid.
      @@ -8383,17 +8736,17 @@

      Multi-File

      Bytes -77.50 MiB -2.67 MiB +1.87 MiB +1.87 MiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -8401,53 +8754,37 @@

      Multi-File

      - +
      -geoid +reach_id
      -(num_lines, num_pixels) +(points, nchar_reach_id)
      -float64 +|S1
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673, 11), meta=np.ndarray>
      long_name :
      -geoid height -
      -
      standard_name :
      -
      -geoid_height_above_reference_ellipsoid -
      -
      source :
      -
      -EGM2008 (Pavlis et al., 2012) -
      -
      units :
      -
      -m -
      -
      valid_min :
      -
      --1500000 +identifier of the associated prior river reach
      -
      valid_max :
      +
      coordinates :
      -1500000 +longitude_vectorproc latitude_vectorproc
      comment :
      -Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency). +Unique reach identifier from the prior river database. The format of the identifier is CBBBBBRRRRT, where C=continent, B=basin, R=reach, T=type.
      @@ -8470,71 +8807,55 @@

      Multi-File

      Bytes -155.00 MiB -5.35 MiB +5.14 MiB +5.14 MiB Shape -(286144, 71) -(9868, 71) +(489673, 11) +(489673, 11) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +|S1 numpy.ndarray - +
      -mean_dynamic_topography +node_id
      -(num_lines, num_pixels) +(points, nchar_node_id)
      -float32 +|S1
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673, 14), meta=np.ndarray>
      long_name :
      -mean dynamic topography -
      -
      source :
      -
      -CNES_CLS_18 -
      -
      institution :
      -
      -CNES/CLS -
      -
      units :
      -
      -m -
      -
      valid_min :
      -
      --30000 +identifier of the associated prior river node
      -
      valid_max :
      +
      coordinates :
      -30000 +longitude_vectorproc latitude_vectorproc
      comment :
      -Mean dynamic topography above the geoid. +Unique node identifier from the prior river database. The format of the identifier is CBBBBBRRRRNNNT, where C=continent, B=basin, R=reach, N=node, T=type of water body.
      @@ -8557,71 +8878,126 @@

      Multi-File

      Bytes -77.50 MiB -2.67 MiB +6.54 MiB +6.54 MiB Shape -(286144, 71) -(9868, 71) +(489673, 14) +(489673, 14) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +|S1 numpy.ndarray - +
      -mean_dynamic_topography_uncert +lake_id
      -(num_lines, num_pixels) +(points, nchar_lake_id)
      -float32 +|S1
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673, 10), meta=np.ndarray>
      long_name :
      -mean dynamic topography accuracy -
      -
      source :
      -
      -CNES_CLS_18 +identifier of the associated prior lake
      -
      institution :
      +
      coordinates :
      -CNES/CLS +longitude_vectorproc latitude_vectorproc
      -
      units :
      +
      comment :
      -m +Identifier of the lake from the lake prior database) associated to the pixel. The format of the identifier is CBBNNNNNNT, where C=continent, B=basin, N=counter within the basin, T=type of water body.
      -
      valid_min :
      +
      +
      +
      + ++++ + + + + + + +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      ArrayChunk
      Bytes4.67 MiB4.67 MiB
      Shape(489673, 10)(489673, 10)
      Dask graph1 chunks in 2 graph layers
      Data type|S1 numpy.ndarray
      +
      +
      +obs_id +
      +
      +(points, nchar_obs_id) +
      +
      +|S1 +
      +
      +dask.array<chunksize=(489673, 13), meta=np.ndarray> +
      + +
      +
      +
      long_name :
      -0 +identifier of the observed feature
      -
      valid_max :
      +
      coordinates :
      -10000 +longitude_vectorproc latitude_vectorproc
      comment :
      -Accuracy of the mean dynamic topography. +Tile-specific identifier of the observed feature associated to the pixel. The format of the identifier is CBBTTTSNNNNNN, where C=continent, B=basin, T=tile number, S=swath side, N=lake counter within the PIXC tile.
      @@ -8644,71 +9020,71 @@

      Multi-File

      Bytes -77.50 MiB -2.67 MiB +6.07 MiB +6.07 MiB Shape -(286144, 71) -(9868, 71) +(489673, 13) +(489673, 13) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +|S1 numpy.ndarray - +
      -depth_or_elevation +ice_clim_f
      -(num_lines, num_pixels) +(points)
      -float32 +int8
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
      -
      long_name :
      +
      _FillValue :
      -ocean depth or land elevation +127
      -
      source :
      +
      long_name :
      -Altimeter Corrected Elevations, version 2 +climatological ice cover flag
      -
      institution :
      +
      flag_meanings :
      -European Space Agency +no_ice_cover partial_ice_cover full_ice_cover
      -
      units :
      +
      flag_values :
      -m +[0 1 2]
      -
      valid_min :
      +
      institution :
      --12000 +University of North Carolina
      -
      valid_max :
      +
      coordinates :
      -10000 +longitude_vectorproc latitude_vectorproc
      comment :
      -Ocean depth or land elevation above reference ellipsoid. Ocean depth (bathymetry) is given as negative values, and land elevation positive values. +Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.
      @@ -8731,67 +9107,71 @@

      Multi-File

      Bytes -77.50 MiB -2.67 MiB +478.20 kiB +478.20 kiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +int8 numpy.ndarray - +
      -solid_earth_tide +ice_dyn_f
      -(num_lines, num_pixels) +(points)
      -float32 +int8
      -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(489673,), meta=np.ndarray>
      +
      _FillValue :
      +
      +127 +
      long_name :
      -solid Earth tide height +dynamical ice cover flag
      -
      source :
      +
      flag_meanings :
      -Cartwright and Taylor (1971) and Cartwright and Edden (1973) +no_ice_cover partial_ice_cover full_ice_cover
      -
      units :
      +
      flag_values :
      -m +[0 1 2]
      -
      valid_min :
      +
      institution :
      --10000 +University of North Carolina
      -
      valid_max :
      +
      coordinates :
      -10000 +longitude_vectorproc latitude_vectorproc
      comment :
      -Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included. +Dynamic ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.
      @@ -8814,75 +9194,484 @@

      Multi-File

      Bytes -77.50 MiB -2.67 MiB +478.20 kiB +478.20 kiB Shape -(286144, 71) -(9868, 71) +(489673,) +(489673,) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +int8 numpy.ndarray - +
      -
      -ocean_tide_fes +
      • Conventions :
        CF-1.7
        title :
        Level 2 KaRIn high rate pixel cloud vector attribute product
        institution :
        CNES
        source :
        Simulation
        history :
        2021-04-14 18:11:49Z: Creation
        platform :
        SWOT
        references :
        0.2
        reference_document :
        SWOT-TN-CDM-0677-CNES
        contact :
        test@cnes.fr
        cycle_number :
        7
        pass_number :
        522
        tile_number :
        93
        swath_side :
        L
        tile_name :
        522_093L
        continent :
        NA
        time_coverage_start :
        2022-08-22 19:28:50.964042Z
        time_coverage_end :
        2022-08-22 19:29:00.951707Z
        geospatial_lon_min :
        -90.50978392584558
        geospatial_lon_max :
        -89.62391069818588
        geospatial_lat_min :
        34.79938523279299
        geospatial_lat_max :
        35.48806356833591
        inner_first_longitude :
        -90.50978392584558
        inner_first_latitude :
        35.362946480544466
        inner_last_longitude :
        -90.36177519926197
        inner_last_latitude :
        34.79938523279299
        outer_first_longitude :
        -89.76709094457013
        outer_first_latitude :
        35.48806356833591
        outer_last_longitude :
        -89.62391069818588
        outer_last_latitude :
        34.92312785225805
        xref_input_l2_hr_pixc_file :
        /work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/simu/SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc
        xref_input_l2_hr_pixc_vec_river_file :
        /work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/river/SWOT_L2_HR_PIXCVecRiver_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc
        xref_static_river_db_file :
        xref_static_lake_db_file :
        /work/ALT/swot/swotpub/BD/BD_lakes/PLD
        xref_l2_hr_lake_tile_config_parameter_file :
        /work/ALT/swot/swotdev/desrochesd/swot-sds-16-10/swotCNES/PGE/lake_tile/lake_tile_param.cfg
        ellipsoid_semi_major_axis :
        6371008.771416667
        ellipsoid_flattening :
        0.0
        • -
        -(num_lines, num_pixels)
        -
        -float64 +
        +
        pixcvec_htvals = ds_PIXCVEC.height_vectorproc.compute()
+pixcvec_latvals = ds_PIXCVEC.latitude_vectorproc.compute()
+pixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc.compute()
+
+#Before plotting, we set all fill values to nan so that the graph shows up better spatially
+pixcvec_htvals[pixcvec_htvals > 15000] = np.nan
+pixcvec_latvals[pixcvec_latvals > 80] = np.nan
+pixcvec_lonvals[pixcvec_lonvals > 180] = np.nan
        -
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +
        +
        plt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)
+plt.colorbar().set_label('Height (m)')
        +
        +

        +
        +
        +

        5. Raster NetCDF

        +
        +
        raster_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1', temporal = ('2022-08-22 19:28:50', '2022-08-22 19:29:11'), point = ('-90', '35'))
        +
        +
        Granules found: 2
        +
        +
        +
        +
        #this collection has 100m and 250m granules, but we only want 100m
+raster_data = []
+for g in raster_results:
+    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
+        if "100m" in l:
+            raster_data.append(l)
+print(raster_data)
        +
        +
        ['s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1/SWOT_L2_HR_Raster_100m_UTM15S_N_x_x_x_007_522_047F_20220822T192850_20220822T192911_Dx0000_01.nc']
        +
        +
        +
        +
        ds_raster = xr.open_mfdataset(earthaccess.open([raster_data[0]], provider = 'POCLOUD'), engine='h5netcdf')
+ds_raster
        +
        + +
        +
        +
        - -
        -
        -
        long_name :
        -
        -geocentric ocean tide height (FES) -
        -
        source :
        -
        -FES2014b (Carrere et al., 2016) -
        -
        institution :
        -
        -LEGOS/CNES -
        -
        units :
        -
        -m -
        -
        valid_min :
        -
        --300000 -
        -
        valid_max :
        -
        -300000 -
        -
        comment :
        -
        -Geocentric ocean tide height. Includes the sum total of the ocean tide, the corresponding load tide (load_tide_fes) and equilibrium long-period ocean tide height (ocean_tide_eq). -
        -
        +
        +
        -
        +
        + +
        + + + + + + + + + + + + + + +
        <xarray.Dataset>
+Dimensions:                (x: 1543, y: 1540)
+Coordinates:
+  * x                      (x) float64 6.567e+05 6.568e+05 ... 8.109e+05
+  * y                      (y) float64 3.775e+06 3.775e+06 ... 3.929e+06
+Data variables: (12/30)
+    crs                    object ...
+    longitude              (y, x) float64 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    latitude               (y, x) float64 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    wse                    (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    wse_uncert             (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    water_area             (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    ...                     ...
+    load_tide_fes          (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    load_tide_got          (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    pole_tide              (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    model_dry_tropo_cor    (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    model_wet_tropo_cor    (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+    iono_cor_gim_ka        (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
+Attributes: (12/45)
+    Conventions:                     CF-1.7
+    title:                           Level 2 KaRIn High Rate Raster Data Product
+    institution:                     JPL
+    source:                          Large scale simulator
+    history:                         2021-09-08T22:28:33Z : Creation
+    mission_name:                    SWOT
+    ...                              ...
+    utm_zone_num:                    15
+    mgrs_latitude_band:              S
+    x_min:                           656700.0
+    x_max:                           810900.0
+    y_min:                           3775000.0
+    y_max:                           3928900.0
        -load_tide_got +wse_uncert
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -geocentric load tide height (GOT) -
        -
        source :
        -
        -GOT4.10c (Ray, 2013) +uncertainty in the water surface elevation
        -
        institution :
        +
        grid_mapping :
        -GSFC +crs
        units :
        @@ -9131,15 +9911,15 @@

        Multi-File

        valid_min :
        --2000 +0.0
        valid_max :
        -2000 +999999.0
        comment :
        -Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value has already been added to the corresponding ocean tide height value (ocean_tide_got). +1-sigma uncertainty in the water surface elevation.
        @@ -9162,17 +9942,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9180,45 +9960,49 @@

        Multi-File

        - +
        -ocean_tide_eq +water_area
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -equilibrium long-period ocean tide height +surface area of water +
        +
        grid_mapping :
        +
        +crs
        units :
        -m +m^2
        valid_min :
        --2000 +-2000000.0
        valid_max :
        -2000 +2000000000.0
        comment :
        -Equilibrium long-period ocean tide height. This value has already been added to the corresponding ocean tide height values (ocean_tide_fes and ocean_tide_got). +Surface area of the water pixels.
        @@ -9241,17 +10025,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9259,53 +10043,49 @@

        Multi-File

        - +
        -ocean_tide_non_eq +water_area_uncert
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -non-equilibrium long-period ocean tide height -
        -
        source :
        -
        -FES2014b (Carrere et al., 2016) +uncertainty in the water surface area
        -
        institution :
        +
        grid_mapping :
        -LEGOS/CNES +crs
        units :
        -m +m^2
        valid_min :
        --2000 +0.0
        valid_max :
        -2000 +2000000000.0
        comment :
        -Non-equilibrium long-period ocean tide height. This value is reported as a relative displacement with repsect to ocean_tide_eq. This value can be added to ocean_tide_eq, ocean_tide_fes, or ocean_tide_got, or subtracted from ssha_karin and ssha_karin_2, to account for the total long-period ocean tides from equilibrium and non-equilibrium contributions. +1-sigma uncertainty in the water surface area
        @@ -9328,17 +10108,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9346,49 +10126,49 @@

        Multi-File

        - +
        -internal_tide_hret +water_frac
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -coherent internal tide (HRET) +water fraction
        -
        source :
        +
        grid_mapping :
        -Zaron (2019) +crs
        units :
        -m +1
        valid_min :
        --2000 +-1000.0
        valid_max :
        -2000 +10000.0
        comment :
        -Coherent internal ocean tide. This value is subtracted from the ssh_karin and ssh_karin_2 to compute ssha_karin and ssha_karin_2, respectively. +Fraction of the pixel that is water.
        @@ -9411,17 +10191,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9429,49 +10209,49 @@

        Multi-File

        - +
        -internal_tide_sol2 +water_frac_uncert
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -coherent internal tide (Model 2) +uncertainty in the water fraction
        -
        source :
        +
        grid_mapping :
        -TBD +crs
        units :
        -m +1
        valid_min :
        --2000 +0.0
        valid_max :
        -2000 +999999.0
        comment :
        -Coherent internal tide. +1-sigma uncertainty in the water fraction.
        @@ -9494,17 +10274,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9512,49 +10292,49 @@

        Multi-File

        - +
        -pole_tide +sig0
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -geocentric pole tide height +sigma0
        -
        source :
        +
        grid_mapping :
        -Wahr (1985) and Desai et al. (2015) +crs
        units :
        -m +1
        valid_min :
        --2000 +-1000.0
        valid_max :
        -2000 +10000000.0
        comment :
        -Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height, the ocean pole tide height, and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust). +Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.
        @@ -9577,17 +10357,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9595,53 +10375,49 @@

        Multi-File

        - +
        -dac +sig0_uncert
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -dynamic atmospheric correction -
        -
        source :
        -
        -MOG2D +uncertainty in sigma0
        -
        institution :
        +
        grid_mapping :
        -LEGOS/CNES/CLS +crs
        units :
        -m +1
        valid_min :
        --12000 +0.0
        valid_max :
        -12000 +1000.0
        comment :
        -Model estimate of the effect on sea surface topography due to high frequency air pressure and wind effects and the low-frequency height from inverted barometer effect (inv_bar_cor). This value is subtracted from the ssh_karin and ssh_karin_2 to compute ssha_karin and ssha_karin_2, respectively. Use only one of inv_bar_cor and dac. +1-sigma uncertainty in sigma0. The value is provided in linear units. This value is a one-sigma additive (not multiplicative) uncertainty term, which can be added to or subtracted from sigma0.
        @@ -9664,17 +10440,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9682,45 +10458,49 @@

        Multi-File

        - +
        -inv_bar_cor +inc
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -static inverse barometer effect on sea surface height +incidence angle +
        +
        grid_mapping :
        +
        +crs
        units :
        -m +degrees
        valid_min :
        --2000 +0.0
        valid_max :
        -2000 +90.0
        comment :
        -Estimate of static effect of atmospheric pressure on sea surface height. Above average pressure lowers sea surface height. Computed by interpolating ECMWF pressure fields in space and time. The value is included in dac. To apply, add dac to ssha_karin and ssha_karin_2 and subtract inv_bar_cor. +Incidence angle.
        @@ -9743,17 +10523,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9761,37 +10541,33 @@

        Multi-File

        - +
        -model_dry_tropo_cor +cross_track
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -dry troposphere vertical correction -
        -
        source :
        -
        -European Centre for Medium-Range Weather Forecasts +approximate cross-track location
        -
        institution :
        +
        grid_mapping :
        -ECMWF +crs
        units :
        @@ -9799,15 +10575,15 @@

        Multi-File

        valid_min :
        --30000 +-75000.0
        valid_max :
        --15000 +75000.0
        comment :
        -Equivalent vertical correction due to dry troposphere delay. The reported sea surface height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height. +Approximate cross-track location of the pixel.
        @@ -9830,17 +10606,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -9848,53 +10624,45 @@

        Multi-File

        - +
        -model_wet_tropo_cor +illumination_time
        -(num_lines, num_pixels) +(y, x)
        -float32 +datetime64[ns]
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -wet troposphere vertical correction from weather model data -
        -
        source :
        -
        -European Centre for Medium-Range Weather Forecasts -
        -
        institution :
        -
        -ECMWF +time of illumination of each pixel (UTC)
        -
        units :
        +
        standard_name :
        -m +time
        -
        valid_min :
        +
        tai_utc_difference :
        --10000 +-32.0
        -
        valid_max :
        +
        leap_second :
        -0 +YYYY-MM-DDThh:mm:ssZ
        comment :
        -Equivalent vertical correction due to wet troposphere delay from weather model data. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height (ssh_karin_2) results in the uncorrected sea surface height. +Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.
        @@ -9917,67 +10685,55 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +18.13 MiB +18.13 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +datetime64[ns] numpy.ndarray - +
        -rad_wet_tropo_cor +illumination_time_tai
        -(num_lines, num_pixels) +(y, x)
        -float32 +datetime64[ns]
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -wet troposphere vertical correction from radiometer data -
        -
        source :
        -
        -Advanced Microwave Radiometer -
        -
        units :
        -
        -m -
        -
        valid_min :
        -
        --10000 +time of illumination of each pixel (TAI)
        -
        valid_max :
        +
        standard_name :
        -0 +time
        comment :
        -Equivalent vertical correction due to wet troposphere delay from radiometer measurements. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height (ssh_karin) results in the uncorrected sea surface height. +Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].
        @@ -10000,71 +10756,71 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +18.13 MiB +18.13 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +datetime64[ns] numpy.ndarray - +
        -iono_cor_gim_ka +raster_qual
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        -
        long_name :
        +
        standard_name :
        -ionosphere vertical correction +status_flag
        -
        source :
        +
        grid_mapping :
        -Global Ionosphere Maps +crs
        -
        institution :
        +
        flag_meanings :
        -JPL +good bad
        -
        units :
        +
        flag_values :
        -m +[0 1]
        valid_min :
        --5000 +0
        valid_max :
        -0 +1
        comment :
        -Equivalent vertical correction due to ionosphere delay. The reported sea surface height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height. +Quality flag for raster data.
        @@ -10087,17 +10843,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -10105,45 +10861,49 @@

        Multi-File

        - +
        -height_cor_xover +n_wse_pix
        -(num_lines, num_pixels) +(y, x)
        float64
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -height correction from KaRIn crossovers +number of wse pixels +
        +
        grid_mapping :
        +
        +crs
        units :
        -m +l
        valid_min :
        --100000 +0
        valid_max :
        -100000 +999999
        comment :
        -Height correction from KaRIn crossover calibration. To apply this correction the value of height_cor_xover should be added to the value of ssh_karin, ssh_karin_2, ssha_karin, and ssha_karin_2. +Number of pixel cloud samples used in water surface elevation aggregation.
        @@ -10166,17 +10926,17 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +18.13 MiB +18.13 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -10184,41 +10944,37 @@

        Multi-File

        - +
        -correction_flag +n_area_pix
        -(num_lines, num_pixels) +(y, x)
        -float32 +float64
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -quality flag for corrections -
        -
        standard_name :
        -
        -status_flag +number of area pixels
        -
        flag_meanings :
        +
        grid_mapping :
        -good bad +crs
        -
        flag_values :
        +
        units :
        -[0 1] +l
        valid_min :
        @@ -10226,11 +10982,11 @@

        Multi-File

        valid_max :
        -1 +999999
        comment :
        -Quality flag for corrections. +Number of pixel cloud samples used in water area and water fraction aggregation.
        @@ -10253,71 +11009,67 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +18.13 MiB +18.13 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float32 numpy.ndarray +float64 numpy.ndarray - +
        -rain_rate +dark_frac
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -rain rate from weather model -
        -
        source :
        -
        -European Centre for Medium-Range Weather Forecasts +fractional area of dark water
        -
        institution :
        +
        grid_mapping :
        -ECMWF +crs
        units :
        -mm/hr +l
        valid_min :
        -0 +-1000.0
        valid_max :
        -200 +10000.0
        comment :
        -Rain rate from weather model. +Fraction of pixel water area covered by dark water.
        @@ -10340,17 +11092,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -10358,45 +11110,45 @@

        Multi-File

        - +
        -ice_conc +ice_clim_flag
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -concentration of sea ice +climatological ice cover flag
        -
        standard_name :
        +
        source :
        -sea_ice_area_fraction +UNC
        -
        source :
        +
        grid_mapping :
        -EUMETSAT Ocean and Sea Ice Satellite Applications Facility +crs
        -
        institution :
        +
        flag_meanings :
        -EUMETSAT +no_ice_cover uncertain_ice_cover full_ice_cover
        -
        units :
        +
        flag_values :
        -% +[0 1 2]
        valid_min :
        @@ -10404,11 +11156,11 @@

        Multi-File

        valid_max :
        -10000 +2
        comment :
        -Concentration of sea ice from model. +Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the pixel is likely not ice covered, may or may not be partially or fully ice covered, and likely fully ice covered, respectively.
        @@ -10431,17 +11183,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -10449,49 +11201,57 @@

        Multi-File

        - +
        -sea_state_bias_cor +ice_dyn_flag
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -sea state bias correction to height +dynamic ice cover flag
        source :
        -TBD +UNC
        -
        units :
        +
        grid_mapping :
        -m +crs +
        +
        flag_meanings :
        +
        +no_ice_cover partial_ice_cover full_ice_cover +
        +
        flag_values :
        +
        +[0 1 2]
        valid_min :
        --6000 +0
        valid_max :
        -0 +2
        comment :
        -Sea state bias correction to ssh_karin. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height. The wind_speed_karin value is used to compute this quantity. +Dynamic ice cover flag indicating whether the surface is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the pixel is not ice covered, partially ice covered, and fully ice covered, respectively.
        @@ -10514,17 +11274,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -10532,33 +11292,33 @@

        Multi-File

        - +
        -sea_state_bias_cor_2 +layover_impact
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -sea state bias correction to height +layover impact
        -
        source :
        +
        grid_mapping :
        -TBD +crs
        units :
        @@ -10566,15 +11326,15 @@

        Multi-File

        valid_min :
        --6000 +-999999.0
        valid_max :
        -0 +999999.0
        comment :
        -Sea state bias correction to ssh_karin_2. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height. The wind_speed_karin_2 value is used to compute this quantity. +Estimate of the water surface elevation error caused by layover.
        @@ -10597,17 +11357,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -10615,29 +11375,41 @@

        Multi-File

        - +
        -swh_sea_state_bias +geoid
        -(num_lines, num_pixels) +(y, x)
        float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -SWH used in sea state bias correction +geoid height +
        +
        standard_name :
        +
        +geoid_height_above_reference_ellipsoid +
        +
        source :
        +
        +EGM2008 (Pavlis et al., 2012) +
        +
        grid_mapping :
        +
        +crs
        units :
        @@ -10645,15 +11417,15 @@

        Multi-File

        valid_min :
        -0 +-150.0
        valid_max :
        -25000 +150.0
        comment :
        -Significant wave height used in sea state bias correction. +Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).
        @@ -10676,17 +11448,17 @@

        Multi-File

        Bytes -77.50 MiB -2.67 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type @@ -10694,33 +11466,37 @@

        Multi-File

        - +
        -simulated_true_ssh_karin +solid_earth_tide
        -(num_lines, num_pixels) +(y, x)
        -float64 +float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -sea surface height +solid Earth tide height
        -
        standard_name :
        +
        source :
        +
        +Cartwright and Taylor (1971) and Cartwright and Edden (1973) +
        +
        grid_mapping :
        -sea surface height above reference ellipsoid +crs
        units :
        @@ -10728,15 +11504,15 @@

        Multi-File

        valid_min :
        --15000000 +-1.0
        valid_max :
        -150000000 +1.0
        comment :
        -Height of the sea surface free of measurement errors. +Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.
        @@ -10759,52 +11535,76 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
        -simulated_error_karin +load_tide_fes
        -(num_lines, num_pixels) +(y, x)
        -float64 +float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -KaRIn error +geocentric load tide height (FES) +
        +
        source :
        +
        +FES2014b (Carrere et al., 2016) +
        +
        institution :
        +
        +LEGOS/CNES +
        +
        grid_mapping :
        +
        +crs
        units :
        m
        +
        valid_min :
        +
        +-0.2 +
        +
        valid_max :
        +
        +0.2 +
        +
        comment :
        +
        +Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust. +
        @@ -10826,52 +11626,76 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
        -simulated_error_timing +load_tide_got
        -(num_lines, num_pixels) +(y, x)
        -float64 +float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -Timing error +geocentric load tide height (GOT) +
        +
        source :
        +
        +GOT4.10c (Ray, 2013) +
        +
        institution :
        +
        +GSFC +
        +
        grid_mapping :
        +
        +crs
        units :
        m
        +
        valid_min :
        +
        +-0.2 +
        +
        valid_max :
        +
        +0.2 +
        +
        comment :
        +
        +Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust. This value is reported for reference but is not applied to the reported height. +
        @@ -10893,52 +11717,72 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
        -simulated_error_baseline_dilation +pole_tide
        -(num_lines, num_pixels) +(y, x)
        -float64 +float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -Error due to baseline mast dilation +geocentric pole tide height +
        +
        source :
        +
        +Wahr (1985) and Desai et al. (2015) +
        +
        grid_mapping :
        +
        +crs
        units :
        m
        +
        valid_min :
        +
        +-0.2 +
        +
        valid_max :
        +
        +0.2 +
        +
        comment :
        +
        +Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth’s crust). +
        @@ -10960,52 +11804,76 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
        -simulated_error_roll +model_dry_tropo_cor
        -(num_lines, num_pixels) +(y, x)
        -float64 +float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -Error due to roll +dry troposphere vertical correction +
        +
        source :
        +
        +European Centre for Medium-Range Weather Forecasts +
        +
        institution :
        +
        +ECMWF +
        +
        grid_mapping :
        +
        +crs
        units :
        m
        +
        valid_min :
        +
        +-3.0 +
        +
        valid_max :
        +
        +-1.5 +
        +
        comment :
        +
        +Equivalent vertical correction due to dry troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height. +
        @@ -11027,52 +11895,76 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
        -simulated_error_phase +model_wet_tropo_cor
        -(num_lines, num_pixels) +(y, x)
        -float64 +float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -Error due to phase +wet troposphere vertical correction +
        +
        source :
        +
        +European Centre for Medium-Range Weather Forecasts +
        +
        institution :
        +
        +ECMWF +
        +
        grid_mapping :
        +
        +crs
        units :
        m
        +
        valid_min :
        +
        +-1.0 +
        +
        valid_max :
        +
        +0.0 +
        +
        comment :
        +
        +Equivalent vertical correction due to wet troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height. +
        @@ -11094,52 +11986,76 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
        -simulated_error_orbital +iono_cor_gim_ka
        -(num_lines, num_pixels) +(y, x)
        -float64 +float32
        -dask.array<chunksize=(9868, 71), meta=np.ndarray> +dask.array<chunksize=(1540, 1543), meta=np.ndarray>
        long_name :
        -Error due to orbital perturbations +ionosphere vertical correction +
        +
        source :
        +
        +Global Ionosphere Maps +
        +
        institution :
        +
        +JPL +
        +
        grid_mapping :
        +
        +crs
        units :
        m
        +
        valid_min :
        +
        +-0.5 +
        +
        valid_max :
        +
        +0.0 +
        +
        comment :
        +
        +Equivalent vertical correction due to ionosphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height. +
        @@ -11161,47 +12077,85 @@

        Multi-File

        Bytes -155.00 MiB -5.35 MiB +9.06 MiB +9.06 MiB Shape -(286144, 71) -(9868, 71) +(1540, 1543) +(1540, 1543) Dask graph -29 chunks in 59 graph layers +1 chunks in 2 graph layers Data type -float64 numpy.ndarray +float32 numpy.ndarray - +
        - -
        • Conventions :
          CF-1.7
          title :
          Level 2 Low Rate Sea Surface Height Data Product - Expert SSH with Wind and Wave
          institution :
          CNES/JPL
          source :
          Simulate product
          history :
          2021-09-10 10:37:14Z : Creation
          platform :
          SWOT
          product_version :
          1.1.0.dev33
          references :
          Gaultier, L., C. Ubelmann, and L.-L. Fu, 2016: The Challenge of Using Future SWOT Data for Oceanic Field Reconstruction. J. Atmos. Oceanic Technol., 33, 119-126, doi:10.1175/jtech-d-15-0160.1. http://dx.doi.org/10.1175/JTECH-D-15-0160.1.
          reference_document :
          D-56407_SWOT_Product_Description_L2_LR_SSH
          contact :
          CNES aviso@altimetry.fr, JPL podaac@podaac.jpl.nasa.gov
          cycle_number :
          387
          pass_number :
          1
          equator_time :
          2015-05-01T00:04:28.200105Z
          equator_longitude :
          73.87996988322527
          time_coverage_start :
          2015-04-30T23:38:53.183986Z
          time_coverage_end :
          2015-05-01T00:29:58.780239Z
          geospatial_lon_min :
          0.0004904718944836875
          geospatial_lon_max :
          359.9988969843575
          geospatial_lat_min :
          -78.29252416681717
          geospatial_lat_max :
          78.2921281674593
          left_first_longitude :
          350.2735935134039
          left_first_latitude :
          -77.03347507655957
          left_last_longitude :
          157.4129761538219
          left_last_latitude :
          78.2921281674593
          right_first_longitude :
          350.28137873490164
          right_first_latitude :
          -78.29252416681717
          right_last_longitude :
          157.4108182844071
          right_last_latitude :
          77.03307806541929
          wavelength :
          0.008385803020979
          orbit_solution :
          POE
          ellipsoid_semi_major_axis :
          6378137.0
          ellipsoid_flattening :
          0.003352810664781205
          • +
          • x
            PandasIndex
            PandasIndex(Index([656700.0, 656800.0, 656900.0, 657000.0, 657100.0, 657200.0, 657300.0,
+       657400.0, 657500.0, 657600.0,
+       ...
+       810000.0, 810100.0, 810200.0, 810300.0, 810400.0, 810500.0, 810600.0,
+       810700.0, 810800.0, 810900.0],
+      dtype='float64', name='x', length=1543))
          • y
            PandasIndex
            PandasIndex(Index([3775000.0, 3775100.0, 3775200.0, 3775300.0, 3775400.0, 3775500.0,
+       3775600.0, 3775700.0, 3775800.0, 3775900.0,
+       ...
+       3928000.0, 3928100.0, 3928200.0, 3928300.0, 3928400.0, 3928500.0,
+       3928600.0, 3928700.0, 3928800.0, 3928900.0],
+      dtype='float64', name='y', length=1540))
        • Conventions :
          CF-1.7
          title :
          Level 2 KaRIn High Rate Raster Data Product
          institution :
          JPL
          source :
          Large scale simulator
          history :
          2021-09-08T22:28:33Z : Creation
          mission_name :
          SWOT
          references :
          https://github.com/SWOTAlgorithms/Raster-Processor
          reference_document :
          JPL D-56416 - Revision A (DRAFT) - November 5, 2020
          contact :
          alexander.t.corben[at]jpl.nasa.gov
          cycle_number :
          7
          pass_number :
          522
          scene_number :
          47
          tile_numbers :
          [92 93 94 95 92 93 94 95]
          tile_names :
          522_092L, 522_093L, 522_094L, 522_095L, 522_092R, 522_093R, 522_094R, 522_095R
          tile_polarizations :
          V, V, V, V, V, V, V, V
          coordinate_reference_system :
          Universal Transverse Mercator
          resolution :
          100.0
          short_name :
          L2_HR_Raster
          descriptor_string :
          100m_UTM15S_N_x_x_x
          crid :
          Dx0000
          product_version :
          V0.1
          pge_name :
          adt_pge_standin
          pge_version :
          V0.1
          time_coverage_start :
          2022-08-22 19:28:50.964042Z
          time_coverage_end :
          2022-08-22 19:29:10.946208Z
          geospatial_lon_min :
          -91.27757002156555
          geospatial_lon_max :
          -89.62061588835118
          geospatial_lat_min :
          34.09943218249787
          geospatial_lat_max :
          35.464214684504334
          left_first_longitude :
          -89.89843338760357
          left_first_latitude :
          35.464214684504334
          left_last_longitude :
          -89.62061588835118
          left_last_latitude :
          34.33243031374548
          right_first_longitude :
          -91.27757002156555
          right_first_latitude :
          35.22613283570163
          right_last_longitude :
          -90.98228790375923
          right_last_latitude :
          34.09943218249787
          xref_input_l2_hr_pixc_files :
          SWOT_L2_HR_PIXC_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.nc
          xref_input_l2_hr_pixcvec_files :
          SWOT_L2_HR_PIXCVec_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.nc
          utm_zone_num :
          15
          mgrs_latitude_band :
          S
          x_min :
          656700.0
          x_max :
          810900.0
          y_min :
          3775000.0
          y_max :
          3928900.0
          • -
          -
          #mapping the dynamic ice flag variable
-plt.figure(figsize=(21, 12))
-ax = plt.axes(projection=ccrs.PlateCarree())
-ax.set_global()
-ds.dynamic_ice_flag.plot.pcolormesh(
- ax=ax, transform=ccrs.PlateCarree(), x="longitude", y="latitude", add_colorbar=True
-)
-ax.coastlines()
          -
          -
          <cartopy.mpl.feature_artist.FeatureArtist at 0x7f21ef868c40>
          -
          +

          It’s easy to analyze and plot the data with packages such as hvplot!

          +
          +
          ds_raster.wse.hvplot.image(y='y', x='x')
          -

          + +
          +
          +
          +
          +
          +
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this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\nLearning Objectives:\n\nAccess all 6 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine\nVisualize accessed data\n\n\n\nSWOT Simulated Level 2 North America Continent KaRIn High Rate Version 1 Datasets:\n\nRiver Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RSP1\n\n\nLake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2LSP1\n\n\nWater Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PIX1\n\n\nWater Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PXV1\n\n\nRaster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RAS1\n\n\nKaRIn Sea Surface Height from GLORYSs - SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2GLC1\n\nNotebook Authors: Cassie Nickles and Zoë Walschots, NASA PO.DAAC (July 2023)\n\n\nLibraries Needed\n\nimport glob\nimport os\nimport requests\nimport s3fs\nimport netCDF4 as nc\nfrom netCDF4 import Dataset\nimport h5netcdf\nimport xarray as xr\nimport pandas as pd\nimport geopandas as gpd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport hvplot.xarray\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeat\nimport shapefile as shp\nimport zipfile\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\n\n\n\n\n\n\n\n\n\n\n\n\nEarthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)\n\nEnter your Earthdata Login username: zoewalschots\nEnter your Earthdata password: ········\n\n\nYou're now authenticated with NASA Earthdata Login\nUsing token with expiration date: 08/12/2023\nUsing user provided credentials for EDL\nPersisting credentials to .netrc\n\n\n\nSet up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data access information.\n\nfs_s3 = earthaccess.get_s3fs_session(daac='PODAAC', provider='POCLOUD')" + "text": "Accessing and Visualizing SWOT Simulated Datasets\n\nRequirement:\nThis tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via earthaccess python library; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\nLearning Objectives:\n\nAccess all 5 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine\nVisualize accessed data\n\n\n\nSWOT Simulated Level 2 North America Continent KaRIn High Rate Version 1 Datasets:\n\nRiver Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RSP1\n\n\nLake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2LSP1\n\n\nWater Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PIX1\n\n\nWater Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PXV1\n\n\nRaster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RAS1\n\nNotebook Author: Cassie Nickles, NASA PO.DAAC (Aug 2022)\n\n\nLibraries Needed\n\nimport glob\nimport os\nimport requests\nimport s3fs\nimport netCDF4 as nc\nimport h5netcdf\nimport xarray as xr\nimport pandas as pd\nimport geopandas as gpd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport hvplot.xarray\nimport shapefile as shp\nimport zipfile\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\n\n\n\n\n\n\n\n\n\n\n\n\nEarthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)\n\n\nSet up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data access information.\n\nfs_s3 = earthaccess.get_s3fs_session(daac='PODAAC', provider='POCLOUD')" }, { "objectID": "notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html#single-file-access", "href": "notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html#single-file-access", "title": "SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud", "section": "Single File Access", - "text": "Single File Access\nThe s3 access link can be found using earthaccess data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the ‘Reach’ title in the data link.\nAlternatively, Earthdata Search (see tutorial) can be used to manually search for a single file.\n1. River Vector Shapefiles\n\n#retrieves granule from the day we want\nriver_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1', temporal = ('2022-08-22 19:24:41', '2022-08-22 19:30:37'))\n\n\n#finds the s3 link of the one granule we want (The collection contains both Reaches and Nodes, but here we want only the Reach)\nriver_data = []\nfor g in river_results:\n for l in earthaccess.results.DataGranule.data_links(g, access='direct'):\n if \"Reach\" in l:\n river_data.append(l)\nprint(river_data)\n\n\n#creates s3 file object for the one link we want\ns3_file_obj1 = fs_s3.open(river_data[0], mode='rb')\n\nThe native format for this sample data is a .zip file, and we want the .shp file within the .zip file, so we need to download the contents of the zip file into the cloud environment. I created a folder called SWOT_HR_shp to write to. Change the path to where you would like your extracted files to be written.\n\nwith zipfile.ZipFile(s3_file_obj1, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\nNext, we’ll look at the attribute table of the .shp file we just extracted to the ‘SWOT_HR_shp’ folder.\n\nSWOT_HR_shp1 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shp') \nSWOT_HR_shp1\n\n\nfig, ax = plt.subplots(figsize=(11,7))\nSWOT_HR_shp1.plot(ax=ax, color='black')\n\n2. Lake Vector Shapefiles\nThe lake vector shapefiles can be accessed in the same way as the river shapefiles above.\n\nlake_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1', temporal = ('2022-08-22 19:24:18', '2022-08-22 19:30:50'))\n\n\n#find the s3 link of the desired granule (This collection has three options: Obs, Unassigned, and Prior - we want Obs)\nlake_data = []\nfor g in lake_results:\n for l in earthaccess.results.DataGranule.data_links(g, access='direct'):\n if \"Obs\" in l:\n lake_data.append(l)\nprint(lake_data)\n\n\ns3_file_obj2 = fs_s3.open(lake_data[0], mode='rb')\n\n\nwith zipfile.ZipFile(s3_file_obj2, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\n\nSWOT_HR_shp2 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.shp') \nSWOT_HR_shp2\n\n\nfig, ax = plt.subplots(figsize=(7,12))\nSWOT_HR_shp2.plot(ax=ax, color='black')\n\n3. Water Mask Pixel Cloud NetCDF\nAccessing the remaining files is different than the shp files above. We do not need to unzip the files because they are stored in native netCDF files in the cloud. For the rest of the products, we will open via xarray.\n\nwatermask_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))\n\nThe pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.\n\nds_PIXC = xr.open_mfdataset(earthaccess.open([watermask_results[0]]), group = 'pixel_cloud', engine='h5netcdf')\nds_PIXC\n\n\nplt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)\nplt.colorbar().set_label('Height (m)')\n\n4. Water Mask Pixel Cloud Vector Attribute NetCDF\n\nvector_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))\n\n\nds_PIXCVEC = xr.open_mfdataset(earthaccess.open([vector_results[0]]), decode_cf=False, engine='h5netcdf')\nds_PIXCVEC\n\n\npixcvec_htvals = ds_PIXCVEC.height_vectorproc.compute()\npixcvec_latvals = ds_PIXCVEC.latitude_vectorproc.compute()\npixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc.compute()\n\n#Before plotting, we set all fill values to nan so that the graph shows up better spatially\npixcvec_htvals[pixcvec_htvals > 15000] = np.nan\npixcvec_latvals[pixcvec_latvals > 80] = np.nan\npixcvec_lonvals[pixcvec_lonvals > 180] = np.nan\n\n\nplt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)\nplt.colorbar().set_label('Height (m)')\n\n5. Raster NetCDF\n\nraster_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1', temporal = ('2022-08-22 19:28:50', '2022-08-22 19:29:11'), point = ('-90', '35'))\n\n\n#this collection has 100m and 250m granules, but we only want 100m\nraster_data = []\nfor g in raster_results:\n for l in earthaccess.results.DataGranule.data_links(g, access='direct'):\n if \"100m\" in l:\n raster_data.append(l)\nprint(raster_data)\n\n\nds_raster = xr.open_mfdataset(earthaccess.open([raster_data[0]], provider = 'POCLOUD'), engine='h5netcdf')\nds_raster\n\nIt’s easy to analyze and plot the data with packages such as hvplot!\n\nds_raster.wse.hvplot.image(y='y', x='x')" + "text": "Single File Access\nThe s3 access link can be found using earthaccess data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the ‘Reach’ title in the data link.\nAlternatively, Earthdata Search (see tutorial) can be used to manually search for a single file.\n1. River Vector Shapefiles\n\n#retrieves granule from the day we want\nriver_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1', temporal = ('2022-08-22 19:24:41', '2022-08-22 19:30:37'))\n\nGranules found: 2\n\n\n\n#finds the s3 link of the one granule we want (The collection contains both Reaches and Nodes, but here we want only the Reach)\nriver_data = []\nfor g in river_results:\n for l in earthaccess.results.DataGranule.data_links(g, access='direct'):\n if \"Reach\" in l:\n river_data.append(l)\nprint(river_data)\n\n['s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip']\n\n\n\n#creates s3 file object for the one link we want\ns3_file_obj1 = fs_s3.open(river_data[0], mode='rb')\n\nThe native format for this sample data is a .zip file, and we want the .shp file within the .zip file, so we need to download the contents of the zip file into the cloud environment. I created a folder called SWOT_HR_shp to write to. Change the path to where you would like your extracted files to be written.\n\nwith zipfile.ZipFile(s3_file_obj1, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\nNext, we’ll look at the attribute table of the .shp file we just extracted to the ‘SWOT_HR_shp’ folder.\n\nSWOT_HR_shp1 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shp') \nSWOT_HR_shp1\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n0\n71224300241\n7.145115e+08\n7.145114e+08\n2022-08-22T19:2441Z\n49.364818\n-94.879318\nno_data\n3.472248e+01\n-1.000000e+12\n1.511000e-02\n...\n7294.5\n3.265803e+06\n15\n390935.258\n3008.959150\n-1.000000e+12\n0\n15\n8\nLINESTRING (-94.86483 49.37485, -94.86515 49.3...\n\n\n1\n71224300253\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2446Z\n49.049486\n-94.899554\nno_data\n3.439994e+01\n-1.000000e+12\n7.600000e-03\n...\n394.5\n7.876447e+06\n42\n444613.943\n8411.845753\n-1.000000e+12\n0\n8\n1\nLINESTRING (-94.92557 49.08401, -94.92556 49.0...\n\n\n2\n71224300263\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2448Z\n48.977915\n-94.869598\nno_data\n3.434701e+01\n-1.000000e+12\n9.620000e-03\n...\n6365.5\n2.935181e+06\n42\n453020.631\n8406.687501\n-1.000000e+12\n0\n3\n1\nLINESTRING (-94.88015 49.01512, -94.88006 49.0...\n\n\n3\n71224300273\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2449Z\n48.902998\n-94.854720\nno_data\n3.416786e+01\n-1.000000e+12\n1.372000e-02\n...\n4650.0\n3.770782e+06\n43\n461636.940\n8616.309267\n-1.000000e+12\n0\n1\n1\nLINESTRING (-94.86229 48.94092, -94.86228 48.9...\n\n\n4\n71224300283\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2450Z\n48.883377\n-94.783621\nno_data\n3.426341e+01\n-1.000000e+12\n5.050000e-03\n...\n10439.0\n2.952077e+07\n46\n470821.047\n9184.106587\n-1.000000e+12\n0\n5\n1\nLINESTRING (-94.73002 48.90430, -94.72952 48.9...\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n666\n74291700141\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2729Z\n39.811344\n-92.684233\nno_data\n3.869180e+01\n-1.000000e+12\n3.840000e-01\n...\n42.0\n1.060619e+02\n48\n2463828.961\n9553.659853\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68784 39.77391, -92.68804 39.7...\n\n\n667\n74291700151\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2727Z\n39.888856\n-92.683047\nno_data\n3.687848e+01\n-1.000000e+12\n1.897500e-01\n...\n42.0\n1.208373e+02\n48\n2473386.489\n9557.528221\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68497 39.84931, -92.68497 39.8...\n\n\n668\n74291700161\n-1.000000e+12\n-1.000000e+12\nno_data\n39.962507\n-92.671510\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n42.0\n9.634731e+01\n48\n2482916.982\n9530.492810\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.66461 39.92629, -92.66425 39.9...\n\n\n669\n74291700171\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2725Z\n40.045903\n-92.690485\nno_data\n3.647532e+01\n-1.000000e+12\n1.394000e-01\n...\n54.0\n6.557368e+02\n59\n2494780.714\n11863.732260\n-1.000000e+12\n0\n2\n1\nLINESTRING (-92.68041 40.00013, -92.68006 40.0...\n\n\n670\n74291700181\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2723Z\n40.133798\n-92.687305\nno_data\n3.384407e+01\n-1.000000e+12\n1.019290e+00\n...\n42.0\n1.250056e+02\n58\n2506424.880\n11644.165636\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68547 40.09150, -92.68519 40.0...\n\n\n\n\n671 rows × 111 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(11,7))\nSWOT_HR_shp1.plot(ax=ax, color='black')\n\n<Axes: >\n\n\n\n\n\n2. Lake Vector Shapefiles\nThe lake vector shapefiles can be accessed in the same way as the river shapefiles above.\n\nlake_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1', temporal = ('2022-08-22 19:24:18', '2022-08-22 19:30:50'))\n\nGranules found: 3\n\n\n\n#find the s3 link of the desired granule (This collection has three options: Obs, Unassigned, and Prior - we want Obs)\nlake_data = []\nfor g in lake_results:\n for l in earthaccess.results.DataGranule.data_links(g, access='direct'):\n if \"Obs\" in l:\n lake_data.append(l)\nprint(lake_data)\n\n['s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.zip']\n\n\n\ns3_file_obj2 = fs_s3.open(lake_data[0], mode='rb')\n\n\nwith zipfile.ZipFile(s3_file_obj2, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\n\nSWOT_HR_shp2 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.shp') \nSWOT_HR_shp2\n\n\n\n\n\n\n\n\nobs_id\nlake_id\noverlap\ntime\ntime_tai\ntime_str\nwse\nwse_u\nwse_r_u\nwse_std\n...\niono_c\nxovr_cal_c\np_name\np_grand_id\np_max_wse\np_max_area\np_ref_date\np_ref_ds\np_storage\ngeometry\n\n\n\n\n0\n742081R000002\n7420470702\n93\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.934\n0.051\n0.051\n0.159\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.35\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.75926 42.04142, -92.75977 42.041...\n\n\n1\n742081R000003\n7420472462\n75\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n37.037\n0.080\n0.080\n0.143\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.62\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.91651 42.01167, -92.91681 42.011...\n\n\n2\n742081R000008\n7420473212\n58\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.578\n0.181\n0.181\n0.058\n...\n0.0\n0.0\nHENDRICKSON MARSH LAKE\n-99999999\n-1.000000e+12\n45.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-93.24060 41.93319, -93.24066 41.933...\n\n\n3\n742081R000009\n7420470712\n73\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.910\n0.110\n0.110\n0.136\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n4.50\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.72557 42.03424, -92.72560 42.034...\n\n\n4\n742081R000011\n7420470582\n76\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.904\n0.109\n0.109\n0.628\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.89\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-93.39929 41.90871, -93.39945 41.908...\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n17240\n742070R000729\n7420857422\n92\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.186\n0.098\n0.098\n0.056\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n14.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.07581 47.57853, -95.07587 47.578...\n\n\n17241\n742070R000730\n7420848152\n96\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.071\n0.038\n0.038\n0.174\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n3.06\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-94.88532 47.61669, -94.88577 47.616...\n\n\n17242\n712070R000731\n7120812272\n74\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.104\n0.077\n0.077\n0.085\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n4.41\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.08313 47.57179, -95.08341 47.571...\n\n\n17243\n712070R000732\n7120816202\n67\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n32.713\n0.106\n0.106\n0.198\n...\n0.0\n0.0\nMUD LAKE\n-99999999\n-1.000000e+12\n6.30\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.39968 47.51004, -95.39975 47.510...\n\n\n17244\n742070R000733\n7420857422\n95\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n32.725\n0.093\n0.093\n0.119\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n14.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.07228 47.57473, -95.07257 47.574...\n\n\n\n\n17245 rows × 43 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(7,12))\nSWOT_HR_shp2.plot(ax=ax, color='black')\n\n<Axes: >\n\n\n\n\n\n3. Water Mask Pixel Cloud NetCDF\nAccessing the remaining files is different than the shp files above. We do not need to unzip the files because they are stored in native netCDF files in the cloud. For the rest of the products, we will open via xarray.\n\nwatermask_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))\n\nGranules found: 1\n\n\nThe pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.\n\nds_PIXC = xr.open_mfdataset(earthaccess.open([watermask_results[0]]), group = 'pixel_cloud', engine='h5netcdf')\nds_PIXC\n\n Opening 1 granules, approx size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (points: 489673, complex_depth: 2)\nCoordinates:\n latitude (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>\n longitude (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>\nDimensions without coordinates: points, complex_depth\nData variables: (12/49)\n azimuth_index (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>\n range_index (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>\n interferogram (points, complex_depth) float32 dask.array<chunksize=(489673, 2), meta=np.ndarray>\n power_plus_y (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n power_minus_y (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n coherent_power (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n ... ...\n solid_earth_tide (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n load_tide_fes (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n load_tide_got (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n pole_tide (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n ancillary_surface_classification_flag (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n pixc_qual (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\nAttributes:\n description: cloud of geolocated interferogram pixels\n interferogram_size_azimuth: 2923\n interferogram_size_range: 4575\n looks_to_efflooks: 1.75xarray.DatasetDimensions:points: 489673complex_depth: 2Coordinates: (2)latitude(points)float64dask.array<chunksize=(489673,), meta=np.ndarray>long_name :latitude (positive N, negative S)standard_name :latitudeunits :degrees_northvalid_min :-80.0valid_max :80.0comment :Geodetic latitude [-80,80] (degrees north of equator) of the pixel.\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\nlongitude\n\n\n(points)\n\n\nfloat64\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlongitude (degrees East)\n\nstandard_name :\n\nlongitude\n\nunits :\n\ndegrees_east\n\nvalid_min :\n\n-180.0\n\nvalid_max :\n\n180.0\n\ncomment :\n\nLongitude [-180,180) (east of the Greenwich meridian) of the pixel.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\nData variables: (49)\n\n\n\n\n\nazimuth_index\n\n\n(points)\n\n\nfloat64\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nrare interferogram azimuth index\n\nunits :\n\n1\n\nvalid_min :\n\n0\n\nvalid_max :\n\n999999\n\ncomment :\n\nRare interferogram azimuth index (indexed from 0).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nrange_index\n\n\n(points)\n\n\nfloat64\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nrare interferogram range index\n\nunits :\n\n1\n\nvalid_min :\n\n0\n\nvalid_max :\n\n999999\n\ncomment :\n\nRare interferogram range index (indexed from 0).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\ninterferogram\n\n\n(points, complex_depth)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673, 2), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nrare interferogram\n\nunits :\n\n1\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nComplex unflattened rare interferogram.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673, 2)\n(489673, 2)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\npower_plus_y\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\npower for plus_y channel\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nPower for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\npower_minus_y\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\npower for minus_y channel\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nPower for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ncoherent_power\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ncoherent power combination of minus_y and plus_y channels\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nPower computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nx_factor_plus_y\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nX factor for plus_y channel power\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nX factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nx_factor_minus_y\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nX factor for minus_y channel power\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nX factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwater_frac\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nwater fraction\n\nunits :\n\n1\n\nvalid_min :\n\n-1000.0\n\nvalid_max :\n\n10000.0\n\ncomment :\n\nNoisy estimate of the fraction of the pixel that is water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwater_frac_uncert\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nwater fraction uncertainty\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nUncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nclassification\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nclassification\n\nflag_meanings :\n\nland land_near_water water_near_land open_water land_near_dark_water dark_water_edge dark_water\n\nflag_values :\n\n[ 1 2 3 4 22 23 24]\n\nvalid_min :\n\n1\n\nvalid_max :\n\n24\n\ncomment :\n\nFlags indicating water detection results.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nfalse_detection_rate\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nfalse detection rate\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncomment :\n\nProbability of falsely detecting water when there is none.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nmissed_detection_rate\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nmissed detection rate\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncomment :\n\nProbability of falsely detecting no water when there is water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nprior_water_prob\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nprior water probability\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncomment :\n\nPrior probability of water occurring.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nbright_land_flag\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nbright land flag\n\nstandard_name :\n\nstatus_flag\n\nflag_meanings :\n\nnot_bright_land bright_land bright_land_or_water\n\nflag_values :\n\n[0 1 2]\n\nvalid_min :\n\n0\n\nvalid_max :\n\n2\n\ncomment :\n\nFlag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice). Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nlayover_impact\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlayover impact\n\nunits :\n\nm\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nEstimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\neff_num_rare_looks\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\neffective number of rare looks\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nEffective number of independent looks taken to form the rare interferogram.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nheight\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nheight above reference ellipsoid\n\nunits :\n\nm\n\nvalid_min :\n\n-1500.0\n\nvalid_max :\n\n15000.0\n\ncomment :\n\nHeight of the pixel above the reference ellipsoid.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ncross_track\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\napproximate cross-track location\n\nunits :\n\nm\n\nvalid_min :\n\n-75000.0\n\nvalid_max :\n\n75000.0\n\ncomment :\n\nApproximate cross-track location of the pixel.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\npixel_area\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\npixel area\n\nunits :\n\nm^2\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nPixel area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ninc\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nincidence angle\n\nunits :\n\ndegrees\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nIncidence angle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nphase_noise_std\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nphase noise standard deviation\n\nunits :\n\nradians\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nEstimate of the phase noise standard deviation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndlatitude_dphase\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsensitivity of latitude estimate to interferogram phase\n\nunits :\n\ndegrees/radian\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nSensitivity of the latitude estimate to the interferogram phase.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndlongitude_dphase\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsensitivity of longitude estimate to interferogram phase\n\nunits :\n\ndegrees/radian\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nSensitivity of the longitude estimate to the interferogram phase.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndheight_dphase\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsensitivity of height estimate to interferogram phase\n\nunits :\n\nm/radian\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nSensitivity of the height estimate to the interferogram phase.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndheight_droll\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsensitivity of height estimate to spacecraft roll\n\nunits :\n\nm/degrees\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nSensitivity of the height estimate to the spacecraft roll.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndheight_dbaseline\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsensitivity of height estimate to interferometric baseline\n\nunits :\n\nm/m\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nSensitivity of the height estimate to the interferometric baseline.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndheight_drange\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsensitivity of height estimate to range (delay)\n\nunits :\n\nm/m\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nSensitivity of the height estimate to the range (delay).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndarea_dheight\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsensitivity of pixel area to reference height\n\nunits :\n\nm^2/m\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nSensitivity of the pixel area to the reference height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nillumination_time\n\n\n(points)\n\n\ndatetime64[ns]\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime of illumination of each pixel (UTC)\n\nstandard_name :\n\ntime\n\ntai_utc_difference :\n\n[Value of TAI-UTC at time of first record]\n\nleap_second :\n\nYYYY-MM-DD hh:mm:ss\n\ncomment :\n\nTime of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\ndatetime64[ns] numpy.ndarray\n\n\n\n\n\n\n\n\n\nillumination_time_tai\n\n\n(points)\n\n\ndatetime64[ns]\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime of illumination of each pixel (TAI)\n\nstandard_name :\n\ntime\n\ncomment :\n\nTime of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\ndatetime64[ns] numpy.ndarray\n\n\n\n\n\n\n\n\n\neff_num_medium_looks\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\neffective number of medium looks\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nEffective number of independent looks taken in forming the medium interferogram (after adaptive averaging).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsig0\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsigma0\n\nunits :\n\n1\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nNormalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nphase_unwrapping_region\n\n\n(points)\n\n\nfloat64\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nphase unwrapping region index\n\nunits :\n\n1\n\nvalid_min :\n\n-1\n\nvalid_max :\n\n99999999\n\ncomment :\n\nPhase unwrapping region index.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\ninstrument_range_cor\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ninstrument range correction\n\nunits :\n\nm\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nTerm that incorporates all calibration corrections applied to range before geolocation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ninstrument_phase_cor\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ninstrument phase correction\n\nunits :\n\nradians\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nTerm that incorporates all calibration corrections applied to phase before geolocation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ninstrument_baseline_cor\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ninstrument baseline correction\n\nunits :\n\nm\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nTerm that incorporates all calibration corrections applied to baseline before geolocation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ninstrument_attitude_cor\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ninstrument attitude correction\n\nunits :\n\ndegrees\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nTerm that incorporates all calibration corrections applied to attitude before geolocation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nmodel_dry_tropo_cor\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ndry troposphere vertical correction\n\nsource :\n\nEuropean Centre for Medium-Range Weather Forecasts\n\ninstitution :\n\nECMWF\n\nunits :\n\nm\n\nvalid_min :\n\n-3.0\n\nvalid_max :\n\n-1.5\n\ncomment :\n\nEquivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nmodel_wet_tropo_cor\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nwet troposphere vertical correction\n\nsource :\n\nEuropean Centre for Medium-Range Weather Forecasts\n\ninstitution :\n\nECMWF\n\nunits :\n\nm\n\nvalid_min :\n\n-1.0\n\nvalid_max :\n\n0.0\n\ncomment :\n\nEquivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\niono_cor_gim_ka\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nionosphere vertical correction\n\nsource :\n\nGlobal Ionosphere Maps\n\ninstitution :\n\nJPL\n\nunits :\n\nm\n\nvalid_min :\n\n-0.5\n\nvalid_max :\n\n0.0\n\ncomment :\n\nEquivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nheight_cor_xover\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nheight correction from KaRIn crossovers\n\nunits :\n\nm\n\nvalid_min :\n\n-10.0\n\nvalid_max :\n\n10.0\n\ncomment :\n\nHeight correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ngeoid\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeoid height\n\nstandard_name :\n\ngeoid_height_above_reference_ellipsoid\n\nsource :\n\nEGM2008 (Pavlis et al., 2012)\n\nunits :\n\nm\n\nvalid_min :\n\n-150.0\n\nvalid_max :\n\n150.0\n\ncomment :\n\nGeoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsolid_earth_tide\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsolid Earth tide height\n\nsource :\n\nCartwright and Taylor (1971) and Cartwright and Edden (1973)\n\nunits :\n\nm\n\nvalid_min :\n\n-1.0\n\nvalid_max :\n\n1.0\n\ncomment :\n\nSolid-Earth (body) tide height. The zero-frequency permanent tide component is not included.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nload_tide_fes\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeocentric load tide height (FES)\n\nsource :\n\nFES2014b (Carrere et al., 2016)\n\ninstitution :\n\nLEGOS/CNES\n\nunits :\n\nm\n\nvalid_min :\n\n-0.2\n\nvalid_max :\n\n0.2\n\ncomment :\n\nGeocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nload_tide_got\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeocentric load tide height (GOT)\n\nsource :\n\nGOT4.10c (Ray, 2013)\n\ninstitution :\n\nGSFC\n\nunits :\n\nm\n\nvalid_min :\n\n-0.2\n\nvalid_max :\n\n0.2\n\ncomment :\n\nGeocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\npole_tide\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeocentric pole tide height\n\nsource :\n\nWahr (1985) and Desai et al. (2015)\n\nunits :\n\nm\n\nvalid_min :\n\n-0.2\n\nvalid_max :\n\n0.2\n\ncomment :\n\nGeocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nancillary_surface_classification_flag\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsurface classification\n\nstandard_name :\n\nstatus_flag\n\nsource :\n\nMODIS/GlobCover\n\ninstitution :\n\nEuropean Space Agency\n\nflag_meanings :\n\nopen_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin\n\nflag_values :\n\n[0 1 2 3 4 5 6]\n\nvalid_min :\n\n0\n\nvalid_max :\n\n6\n\ncomment :\n\n7-state surface type classification computed from a mask built with MODIS and GlobCover data.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\npixc_qual\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\nstandard_name :\n\nstatus_flag\n\nflag_meanings :\n\ngood bad\n\nflag_values :\n\n[0 1]\n\nvalid_min :\n\n0\n\nvalid_max :\n\n1\n\ncomment :\n\nQuality flag for pixel cloud data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (0)Attributes: (4)description :cloud of geolocated interferogram pixelsinterferogram_size_azimuth :2923interferogram_size_range :4575looks_to_efflooks :1.75\n\n\n\nplt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)\nplt.colorbar().set_label('Height (m)')\n\n\n\n\n4. Water Mask Pixel Cloud Vector Attribute NetCDF\n\nvector_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))\n\nGranules found: 1\n\n\n\nds_PIXCVEC = xr.open_mfdataset(earthaccess.open([vector_results[0]]), decode_cf=False, engine='h5netcdf')\nds_PIXCVEC\n\n Opening 1 granules, approx size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (points: 489673, nchar_reach_id: 11,\n nchar_node_id: 14, nchar_lake_id: 10,\n nchar_obs_id: 13)\nDimensions without coordinates: points, nchar_reach_id, nchar_node_id,\n nchar_lake_id, nchar_obs_id\nData variables:\n azimuth_index (points) int32 dask.array<chunksize=(489673,), meta=np.ndarray>\n range_index (points) int32 dask.array<chunksize=(489673,), meta=np.ndarray>\n latitude_vectorproc (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>\n longitude_vectorproc (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>\n height_vectorproc (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>\n reach_id (points, nchar_reach_id) |S1 dask.array<chunksize=(489673, 11), meta=np.ndarray>\n node_id (points, nchar_node_id) |S1 dask.array<chunksize=(489673, 14), meta=np.ndarray>\n lake_id (points, nchar_lake_id) |S1 dask.array<chunksize=(489673, 10), meta=np.ndarray>\n obs_id (points, nchar_obs_id) |S1 dask.array<chunksize=(489673, 13), meta=np.ndarray>\n ice_clim_f (points) int8 dask.array<chunksize=(489673,), meta=np.ndarray>\n ice_dyn_f (points) int8 dask.array<chunksize=(489673,), meta=np.ndarray>\nAttributes: (12/36)\n Conventions: CF-1.7\n title: Level 2 KaRIn high rate pixe...\n institution: CNES\n source: Simulation\n history: 2021-04-14 18:11:49Z: Creation\n platform: SWOT\n ... ...\n xref_input_l2_hr_pixc_vec_river_file: /work/ALT/swot/swotdev/desro...\n xref_static_river_db_file: \n xref_static_lake_db_file: /work/ALT/swot/swotpub/BD/BD...\n xref_l2_hr_lake_tile_config_parameter_file: /work/ALT/swot/swotdev/desro...\n ellipsoid_semi_major_axis: 6371008.771416667\n ellipsoid_flattening: 0.0xarray.DatasetDimensions:points: 489673nchar_reach_id: 11nchar_node_id: 14nchar_lake_id: 10nchar_obs_id: 13Coordinates: (0)Data variables: (11)azimuth_index(points)int32dask.array<chunksize=(489673,), meta=np.ndarray>_FillValue :2147483647long_name :rare interferogram azimuth indexunits :1valid_min :0valid_max :999999coordinates :longitude_vectorproc latitude_vectorproccomment :Rare interferogram azimuth index (indexed from 0).\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nint32 numpy.ndarray\n\n\n\n\n\n\n\n\nrange_index\n\n\n(points)\n\n\nint32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\n_FillValue :\n\n2147483647\n\nlong_name :\n\nrare interferogram range index\n\nunits :\n\n1\n\nvalid_min :\n\n0\n\nvalid_max :\n\n999999\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nRare interferogram range index (indexed from 0).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nint32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nlatitude_vectorproc\n\n\n(points)\n\n\nfloat64\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\n_FillValue :\n\n9.969209968386869e+36\n\nlong_name :\n\nheight-constrained geolocation latitude\n\nstandard_name :\n\nlatitude\n\nunits :\n\ndegrees_north\n\nvalid_min :\n\n-80.0\n\nvalid_max :\n\n80.0\n\ncomment :\n\nHeight-constrained geodetic latitude of the pixel. Units are in degrees north of the equator.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nlongitude_vectorproc\n\n\n(points)\n\n\nfloat64\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\n_FillValue :\n\n9.969209968386869e+36\n\nlong_name :\n\nheight-constrained geolocation longitude\n\nstandard_name :\n\nlongitude\n\nunits :\n\ndegrees_east\n\nvalid_min :\n\n-180.0\n\nvalid_max :\n\n180.0\n\ncomment :\n\nHeight-constrained geodetic longitude of the pixel. Positive=degrees east of the Greenwich meridian. Negative=degrees west of the Greenwich meridian.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.74 MiB\n3.74 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nheight_vectorproc\n\n\n(points)\n\n\nfloat32\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\n_FillValue :\n\n9.96921e+36\n\nlong_name :\n\nheight above reference ellipsoid\n\nunits :\n\nm\n\nvalid_min :\n\n-1500.0\n\nvalid_max :\n\n15000.0\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nHeight-constrained height of the pixel above the reference ellipsoid.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.87 MiB\n1.87 MiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nreach_id\n\n\n(points, nchar_reach_id)\n\n\n|S1\n\n\ndask.array<chunksize=(489673, 11), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nidentifier of the associated prior river reach\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nUnique reach identifier from the prior river database. The format of the identifier is CBBBBBRRRRT, where C=continent, B=basin, R=reach, T=type.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n5.14 MiB\n5.14 MiB\n\n\nShape\n(489673, 11)\n(489673, 11)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\n|S1 numpy.ndarray\n\n\n\n\n\n\n\n\n\nnode_id\n\n\n(points, nchar_node_id)\n\n\n|S1\n\n\ndask.array<chunksize=(489673, 14), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nidentifier of the associated prior river node\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nUnique node identifier from the prior river database. The format of the identifier is CBBBBBRRRRNNNT, where C=continent, B=basin, R=reach, N=node, T=type of water body.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n6.54 MiB\n6.54 MiB\n\n\nShape\n(489673, 14)\n(489673, 14)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\n|S1 numpy.ndarray\n\n\n\n\n\n\n\n\n\nlake_id\n\n\n(points, nchar_lake_id)\n\n\n|S1\n\n\ndask.array<chunksize=(489673, 10), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nidentifier of the associated prior lake\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nIdentifier of the lake from the lake prior database) associated to the pixel. The format of the identifier is CBBNNNNNNT, where C=continent, B=basin, N=counter within the basin, T=type of water body.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n4.67 MiB\n4.67 MiB\n\n\nShape\n(489673, 10)\n(489673, 10)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\n|S1 numpy.ndarray\n\n\n\n\n\n\n\n\n\nobs_id\n\n\n(points, nchar_obs_id)\n\n\n|S1\n\n\ndask.array<chunksize=(489673, 13), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nidentifier of the observed feature\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nTile-specific identifier of the observed feature associated to the pixel. The format of the identifier is CBBTTTSNNNNNN, where C=continent, B=basin, T=tile number, S=swath side, N=lake counter within the PIXC tile.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n6.07 MiB\n6.07 MiB\n\n\nShape\n(489673, 13)\n(489673, 13)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\n|S1 numpy.ndarray\n\n\n\n\n\n\n\n\n\nice_clim_f\n\n\n(points)\n\n\nint8\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\n_FillValue :\n\n127\n\nlong_name :\n\nclimatological ice cover flag\n\nflag_meanings :\n\nno_ice_cover partial_ice_cover full_ice_cover\n\nflag_values :\n\n[0 1 2]\n\ninstitution :\n\nUniversity of North Carolina\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nClimatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n478.20 kiB\n478.20 kiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nint8 numpy.ndarray\n\n\n\n\n\n\n\n\n\nice_dyn_f\n\n\n(points)\n\n\nint8\n\n\ndask.array<chunksize=(489673,), meta=np.ndarray>\n\n\n\n\n_FillValue :\n\n127\n\nlong_name :\n\ndynamical ice cover flag\n\nflag_meanings :\n\nno_ice_cover partial_ice_cover full_ice_cover\n\nflag_values :\n\n[0 1 2]\n\ninstitution :\n\nUniversity of North Carolina\n\ncoordinates :\n\nlongitude_vectorproc latitude_vectorproc\n\ncomment :\n\nDynamic ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n478.20 kiB\n478.20 kiB\n\n\nShape\n(489673,)\n(489673,)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nint8 numpy.ndarray\n\n\n\n\n\n\n\n\nIndexes: (0)Attributes: (36)Conventions :CF-1.7title :Level 2 KaRIn high rate pixel cloud vector attribute productinstitution :CNESsource :Simulationhistory :2021-04-14 18:11:49Z: Creationplatform :SWOTreferences :0.2reference_document :SWOT-TN-CDM-0677-CNEScontact :test@cnes.frcycle_number :7pass_number :522tile_number :93swath_side :Ltile_name :522_093Lcontinent :NAtime_coverage_start :2022-08-22 19:28:50.964042Ztime_coverage_end :2022-08-22 19:29:00.951707Zgeospatial_lon_min :-90.50978392584558geospatial_lon_max :-89.62391069818588geospatial_lat_min :34.79938523279299geospatial_lat_max :35.48806356833591inner_first_longitude :-90.50978392584558inner_first_latitude :35.362946480544466inner_last_longitude :-90.36177519926197inner_last_latitude :34.79938523279299outer_first_longitude :-89.76709094457013outer_first_latitude :35.48806356833591outer_last_longitude :-89.62391069818588outer_last_latitude :34.92312785225805xref_input_l2_hr_pixc_file :/work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/simu/SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.ncxref_input_l2_hr_pixc_vec_river_file :/work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/river/SWOT_L2_HR_PIXCVecRiver_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.ncxref_static_river_db_file :xref_static_lake_db_file :/work/ALT/swot/swotpub/BD/BD_lakes/PLDxref_l2_hr_lake_tile_config_parameter_file :/work/ALT/swot/swotdev/desrochesd/swot-sds-16-10/swotCNES/PGE/lake_tile/lake_tile_param.cfgellipsoid_semi_major_axis :6371008.771416667ellipsoid_flattening :0.0\n\n\n\npixcvec_htvals = ds_PIXCVEC.height_vectorproc.compute()\npixcvec_latvals = ds_PIXCVEC.latitude_vectorproc.compute()\npixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc.compute()\n\n#Before plotting, we set all fill values to nan so that the graph shows up better spatially\npixcvec_htvals[pixcvec_htvals > 15000] = np.nan\npixcvec_latvals[pixcvec_latvals > 80] = np.nan\npixcvec_lonvals[pixcvec_lonvals > 180] = np.nan\n\n\nplt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)\nplt.colorbar().set_label('Height (m)')\n\n\n\n\n5. Raster NetCDF\n\nraster_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1', temporal = ('2022-08-22 19:28:50', '2022-08-22 19:29:11'), point = ('-90', '35'))\n\nGranules found: 2\n\n\n\n#this collection has 100m and 250m granules, but we only want 100m\nraster_data = []\nfor g in raster_results:\n for l in earthaccess.results.DataGranule.data_links(g, access='direct'):\n if \"100m\" in l:\n raster_data.append(l)\nprint(raster_data)\n\n['s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1/SWOT_L2_HR_Raster_100m_UTM15S_N_x_x_x_007_522_047F_20220822T192850_20220822T192911_Dx0000_01.nc']\n\n\n\nds_raster = xr.open_mfdataset(earthaccess.open([raster_data[0]], provider = 'POCLOUD'), engine='h5netcdf')\nds_raster\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (x: 1543, y: 1540)\nCoordinates:\n * x (x) float64 6.567e+05 6.568e+05 ... 8.109e+05\n * y (y) float64 3.775e+06 3.775e+06 ... 3.929e+06\nData variables: (12/30)\n crs object ...\n longitude (y, x) float64 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n latitude (y, x) float64 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n wse (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n wse_uncert (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n water_area (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n ... ...\n load_tide_fes (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n load_tide_got (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n pole_tide (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n model_dry_tropo_cor (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n model_wet_tropo_cor (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\n iono_cor_gim_ka (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>\nAttributes: (12/45)\n Conventions: CF-1.7\n title: Level 2 KaRIn High Rate Raster Data Product\n institution: JPL\n source: Large scale simulator\n history: 2021-09-08T22:28:33Z : Creation\n mission_name: SWOT\n ... ...\n utm_zone_num: 15\n mgrs_latitude_band: S\n x_min: 656700.0\n x_max: 810900.0\n y_min: 3775000.0\n y_max: 3928900.0xarray.DatasetDimensions:x: 1543y: 1540Coordinates: (2)x(x)float646.567e+05 6.568e+05 ... 8.109e+05long_name :x coordinate of projectionstandard_name :projection_x_coordinateunits :mvalid_min :-10000000.0valid_max :10000000.0comment :UTM easting coordinate of the pixel.array([656700., 656800., 656900., ..., 810700., 810800., 810900.])y(y)float643.775e+06 3.775e+06 ... 3.929e+06long_name :y coordinate of projectionstandard_name :projection_y_coordinateunits :mvalid_min :-20000000.0valid_max :20000000.0comment :UTM northing coordinate of the pixel.array([3775000., 3775100., 3775200., ..., 3928700., 3928800., 3928900.])Data variables: (30)crs()object...long_name :CRS Definitiongrid_mapping_name :transverse_mercatorprojected_crs_name :WGS 84 / UTM zone 15Ngeographic_crs_name :WGS 84reference_ellipsoid_name :WGS 84horizontal_datum_name :WGS_1984prime_meridian_name :Greenwichfalse_easting :500000.0false_northing :0.0longitude_of_central_meridian :-93.0longitude_of_prime_meridian :0.0latitude_of_projection_origin :0.0scale_factor_at_central_meridian :0.9996semi_major_axis :6378137.0inverse_flattening :298.257223563crs_wkt :PROJCS[\"WGS 84 / UTM zone 15N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32615\"]]spatial_ref :PROJCS[\"WGS 84 / UTM zone 15N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32615\"]]comment :UTM zone coordinate reference system.[1 values with dtype=object]longitude(y, x)float64dask.array<chunksize=(1540, 1543), meta=np.ndarray>long_name :longitude (degrees East)standard_name :longitudegrid_mapping :crsunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :Longitude [-180,180) (east of the Greenwich meridian) of the pixel.\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n18.13 MiB\n18.13 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\nlatitude\n\n\n(y, x)\n\n\nfloat64\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlatitude (positive N, negative S)\n\nstandard_name :\n\nlatitude\n\ngrid_mapping :\n\ncrs\n\nunits :\n\ndegrees_north\n\nvalid_min :\n\n-80.0\n\nvalid_max :\n\n80.0\n\ncomment :\n\nLatitude [-80,80] (degrees north of equator) of the pixel.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n18.13 MiB\n18.13 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwse\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nwater surface elevation above geoid\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-1500.0\n\nvalid_max :\n\n15000.0\n\ncomment :\n\nWater surface elevation of the pixel above the geoid and after using models to subtract the effects of tides (solid_earth_tide, load_tide_fes, pole_tide).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwse_uncert\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nuncertainty in the water surface elevation\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\n1-sigma uncertainty in the water surface elevation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwater_area\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsurface area of water\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm^2\n\nvalid_min :\n\n-2000000.0\n\nvalid_max :\n\n2000000000.0\n\ncomment :\n\nSurface area of the water pixels.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwater_area_uncert\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nuncertainty in the water surface area\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm^2\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n2000000000.0\n\ncomment :\n\n1-sigma uncertainty in the water surface area\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwater_frac\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nwater fraction\n\ngrid_mapping :\n\ncrs\n\nunits :\n\n1\n\nvalid_min :\n\n-1000.0\n\nvalid_max :\n\n10000.0\n\ncomment :\n\nFraction of the pixel that is water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwater_frac_uncert\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nuncertainty in the water fraction\n\ngrid_mapping :\n\ncrs\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\n1-sigma uncertainty in the water fraction.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsig0\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsigma0\n\ngrid_mapping :\n\ncrs\n\nunits :\n\n1\n\nvalid_min :\n\n-1000.0\n\nvalid_max :\n\n10000000.0\n\ncomment :\n\nNormalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsig0_uncert\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nuncertainty in sigma0\n\ngrid_mapping :\n\ncrs\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1000.0\n\ncomment :\n\n1-sigma uncertainty in sigma0. The value is provided in linear units. This value is a one-sigma additive (not multiplicative) uncertainty term, which can be added to or subtracted from sigma0.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ninc\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nincidence angle\n\ngrid_mapping :\n\ncrs\n\nunits :\n\ndegrees\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n90.0\n\ncomment :\n\nIncidence angle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ncross_track\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\napproximate cross-track location\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-75000.0\n\nvalid_max :\n\n75000.0\n\ncomment :\n\nApproximate cross-track location of the pixel.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nillumination_time\n\n\n(y, x)\n\n\ndatetime64[ns]\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime of illumination of each pixel (UTC)\n\nstandard_name :\n\ntime\n\ntai_utc_difference :\n\n-32.0\n\nleap_second :\n\nYYYY-MM-DDThh:mm:ssZ\n\ncomment :\n\nTime of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n18.13 MiB\n18.13 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\ndatetime64[ns] numpy.ndarray\n\n\n\n\n\n\n\n\n\nillumination_time_tai\n\n\n(y, x)\n\n\ndatetime64[ns]\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime of illumination of each pixel (TAI)\n\nstandard_name :\n\ntime\n\ncomment :\n\nTime of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n18.13 MiB\n18.13 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\ndatetime64[ns] numpy.ndarray\n\n\n\n\n\n\n\n\n\nraster_qual\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nstandard_name :\n\nstatus_flag\n\ngrid_mapping :\n\ncrs\n\nflag_meanings :\n\ngood bad\n\nflag_values :\n\n[0 1]\n\nvalid_min :\n\n0\n\nvalid_max :\n\n1\n\ncomment :\n\nQuality flag for raster data.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nn_wse_pix\n\n\n(y, x)\n\n\nfloat64\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nnumber of wse pixels\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nl\n\nvalid_min :\n\n0\n\nvalid_max :\n\n999999\n\ncomment :\n\nNumber of pixel cloud samples used in water surface elevation aggregation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n18.13 MiB\n18.13 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nn_area_pix\n\n\n(y, x)\n\n\nfloat64\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nnumber of area pixels\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nl\n\nvalid_min :\n\n0\n\nvalid_max :\n\n999999\n\ncomment :\n\nNumber of pixel cloud samples used in water area and water fraction aggregation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n18.13 MiB\n18.13 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\ndark_frac\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nfractional area of dark water\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nl\n\nvalid_min :\n\n-1000.0\n\nvalid_max :\n\n10000.0\n\ncomment :\n\nFraction of pixel water area covered by dark water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nice_clim_flag\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nclimatological ice cover flag\n\nsource :\n\nUNC\n\ngrid_mapping :\n\ncrs\n\nflag_meanings :\n\nno_ice_cover uncertain_ice_cover full_ice_cover\n\nflag_values :\n\n[0 1 2]\n\nvalid_min :\n\n0\n\nvalid_max :\n\n2\n\ncomment :\n\nClimatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the pixel is likely not ice covered, may or may not be partially or fully ice covered, and likely fully ice covered, respectively.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nice_dyn_flag\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ndynamic ice cover flag\n\nsource :\n\nUNC\n\ngrid_mapping :\n\ncrs\n\nflag_meanings :\n\nno_ice_cover partial_ice_cover full_ice_cover\n\nflag_values :\n\n[0 1 2]\n\nvalid_min :\n\n0\n\nvalid_max :\n\n2\n\ncomment :\n\nDynamic ice cover flag indicating whether the surface is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the pixel is not ice covered, partially ice covered, and fully ice covered, respectively.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nlayover_impact\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlayover impact\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-999999.0\n\nvalid_max :\n\n999999.0\n\ncomment :\n\nEstimate of the water surface elevation error caused by layover.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ngeoid\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeoid height\n\nstandard_name :\n\ngeoid_height_above_reference_ellipsoid\n\nsource :\n\nEGM2008 (Pavlis et al., 2012)\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-150.0\n\nvalid_max :\n\n150.0\n\ncomment :\n\nGeoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsolid_earth_tide\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsolid Earth tide height\n\nsource :\n\nCartwright and Taylor (1971) and Cartwright and Edden (1973)\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-1.0\n\nvalid_max :\n\n1.0\n\ncomment :\n\nSolid-Earth (body) tide height. The zero-frequency permanent tide component is not included.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nload_tide_fes\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeocentric load tide height (FES)\n\nsource :\n\nFES2014b (Carrere et al., 2016)\n\ninstitution :\n\nLEGOS/CNES\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-0.2\n\nvalid_max :\n\n0.2\n\ncomment :\n\nGeocentric load tide height. The effect of the ocean tide loading of the Earth’s crust.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nload_tide_got\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeocentric load tide height (GOT)\n\nsource :\n\nGOT4.10c (Ray, 2013)\n\ninstitution :\n\nGSFC\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-0.2\n\nvalid_max :\n\n0.2\n\ncomment :\n\nGeocentric load tide height. The effect of the ocean tide loading of the Earth’s crust. This value is reported for reference but is not applied to the reported height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\npole_tide\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ngeocentric pole tide height\n\nsource :\n\nWahr (1985) and Desai et al. (2015)\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-0.2\n\nvalid_max :\n\n0.2\n\ncomment :\n\nGeocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth’s crust).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nmodel_dry_tropo_cor\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ndry troposphere vertical correction\n\nsource :\n\nEuropean Centre for Medium-Range Weather Forecasts\n\ninstitution :\n\nECMWF\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-3.0\n\nvalid_max :\n\n-1.5\n\ncomment :\n\nEquivalent vertical correction due to dry troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nmodel_wet_tropo_cor\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nwet troposphere vertical correction\n\nsource :\n\nEuropean Centre for Medium-Range Weather Forecasts\n\ninstitution :\n\nECMWF\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-1.0\n\nvalid_max :\n\n0.0\n\ncomment :\n\nEquivalent vertical correction due to wet troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\niono_cor_gim_ka\n\n\n(y, x)\n\n\nfloat32\n\n\ndask.array<chunksize=(1540, 1543), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nionosphere vertical correction\n\nsource :\n\nGlobal Ionosphere Maps\n\ninstitution :\n\nJPL\n\ngrid_mapping :\n\ncrs\n\nunits :\n\nm\n\nvalid_min :\n\n-0.5\n\nvalid_max :\n\n0.0\n\ncomment :\n\nEquivalent vertical correction due to ionosphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.06 MiB\n9.06 MiB\n\n\nShape\n(1540, 1543)\n(1540, 1543)\n\n\nDask graph\n1 chunks in 2 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\nIndexes: (2)xPandasIndexPandasIndex(Index([656700.0, 656800.0, 656900.0, 657000.0, 657100.0, 657200.0, 657300.0,\n 657400.0, 657500.0, 657600.0,\n ...\n 810000.0, 810100.0, 810200.0, 810300.0, 810400.0, 810500.0, 810600.0,\n 810700.0, 810800.0, 810900.0],\n dtype='float64', name='x', length=1543))yPandasIndexPandasIndex(Index([3775000.0, 3775100.0, 3775200.0, 3775300.0, 3775400.0, 3775500.0,\n 3775600.0, 3775700.0, 3775800.0, 3775900.0,\n ...\n 3928000.0, 3928100.0, 3928200.0, 3928300.0, 3928400.0, 3928500.0,\n 3928600.0, 3928700.0, 3928800.0, 3928900.0],\n dtype='float64', name='y', length=1540))Attributes: (45)Conventions :CF-1.7title :Level 2 KaRIn High Rate Raster Data Productinstitution :JPLsource :Large scale simulatorhistory :2021-09-08T22:28:33Z : Creationmission_name :SWOTreferences :https://github.com/SWOTAlgorithms/Raster-Processorreference_document :JPL D-56416 - Revision A (DRAFT) - November 5, 2020contact :alexander.t.corben[at]jpl.nasa.govcycle_number :7pass_number :522scene_number :47tile_numbers :[92 93 94 95 92 93 94 95]tile_names :522_092L, 522_093L, 522_094L, 522_095L, 522_092R, 522_093R, 522_094R, 522_095Rtile_polarizations :V, V, V, V, V, V, V, Vcoordinate_reference_system :Universal Transverse Mercatorresolution :100.0short_name :L2_HR_Rasterdescriptor_string :100m_UTM15S_N_x_x_xcrid :Dx0000product_version :V0.1pge_name :adt_pge_standinpge_version :V0.1time_coverage_start :2022-08-22 19:28:50.964042Ztime_coverage_end :2022-08-22 19:29:10.946208Zgeospatial_lon_min :-91.27757002156555geospatial_lon_max :-89.62061588835118geospatial_lat_min :34.09943218249787geospatial_lat_max :35.464214684504334left_first_longitude :-89.89843338760357left_first_latitude :35.464214684504334left_last_longitude :-89.62061588835118left_last_latitude :34.33243031374548right_first_longitude :-91.27757002156555right_first_latitude :35.22613283570163right_last_longitude :-90.98228790375923right_last_latitude :34.09943218249787xref_input_l2_hr_pixc_files :SWOT_L2_HR_PIXC_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.ncxref_input_l2_hr_pixcvec_files :SWOT_L2_HR_PIXCVec_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.ncutm_zone_num :15mgrs_latitude_band :Sx_min :656700.0x_max :810900.0y_min :3775000.0y_max :3928900.0\n\n\nIt’s easy to analyze and plot the data with packages such as hvplot!\n\nds_raster.wse.hvplot.image(y='y', x='x')" }, { "objectID": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html", diff --git a/sitemap.xml b/sitemap.xml index 4724e4e8..22b05a30 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,338 +2,338 @@ https://podaac.github.io/tutorials/quarto_text/OPERA.html - 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