From aa8554c6a08607480a9d01bae921314ceb24d002 Mon Sep 17 00:00:00 2001 From: kmcquil Date: Thu, 22 Aug 2024 12:06:14 -0400 Subject: [PATCH 1/4] added usgs transform to swot tutorial and dataset --- notebooks/datasets/SWOT_USGS_Comparison.ipynb | 342 ++++++++++++++ .../resources/usgs_swot_merged_example.csv | 426 ++++++++++++++++++ 2 files changed, 768 insertions(+) create mode 100644 notebooks/datasets/SWOT_USGS_Comparison.ipynb create mode 100644 notebooks/resources/usgs_swot_merged_example.csv diff --git a/notebooks/datasets/SWOT_USGS_Comparison.ipynb b/notebooks/datasets/SWOT_USGS_Comparison.ipynb new file mode 100644 index 00000000..2073a26f --- /dev/null +++ b/notebooks/datasets/SWOT_USGS_Comparison.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare SWOT water surface elevation with USGS gage heights\n", + "\n", + "#### *Author: Katie McQuillan and George Allen, Virginia Tech*\n", + "\n", + "## Summary \n", + "This notebook showcases how to transform the horizontal and vertical coordinates of USGS gage heights to match SWOT LakeSP water surface elevation using GDAL. \n", + "\n", + "## Requirements\n", + "\n", + "### 1. Compute environment \n", + "\n", + "This tutorial is written to run in the following environment:\n", + "- **Local compute environment** e.g. laptop, server: this tutorial can be run on your local machine\n", + "\n", + "- GDAL must be installed (https://gdal.org/)\n", + "\n", + "### 2. Earthdata Login\n", + "\n", + "An 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. \n", + "\n", + "\n", + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from osgeo import osr\n", + "import pandas as pd\n", + "import numpy as np\n", + "import math\n", + "import subprocess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. USGS Lake/Reservoir Water Surface Elevation dataset can be acccessed using the DataRetrieval python module with the parameter code 62615. \n", + "\n", + "2. SWOT LakeSP dataset can be accessed using the EarthAccess python module or the PO.DAAC Data Downloader. \n", + "\n", + "Cotemporal SWOT LakeSP and USGS observations were used to form the dataset used for comparisons including X lakes with gages. To directly compare SWOT and USGS datasets, the USGS horizontal and vertical coordiantes must be transformed to match the SWOT datums. Datums for each dataset are noted in Table 1. \n", + "\n", + "It is important to note that using the generic WGS84 (EPSG:4326) is not recommended because it is based on a datum ensemble whose positional accuracy is approximately two meters. Instead, a realization such as WGS84 (G1762) is recommended. WGS84 (G1762) and ITRF 2014 are equivalent for all practical purposes when their epochs are the same. \n", + "\n", + "Epochs are used to define a reference date for positions esablished using the datum ellipsoid and reference frame. Due to tectonic plate movement, landmasses are constantly moving in relationship to each other and in relation to the reference frame. Therefore, accounting for the epoch is necessary for accurate coordinate transformations. The NAD83 (2011) epoch is 2010.0. The standard epoch of WGS84 (G1762) is 2005.0 and the standard epoch of ITRF2014 is 2010.0. Since SWOT is based on ITRF2014, we set the target epoch to 2010.0. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Table 1. SWOT and USGS datum information\n", + "| Data source | Horizontal Datum | Reference Ellipsoid | Vertical Datum | EPSG Code |\n", + "| --- | --- | --- | --- | --- |\n", + "| SWOT | ITRF2014 | WGS84 | EGM2008 | EPSG:9057+EPSG:3855 |\n", + "| USGS | NAD83 (2011) | GRS80 | NAVD88 | EPSG:6349 |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "USGS data is represented using EPSG:6349. EPSG:6349 is a compound CRS that represents NAD83 (2011) + NAVD88 height. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "COMPD_CS[\"NAD83(2011) + NAVD88 height\",\n", + " GEOGCS[\"NAD83(2011)\",\n", + " DATUM[\"NAD83_National_Spatial_Reference_System_2011\",\n", + " SPHEROID[\"GRS 1980\",6378137,298.257222101,\n", + " AUTHORITY[\"EPSG\",\"7019\"]],\n", + " AUTHORITY[\"EPSG\",\"1116\"]],\n", + " PRIMEM[\"Greenwich\",0,\n", + " AUTHORITY[\"EPSG\",\"8901\"]],\n", + " UNIT[\"degree\",0.0174532925199433,\n", + " AUTHORITY[\"EPSG\",\"9122\"]],\n", + " AXIS[\"Latitude\",NORTH],\n", + " AXIS[\"Longitude\",EAST],\n", + " AUTHORITY[\"EPSG\",\"6318\"]],\n", + " VERT_CS[\"NAVD88 height\",\n", + " VERT_DATUM[\"North American Vertical Datum 1988\",2005,\n", + " AUTHORITY[\"EPSG\",\"5103\"]],\n", + " UNIT[\"metre\",1,\n", + " AUTHORITY[\"EPSG\",\"9001\"]],\n", + " AXIS[\"Gravity-related height\",UP],\n", + " AUTHORITY[\"EPSG\",\"5703\"]],\n", + " AUTHORITY[\"EPSG\",\"6349\"]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\kmcquil\\anaconda3\\envs\\swot-wind\\lib\\site-packages\\osgeo\\osr.py:410: FutureWarning: Neither osr.UseExceptions() nor osr.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Details of the the compound NAD83(2011) + NAVD88 (EPSG:6349)\n", + "src = osr.SpatialReference()\n", + "src.ImportFromEPSG(6349)\n", + "print(src)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SWOT data is represented using EPSG:9057 + EPSG:3855. EPSG:9057 represents WGS84 (G1762) and EPSG: 3855 represents EGM 2008. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GEOGCS[\"WGS 84 (G1762)\",\n", + " DATUM[\"World_Geodetic_System_1984_G1762\",\n", + " SPHEROID[\"WGS 84\",6378137,298.257223563,\n", + " AUTHORITY[\"EPSG\",\"7030\"]],\n", + " AUTHORITY[\"EPSG\",\"1156\"]],\n", + " PRIMEM[\"Greenwich\",0,\n", + " AUTHORITY[\"EPSG\",\"8901\"]],\n", + " UNIT[\"degree\",0.0174532925199433,\n", + " AUTHORITY[\"EPSG\",\"9122\"]],\n", + " AXIS[\"Latitude\",NORTH],\n", + " AXIS[\"Longitude\",EAST],\n", + " AUTHORITY[\"EPSG\",\"9057\"]]\n" + ] + } + ], + "source": [ + "# Details for EPSG:9057 WGS84 (G1762)\n", + "dst = osr.SpatialReference()\n", + "dst.ImportFromEPSG(9057)\n", + "print(dst)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VERT_CS[\"EGM2008 height\",\n", + " VERT_DATUM[\"EGM2008 geoid\",2005,\n", + " AUTHORITY[\"EPSG\",\"1027\"]],\n", + " UNIT[\"metre\",1,\n", + " AUTHORITY[\"EPSG\",\"9001\"]],\n", + " AXIS[\"Gravity-related height\",UP],\n", + " AUTHORITY[\"EPSG\",\"3855\"]]\n" + ] + } + ], + "source": [ + "# Details of EPSG:3855 EGM2008\n", + "v_dst = osr.SpatialReference()\n", + "v_dst.ImportFromEPSG(3855)\n", + "print(v_dst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transform USGS coordinates for comparison with SWOT LakeSP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prep the data" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of cotemporal USGS and SWOT observations = 425\n" + ] + }, + { + "data": { + "text/plain": [ + "CompletedProcess(args='cd c:\\\\Users\\\\kmcquil\\\\Documents\\\\tutorials\\\\notebooks\\\\resources && gdaltransform -s_coord_epoch \"2010.0\" -t_coord_epoch \"2010.0\" -s_srs \"EPSG:6349\" -t_srs \"EPSG:9057+EPSG:3855\" < \"gdal_in.txt\" > \"gdal_out.txt\"', returncode=0)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Change working directory to tutorials folder\n", + "os.chdir('.')\n", + "\n", + "# Open the dataset of cotemporal SWOT and USGS observations\n", + "swot_usgs_df = pd.read_csv(\"notebooks/resources/usgs_swot_merged_example.csv\", index_col=0)\n", + "\n", + "# How many cotemporal observations? \n", + "print('The number of cotemporal USGS and SWOT observations = ' + str(swot_usgs_df.shape[0]))\n", + "\n", + "# Get data into correct format to pass to gdal including longitude, latitude, and gage height in feet\n", + "in_gdal = swot_usgs_df[[\"usgs_long\", \"usgs_lat\", \"usgs_X_62615_00000\"]].copy()\n", + "\n", + "# Since the USGS heights are in feet but the projection we have assigned are in meters, convert heights to meters\n", + "in_gdal.loc[:,'usgs_X_62615_00000'] = in_gdal.loc[:,'usgs_X_62615_00000'] * 0.3048\n", + "\n", + "# Create a column with long, lat, height combined \n", + "in_gdal.loc[:,\"out\"] = [\n", + " str(i) + \" \" + str(j) + \" \" + str(k)\n", + " for i, j, k in zip(\n", + " in_gdal[\"usgs_long\"],\n", + " in_gdal[\"usgs_lat\"],\n", + " in_gdal[\"usgs_X_62615_00000\"],\n", + " )\n", + "]\n", + "\n", + "# Save the combined column to a text file\n", + "in_gdal[\"out\"].to_csv(\"notebooks/resources/gdal_in.txt\", header=False, index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Transform using gdal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write the gdal command and run in the shell \n", + "cd_command = \"cd \" + os.getcwd() + \"\\\\notebooks\\\\resources && \"\n", + "gdal_command = 'gdaltransform -s_coord_epoch \"2010.0\" -t_coord_epoch \"2010.0\" -s_srs \"EPSG:6349\" -t_srs \"EPSG:9057+EPSG:3855\" < \"gdal_in.txt\" > \"gdal_out.txt\"'\n", + "subprocess.run(cd_command + gdal_command, shell=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate error statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " MBE (m) MAE (m) RMSE (m) One-Sigma (m)\n", + "0 -0.144761 0.299489 0.974874 0.139806\n" + ] + } + ], + "source": [ + "# Merge back with the original data \n", + "out_gdal = pd.read_csv(\"notebooks/resources/gdal_out.txt\", header=None)\n", + "out_gdal = out_gdal.rename(columns={0: \"result\"})\n", + "out_gdal[[\"usgs_long\", \"usgs_lat\", \"usgs_X_62615_00000_egm0_meters\"]] = out_gdal[\"result\"].str.split(\" \", expand=True)\n", + "swot_usgs_df[\"usgs_X_62615_00000_egm0_meters\"] = out_gdal[\"usgs_X_62615_00000_egm0_meters\"].astype(float)\n", + "\n", + "# Error stats\n", + "mae = np.mean(np.abs(np.subtract(swot_usgs_df[\"usgs_X_62615_00000_egm0_meters\"], swot_usgs_df[\"swot_wse\"])))\n", + "bias = np.mean(np.subtract(swot_usgs_df[\"usgs_X_62615_00000_egm0_meters\"], swot_usgs_df[\"swot_wse\"]))\n", + "rmse = math.sqrt(np.square(np.subtract(swot_usgs_df[\"usgs_X_62615_00000_egm0_meters\"], swot_usgs_df[\"swot_wse\"])).mean())\n", + "one_sigma = np.quantile(np.abs(np.subtract(swot_usgs_df[\"usgs_X_62615_00000_egm0_meters\"], swot_usgs_df[\"swot_wse\"])),0.68)\n", + "results = pd.DataFrame([[bias, mae, rmse, one_sigma]], columns=[\"MBE (m)\", \"MAE (m)\", \"RMSE (m)\", \"One-Sigma (m)\"])\n", + "print(results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "swot-wind", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/resources/usgs_swot_merged_example.csv b/notebooks/resources/usgs_swot_merged_example.csv new file mode 100644 index 00000000..68f25d92 --- /dev/null +++ b/notebooks/resources/usgs_swot_merged_example.csv @@ -0,0 +1,426 @@ +,swot_lake_id,swot_wse,swot_quality_f,swot_xovr_cal_q,swot_datetime,usgs_site_no,dateTime,usgs_X_62615_00000,usgs_X_62615_00000_cd,usgs_lat,usgs_long +0,7730010462,1294.975,0,1,2024-01-25 09:32:17.380000+00:00,10382000,2024-01-25 09:30:00+00:00,4251.07,P,42.6034892,-120.1869045 +1,7730010462,1294.975,0,1,2024-01-25 09:32:17.380000+00:00,10382000,2024-01-25 09:30:00+00:00,4251.07,P,42.6034892,-120.1869045 +2,7250166752,542.349,0,1,2024-01-25 16:49:53.884000+00:00,4253300,2024-01-25 16:45:00+00:00,1780.53,P,43.74519444,-74.78263889 +3,7310089123,499.69,0,1,2024-01-25 16:49:56.080000+00:00,1314500,2024-01-25 16:45:00+00:00,1640.66,A,43.75562018,-74.2759825 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15:29:04.973000+00:00,2098197,2024-05-01 15:30:00+00:00,215.29,P,35.6547222,-79.0683333 +414,7320345753,22.417,0,0,2024-05-01 15:28:20.941000+00:00,2172000,2024-05-01 15:30:00+00:00,74.51,P,33.24448056,-79.9915556 +415,7320345753,22.417,0,0,2024-05-01 15:28:20.941000+00:00,2171635,2024-05-01 15:30:00+00:00,74.52,P,33.39888889,-79.9975 +416,7320256073,22.32,0,0,2024-05-01 15:28:23.723000+00:00,2171000,2024-05-01 15:30:00+00:00,74.7,P,33.4495361,-80.1644444 +417,7320256073,22.399,0,1,2024-05-02 02:17:32.279000+00:00,2171000,2024-05-02 02:15:00+00:00,74.68,P,33.4495361,-80.1644444 +418,7250215982,180.199,0,1,2024-05-02 02:13:58.030000+00:00,4129950,2024-05-02 02:15:00+00:00,593.11,P,45.57666667,-84.4880556 +419,7250202733,182.655,0,1,2024-05-02 02:13:27.530000+00:00,463741090521301,2024-05-02 02:14:00+00:00,601.75,P,46.6280833,-90.8703889 +420,7320256073,22.399,0,1,2024-05-02 02:17:32.279000+00:00,2171000,2024-05-02 02:15:00+00:00,74.68,P,33.4495361,-80.1644444 +421,7250215982,180.199,0,1,2024-05-02 02:13:58.030000+00:00,4129950,2024-05-02 02:15:00+00:00,593.11,P,45.57666667,-84.4880556 +422,7250202733,182.655,0,1,2024-05-02 02:13:27.530000+00:00,463741090521301,2024-05-02 02:14:00+00:00,601.75,P,46.6280833,-90.8703889 +423,7250032202,260.276,0,0,2024-05-03 02:15:29.394000+00:00,4097188,2024-05-02 04:45:00+00:00,856.01,P,42.1908768,-85.5508343 +424,7250202733,182.619,0,0,2024-05-03 02:13:51.683000+00:00,463741090521301,2024-05-02 04:58:00+00:00,601.7,P,46.6280833,-90.8703889 From ca2a5714465a276c9d644aa13ac9aac9a311c9e0 Mon Sep 17 00:00:00 2001 From: Cassie Nickles Date: Thu, 22 Aug 2024 22:00:09 +0000 Subject: [PATCH 2/4] Link SWOT Datum Tutorial to Cookbook Add ingredients for PO.DAAC Cookbook to tutorial and connect in all the pieces. --- _quarto.yml | 2 + notebooks/datasets/SWOT_USGS_Comparison.ipynb | 80 ++++++++++--------- quarto_text/SWOT.qmd | 14 ++++ quarto_text/SWOT_Datum.qmd | 26 ++++++ 4 files changed, 83 insertions(+), 39 deletions(-) create mode 100644 quarto_text/SWOT_Datum.qmd diff --git a/_quarto.yml b/_quarto.yml index 47f26b98..12fd4069 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -172,6 +172,8 @@ website: href: notebooks/GIS/SWOT_datetime_GIS.ipynb - section: "Transform Data" contents: + - text: "Datum Conversion" + href: quarto_text/SWOT_Datum.qmd - text: "River Time Series" href: notebooks/datasets/Hydrocron_SWOT_timeseries_examples.ipynb - text: "Lake Time Series" diff --git a/notebooks/datasets/SWOT_USGS_Comparison.ipynb b/notebooks/datasets/SWOT_USGS_Comparison.ipynb index 2073a26f..ff093168 100644 --- a/notebooks/datasets/SWOT_USGS_Comparison.ipynb +++ b/notebooks/datasets/SWOT_USGS_Comparison.ipynb @@ -1,12 +1,22 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](https://img.shields.io/badge/Community-Contribution-%20?color=grey&labelColor=yellow)\n", + "\n", + "> From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow [this link](https://github.com/podaac/tutorials/blob/master/notebooks/datasets/SWOT_USGS_Comparison.ipynb). " + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compare SWOT water surface elevation with USGS gage heights\n", + "### Datum Transformation Tutorial\n", "\n", - "#### *Author: Katie McQuillan and George Allen, Virginia Tech*\n", + "#### *Authors: Katie McQuillan and George Allen, Virginia Tech*\n", "\n", "## Summary \n", "This notebook showcases how to transform the horizontal and vertical coordinates of USGS gage heights to match SWOT LakeSP water surface elevation using GDAL. \n", @@ -20,17 +30,12 @@ "\n", "- GDAL must be installed (https://gdal.org/)\n", "\n", - "### 2. Earthdata Login\n", - "\n", - "An 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. \n", - "\n", - "\n", "### Import libraries" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -53,9 +58,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "1. USGS Lake/Reservoir Water Surface Elevation dataset can be acccessed using the DataRetrieval python module with the parameter code 62615. \n", + "1. USGS Lake/Reservoir Water Surface Elevation dataset can be acccessed using the [DataRetrieval](https://github.com/DOI-USGS/dataretrieval-python) python module with the parameter code 62615. \n", + "\n", + "2. SWOT LakeSP dataset can be accessed using the [Earthaccess](https://earthaccess.readthedocs.io/en/latest/) python module or the [PO.DAAC Data Downloader](https://podaac.github.io/tutorials/quarto_text/DataSubscriberDownloader.html). \n", "\n", - "2. SWOT LakeSP dataset can be accessed using the EarthAccess python module or the PO.DAAC Data Downloader. \n", + "### Datum Transformation to Compare with SWOT Data\n", "\n", "Cotemporal SWOT LakeSP and USGS observations were used to form the dataset used for comparisons including X lakes with gages. To directly compare SWOT and USGS datasets, the USGS horizontal and vertical coordiantes must be transformed to match the SWOT datums. Datums for each dataset are noted in Table 1. \n", "\n", @@ -69,6 +76,7 @@ "metadata": {}, "source": [ "Table 1. SWOT and USGS datum information\n", + "\n", "| Data source | Horizontal Datum | Reference Ellipsoid | Vertical Datum | EPSG Code |\n", "| --- | --- | --- | --- | --- |\n", "| SWOT | ITRF2014 | WGS84 | EGM2008 | EPSG:9057+EPSG:3855 |\n", @@ -113,14 +121,6 @@ " AUTHORITY[\"EPSG\",\"5703\"]],\n", " AUTHORITY[\"EPSG\",\"6349\"]]\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\kmcquil\\anaconda3\\envs\\swot-wind\\lib\\site-packages\\osgeo\\osr.py:410: FutureWarning: Neither osr.UseExceptions() nor osr.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -210,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -219,16 +219,6 @@ "text": [ "The number of cotemporal USGS and SWOT observations = 425\n" ] - }, - { - "data": { - "text/plain": [ - "CompletedProcess(args='cd c:\\\\Users\\\\kmcquil\\\\Documents\\\\tutorials\\\\notebooks\\\\resources && gdaltransform -s_coord_epoch \"2010.0\" -t_coord_epoch \"2010.0\" -s_srs \"EPSG:6349\" -t_srs \"EPSG:9057+EPSG:3855\" < \"gdal_in.txt\" > \"gdal_out.txt\"', returncode=0)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -236,7 +226,7 @@ "os.chdir('.')\n", "\n", "# Open the dataset of cotemporal SWOT and USGS observations\n", - "swot_usgs_df = pd.read_csv(\"notebooks/resources/usgs_swot_merged_example.csv\", index_col=0)\n", + "swot_usgs_df = pd.read_csv(\"../resources/usgs_swot_merged_example.csv\", index_col=0)\n", "\n", "# How many cotemporal observations? \n", "print('The number of cotemporal USGS and SWOT observations = ' + str(swot_usgs_df.shape[0]))\n", @@ -258,7 +248,7 @@ "]\n", "\n", "# Save the combined column to a text file\n", - "in_gdal[\"out\"].to_csv(\"notebooks/resources/gdal_in.txt\", header=False, index=False)" + "in_gdal[\"out\"].to_csv(\"../resources/gdal_in.txt\", header=False, index=False)" ] }, { @@ -270,12 +260,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "CompletedProcess(args='cd /home/jovyan/tutorials/notebooks//resources && gdaltransform -s_coord_epoch \"2010.0\" -t_coord_epoch \"2010.0\" -s_srs \"EPSG:6349\" -t_srs \"EPSG:9057+EPSG:3855\" < \"gdal_in.txt\" > \"gdal_out.txt\"', returncode=0)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Write the gdal command and run in the shell \n", - "cd_command = \"cd \" + os.getcwd() + \"\\\\notebooks\\\\resources && \"\n", + "os.chdir('../')\n", + "cd_command = \"cd \" + os.getcwd() + \"//resources && \"\n", "gdal_command = 'gdaltransform -s_coord_epoch \"2010.0\" -t_coord_epoch \"2010.0\" -s_srs \"EPSG:6349\" -t_srs \"EPSG:9057+EPSG:3855\" < \"gdal_in.txt\" > \"gdal_out.txt\"'\n", "subprocess.run(cd_command + gdal_command, shell=True)\n" ] @@ -284,12 +286,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Calculate error statistics" + "#### Calculate error statistics between USGS datum-transformed data and SWOT data" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -303,7 +305,7 @@ ], "source": [ "# Merge back with the original data \n", - "out_gdal = pd.read_csv(\"notebooks/resources/gdal_out.txt\", header=None)\n", + "out_gdal = pd.read_csv(\"resources/gdal_out.txt\", header=None)\n", "out_gdal = out_gdal.rename(columns={0: \"result\"})\n", "out_gdal[[\"usgs_long\", \"usgs_lat\", \"usgs_X_62615_00000_egm0_meters\"]] = out_gdal[\"result\"].str.split(\" \", expand=True)\n", "swot_usgs_df[\"usgs_X_62615_00000_egm0_meters\"] = out_gdal[\"usgs_X_62615_00000_egm0_meters\"].astype(float)\n", @@ -320,7 +322,7 @@ ], "metadata": { "kernelspec": { - "display_name": "swot-wind", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -334,9 +336,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.14" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/quarto_text/SWOT.qmd b/quarto_text/SWOT.qmd index a5abde11..eba1d7e3 100644 --- a/quarto_text/SWOT.qmd +++ b/quarto_text/SWOT.qmd @@ -1,6 +1,17 @@ --- title: SWOT subtitle: SWOT Data Tutorials +jupyter: + jupytext: + text_representation: + extension: .qmd + format_name: quarto + format_version: '1.0' + jupytext_version: 1.16.1 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 --- ## SWOT Background @@ -134,6 +145,8 @@ In [Earthdata Search GUI](https://search.earthdata.nasa.gov/search): ### **Transform** +### [Datum Conversion to Compare with SWOT Data](SWOT_Datum.qmd) + #### [HiTIDE subsetter for Sea Surface Height Products](https://hitide.podaac.earthdatacloud.nasa.gov/) - select KaRIn instrument in `sensors`, see video tutorial [here](https://www.youtube.com/watch?v=S8MIHltlwWA) #### [Hydrocron: Time series API](../notebooks/datasets/Hydrocron_SWOT_timeseries_examples.ipynb) - Currently for rivers, see [Hydrocron documentation](https://podaac.github.io/hydrocron/intro.html) and more description under tools below. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11176233.svg)](https://doi.org/10.5281/zenodo.11176233) @@ -216,3 +229,4 @@ This workshop focuses on the SWOT Hydrology datasets including river and lake ve [https://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/](https://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/){.uri} The goal of the workshop was to enable the (oceanography) science team to be ready for processing and handling the large volumes of SWOT SSH data in the cloud. Learning objectives focus on how to access the [simulated SWOT L2 SSH data](https://podaac.jpl.nasa.gov/announcements/2022-01-31-Release-simulated-SWOT-SSH-version1-datasets) from Earthdata Cloud either by downloading or accessing the data on the cloud. + diff --git a/quarto_text/SWOT_Datum.qmd b/quarto_text/SWOT_Datum.qmd new file mode 100644 index 00000000..6fd171d3 --- /dev/null +++ b/quarto_text/SWOT_Datum.qmd @@ -0,0 +1,26 @@ +--- +title: Datum Conversion for SWOT Data +subtitle: Community Tutorials +jupyter: + jupytext: + text_representation: + extension: .qmd + format_name: quarto + format_version: '1.0' + jupytext_version: 1.16.1 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Datum Conversion for SWOT Data + +Please first see the [SWOT Data User Handbook](https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swot_mission_docs/D-109532_SWOT_UserHandbook_20240502.pdf?_ga=2.76536725.1816513343.1724259518-1354658737.1715875596) sections 3.3.3 on the reference ellipsoid and 11.3.1 on the geoid. In addition, section 3.1.23 gives detailed information about Height with section 3.1.24 elaborating on Sea Surface Height Anomalies and 3.1.25 elaborating on Water Surface Elevation over inland waters. + +As a summary, SWOT data is referenced to the WGS84 reference ellipsoid as a first step, and in a second step referenced to the EGM2008 geoid that references WGS84. To get a direct conversion with another dataset, it is easiest to transform all local data to the EGM2008 geoid. We recognize this conversion is different globally and depending on the data being used to compare, but we have collected a few community examples of direct comparisons to share below: + +1. Comparing USGS gage data and SWOT data in the United States - [Python Notebook](../notebooks/datasets/SWOT_USGS_Comparison.ipynb) +2. Converting the SWOT Frame and Geoid in Canada - [Google Colab Section from Canadian Colleagues](https://colab.research.google.com/drive/1we0a24gBKbs-CFMVBfRIUm4SSW8RdwmA#scrollTo=cu09EYYRxxd4) + +If you have any questions for your specific region, please ask in the [Earthdata Forum](https://forum.earthdata.nasa.gov/viewforum.php?f=7) and experts should be able to help. From 1631f0f7ef9761811f9ace8d7c6a824597e93475 Mon Sep 17 00:00:00 2001 From: cassienickles Date: Thu, 22 Aug 2024 16:05:52 -0700 Subject: [PATCH 3/4] Update Cookbook Content Revise notebook and landing page --- notebooks/datasets/SWOT_USGS_Comparison.ipynb | 13 ++++++----- quarto_text/SWOT_Datum.qmd | 22 +++++++------------ 2 files changed, 15 insertions(+), 20 deletions(-) diff --git a/notebooks/datasets/SWOT_USGS_Comparison.ipynb b/notebooks/datasets/SWOT_USGS_Comparison.ipynb index ff093168..4044afea 100644 --- a/notebooks/datasets/SWOT_USGS_Comparison.ipynb +++ b/notebooks/datasets/SWOT_USGS_Comparison.ipynb @@ -23,12 +23,11 @@ "\n", "## Requirements\n", "\n", - "### 1. Compute environment \n", + "### Compute environment:\n", "\n", "This tutorial is written to run in the following environment:\n", "- **Local compute environment** e.g. laptop, server: this tutorial can be run on your local machine\n", - "\n", - "- GDAL must be installed (https://gdal.org/)\n", + "- **GDAL** must be installed (https://gdal.org/)\n", "\n", "### Import libraries" ] @@ -58,9 +57,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "1. USGS Lake/Reservoir Water Surface Elevation dataset can be acccessed using the [DataRetrieval](https://github.com/DOI-USGS/dataretrieval-python) python module with the parameter code 62615. \n", + "**1. USGS Lake/Reservoir Water Surface Elevation dataset** can be acccessed using the [DataRetrieval](https://github.com/DOI-USGS/dataretrieval-python) python module with the parameter code 62615. \n", + "\n", + "**2. SWOT LakeSP dataset** can be accessed using the [Earthaccess](https://earthaccess.readthedocs.io/en/latest/) python module or the [PO.DAAC Data Downloader](https://podaac.github.io/tutorials/quarto_text/DataSubscriberDownloader.html). \n", "\n", - "2. SWOT LakeSP dataset can be accessed using the [Earthaccess](https://earthaccess.readthedocs.io/en/latest/) python module or the [PO.DAAC Data Downloader](https://podaac.github.io/tutorials/quarto_text/DataSubscriberDownloader.html). \n", + "The above data were combined outside of this tutorial into a csv file called 'usgs_swot_merged_example.csv'.\n", "\n", "### Datum Transformation to Compare with SWOT Data\n", "\n", @@ -336,7 +337,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.8.13" } }, "nbformat": 4, diff --git a/quarto_text/SWOT_Datum.qmd b/quarto_text/SWOT_Datum.qmd index 6fd171d3..110d3705 100644 --- a/quarto_text/SWOT_Datum.qmd +++ b/quarto_text/SWOT_Datum.qmd @@ -1,26 +1,20 @@ --- title: Datum Conversion for SWOT Data subtitle: Community Tutorials -jupyter: - jupytext: - text_representation: - extension: .qmd - format_name: quarto - format_version: '1.0' - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 --- -# Datum Conversion for SWOT Data +### Use the Handbook Please first see the [SWOT Data User Handbook](https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swot_mission_docs/D-109532_SWOT_UserHandbook_20240502.pdf?_ga=2.76536725.1816513343.1724259518-1354658737.1715875596) sections 3.3.3 on the reference ellipsoid and 11.3.1 on the geoid. In addition, section 3.1.23 gives detailed information about Height with section 3.1.24 elaborating on Sea Surface Height Anomalies and 3.1.25 elaborating on Water Surface Elevation over inland waters. +## Community Tutorials + As a summary, SWOT data is referenced to the WGS84 reference ellipsoid as a first step, and in a second step referenced to the EGM2008 geoid that references WGS84. To get a direct conversion with another dataset, it is easiest to transform all local data to the EGM2008 geoid. We recognize this conversion is different globally and depending on the data being used to compare, but we have collected a few community examples of direct comparisons to share below: -1. Comparing USGS gage data and SWOT data in the United States - [Python Notebook](../notebooks/datasets/SWOT_USGS_Comparison.ipynb) -2. Converting the SWOT Frame and Geoid in Canada - [Google Colab Section from Canadian Colleagues](https://colab.research.google.com/drive/1we0a24gBKbs-CFMVBfRIUm4SSW8RdwmA#scrollTo=cu09EYYRxxd4) +#### 1. Comparing **USGS gage** data and SWOT data in the **United States** - [Python Notebook](../notebooks/datasets/SWOT_USGS_Comparison.ipynb) + +#### 2. Converting the SWOT Frame and Geoid in **Canada** - [Google Colab Section from Canadian Colleagues](https://colab.research.google.com/drive/1we0a24gBKbs-CFMVBfRIUm4SSW8RdwmA#scrollTo=cu09EYYRxxd4) + +### For More Help If you have any questions for your specific region, please ask in the [Earthdata Forum](https://forum.earthdata.nasa.gov/viewforum.php?f=7) and experts should be able to help. From 887b7c5505bcc82a340e2dbc0b618fc99d000f6e Mon Sep 17 00:00:00 2001 From: cassienickles Date: Thu, 22 Aug 2024 16:12:46 -0700 Subject: [PATCH 4/4] Update messaging for SWOT landing page --- quarto_text/SWOT.qmd | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/quarto_text/SWOT.qmd b/quarto_text/SWOT.qmd index eba1d7e3..cea85731 100644 --- a/quarto_text/SWOT.qmd +++ b/quarto_text/SWOT.qmd @@ -1,17 +1,6 @@ --- title: SWOT subtitle: SWOT Data Tutorials -jupyter: - jupytext: - text_representation: - extension: .qmd - format_name: quarto - format_version: '1.0' - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 --- ## SWOT Background @@ -145,7 +134,7 @@ In [Earthdata Search GUI](https://search.earthdata.nasa.gov/search): ### **Transform** -### [Datum Conversion to Compare with SWOT Data](SWOT_Datum.qmd) +#### [Datum Conversion](SWOT_Datum.qmd) - transforming to or from EGM2008/WGS84 to compare other data with SWOT #### [HiTIDE subsetter for Sea Surface Height Products](https://hitide.podaac.earthdatacloud.nasa.gov/) - select KaRIn instrument in `sensors`, see video tutorial [here](https://www.youtube.com/watch?v=S8MIHltlwWA)