Power for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).
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power_minus_y
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
power for minus_y channel
units :
1
valid_min :
0.0
valid_max :
999999.0
comment :
Power for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).
\n",
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coherent_power
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
coherent power combination of minus_y and plus_y channels
units :
1
valid_min :
0.0
valid_max :
999999.0
comment :
Power 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).
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x_factor_plus_y
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
X factor for plus_y channel power
units :
1
valid_min :
0.0
valid_max :
999999.0
comment :
X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).
\n",
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float32 numpy.ndarray
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x_factor_minus_y
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
X factor for minus_y channel power
units :
1
valid_min :
0.0
valid_max :
999999.0
comment :
X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).
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float32 numpy.ndarray
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water_frac
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
water fraction
units :
1
valid_min :
-1000.0
valid_max :
10000.0
comment :
Noisy estimate of the fraction of the pixel that is water.
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water_frac_uncert
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
water fraction uncertainty
units :
1
valid_min :
0.0
valid_max :
999999.0
comment :
Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).
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Data type
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float32 numpy.ndarray
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classification
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
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 :
Flags indicating water detection results.
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false_detection_rate
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
false detection rate
units :
1
valid_min :
0.0
valid_max :
1.0
comment :
Probability of falsely detecting water when there is none.
\n",
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missed_detection_rate
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
missed detection rate
units :
1
valid_min :
0.0
valid_max :
1.0
comment :
Probability of falsely detecting no water when there is water.
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prior_water_prob
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
prior water probability
units :
1
valid_min :
0.0
valid_max :
1.0
comment :
Prior probability of water occurring.
\n",
+ "
\n",
+ "
\n",
+ "
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+ " \n",
+ "
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Data type
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float32 numpy.ndarray
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bright_land_flag
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
bright land flag
standard_name :
status_flag
flag_meanings :
not_bright_land bright_land bright_land_or_water
flag_values :
[0 1 2]
valid_min :
0
valid_max :
2
comment :
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.
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float32 numpy.ndarray
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layover_impact
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
layover impact
units :
m
valid_min :
-999999.0
valid_max :
999999.0
comment :
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.
\n",
+ "
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1 chunks in 2 graph layers
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Data type
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float32 numpy.ndarray
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eff_num_rare_looks
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
effective number of rare looks
units :
1
valid_min :
0.0
valid_max :
999999.0
comment :
Effective number of independent looks taken to form the rare interferogram.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
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Array
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1.87 MiB
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(489673,)
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+ "
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\n",
+ "
1 chunks in 2 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
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+ " \n",
+ "
\n",
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\n",
+ " \n",
+ "
\n",
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\n",
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height
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
height above reference ellipsoid
units :
m
valid_min :
-1500.0
valid_max :
15000.0
comment :
Height of the pixel above the reference ellipsoid.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
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Array
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1.87 MiB
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1 chunks in 2 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
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+ " \n",
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+ " \n",
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\n",
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cross_track
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
approximate cross-track location
units :
m
valid_min :
-75000.0
valid_max :
75000.0
comment :
Approximate cross-track location of the pixel.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
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1.87 MiB
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(489673,)
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\n",
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1 chunks in 2 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
pixel_area
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
pixel area
units :
m^2
valid_min :
0.0
valid_max :
999999.0
comment :
Pixel area.
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float32 numpy.ndarray
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+ " \n",
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+ "
inc
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
incidence angle
units :
degrees
valid_min :
0.0
valid_max :
999999.0
comment :
Incidence angle.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
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float32 numpy.ndarray
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phase_noise_std
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
phase noise standard deviation
units :
radians
valid_min :
-999999.0
valid_max :
999999.0
comment :
Estimate of the phase noise standard deviation.
\n",
+ "
\n",
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+ "
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+ " \n",
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Data type
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float32 numpy.ndarray
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dlatitude_dphase
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sensitivity of latitude estimate to interferogram phase
units :
degrees/radian
valid_min :
-999999.0
valid_max :
999999.0
comment :
Sensitivity of the latitude estimate to the interferogram phase.
\n",
+ "
\n",
+ "
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+ "
\n",
+ " \n",
+ "
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float32 numpy.ndarray
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+ "
dlongitude_dphase
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sensitivity of longitude estimate to interferogram phase
units :
degrees/radian
valid_min :
-999999.0
valid_max :
999999.0
comment :
Sensitivity of the longitude estimate to the interferogram phase.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
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+ "
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float32 numpy.ndarray
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+ "
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dheight_dphase
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sensitivity of height estimate to interferogram phase
units :
m/radian
valid_min :
-999999.0
valid_max :
999999.0
comment :
Sensitivity of the height estimate to the interferogram phase.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
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1.87 MiB
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dheight_droll
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sensitivity of height estimate to spacecraft roll
units :
m/degrees
valid_min :
-999999.0
valid_max :
999999.0
comment :
Sensitivity of the height estimate to the spacecraft roll.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
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dheight_dbaseline
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sensitivity of height estimate to interferometric baseline
units :
m/m
valid_min :
-999999.0
valid_max :
999999.0
comment :
Sensitivity of the height estimate to the interferometric baseline.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
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Data type
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dheight_drange
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sensitivity of height estimate to range (delay)
units :
m/m
valid_min :
-999999.0
valid_max :
999999.0
comment :
Sensitivity of the height estimate to the range (delay).
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
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Data type
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darea_dheight
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sensitivity of pixel area to reference height
units :
m^2/m
valid_min :
-999999.0
valid_max :
999999.0
comment :
Sensitivity of the pixel area to the reference height.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
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float32 numpy.ndarray
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\n",
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illumination_time
(points)
datetime64[ns]
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
time of illumination of each pixel (UTC)
standard_name :
time
tai_utc_difference :
[Value of TAI-UTC at time of first record]
leap_second :
YYYY-MM-DD hh:mm:ss
comment :
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.
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3.74 MiB
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datetime64[ns] numpy.ndarray
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+ " \n",
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illumination_time_tai
(points)
datetime64[ns]
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
time of illumination of each pixel (TAI)
standard_name :
time
comment :
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].
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+ "
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+ " \n",
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+ "
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+ " \n",
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eff_num_medium_looks
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
effective number of medium looks
units :
1
valid_min :
0.0
valid_max :
999999.0
comment :
Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
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float32 numpy.ndarray
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+ " \n",
+ "
\n",
+ "
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+ " \n",
+ "
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+ "
\n",
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sig0
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
sigma0
units :
1
valid_min :
-999999.0
valid_max :
999999.0
comment :
Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
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1.87 MiB
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+ "
1 chunks in 2 graph layers
\n",
+ "
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+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
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+ " \n",
+ "
\n",
+ "
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+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
phase_unwrapping_region
(points)
float64
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
phase unwrapping region index
units :
1
valid_min :
-1
valid_max :
99999999
comment :
Phase unwrapping region index.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
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3.74 MiB
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3.74 MiB
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(489673,)
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+ "
(489673,)
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+ "
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\n",
+ "
1 chunks in 2 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float64 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
instrument_range_cor
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
instrument range correction
units :
m
valid_min :
-999999.0
valid_max :
999999.0
comment :
Term that incorporates all calibration corrections applied to range before geolocation.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
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\n",
+ "
\n",
+ " \n",
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1.87 MiB
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1.87 MiB
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(489673,)
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(489673,)
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+ "
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\n",
+ "
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\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
instrument_phase_cor
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
instrument phase correction
units :
radians
valid_min :
-999999.0
valid_max :
999999.0
comment :
Term that incorporates all calibration corrections applied to phase before geolocation.
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instrument_baseline_cor
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
instrument baseline correction
units :
m
valid_min :
-999999.0
valid_max :
999999.0
comment :
Term that incorporates all calibration corrections applied to baseline before geolocation.
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instrument_attitude_cor
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
instrument attitude correction
units :
degrees
valid_min :
-999999.0
valid_max :
999999.0
comment :
Term that incorporates all calibration corrections applied to attitude before geolocation.
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model_dry_tropo_cor
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
dry troposphere vertical correction
source :
European Centre for Medium-Range Weather Forecasts
institution :
ECMWF
units :
m
valid_min :
-3.0
valid_max :
-1.5
comment :
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.
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model_wet_tropo_cor
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
wet troposphere vertical correction
source :
European Centre for Medium-Range Weather Forecasts
institution :
ECMWF
units :
m
valid_min :
-1.0
valid_max :
0.0
comment :
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.
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iono_cor_gim_ka
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
ionosphere vertical correction
source :
Global Ionosphere Maps
institution :
JPL
units :
m
valid_min :
-0.5
valid_max :
0.0
comment :
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.
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height_cor_xover
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
height correction from KaRIn crossovers
units :
m
valid_min :
-10.0
valid_max :
10.0
comment :
Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.
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geoid
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
geoid height
standard_name :
geoid_height_above_reference_ellipsoid
source :
EGM2008 (Pavlis et al., 2012)
units :
m
valid_min :
-150.0
valid_max :
150.0
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).
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solid_earth_tide
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
solid Earth tide height
source :
Cartwright and Taylor (1971) and Cartwright and Edden (1973)
units :
m
valid_min :
-1.0
valid_max :
1.0
comment :
Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.
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load_tide_fes
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
geocentric load tide height (FES)
source :
FES2014b (Carrere et al., 2016)
institution :
LEGOS/CNES
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.
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load_tide_got
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
geocentric load tide height (GOT)
source :
GOT4.10c (Ray, 2013)
institution :
GSFC
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.
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pole_tide
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
geocentric pole tide height
source :
Wahr (1985) and Desai et al. (2015)
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).
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ancillary_surface_classification_flag
(points)
float32
dask.array<chunksize=(489673,), meta=np.ndarray>
long_name :
surface classification
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]
valid_min :
0
valid_max :
6
comment :
7-state surface type classification computed from a mask built with MODIS and GlobCover data.
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.
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.
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.
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ice_clim_f
(points)
int8
dask.array<chunksize=(489673,), meta=np.ndarray>
_FillValue :
127
long_name :
climatological ice cover flag
flag_meanings :
no_ice_cover partial_ice_cover full_ice_cover
flag_values :
[0 1 2]
institution :
University of North Carolina
coordinates :
longitude_vectorproc latitude_vectorproc
comment :
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.
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ice_dyn_f
(points)
int8
dask.array<chunksize=(489673,), meta=np.ndarray>
_FillValue :
127
long_name :
dynamical ice cover flag
flag_meanings :
no_ice_cover partial_ice_cover full_ice_cover
flag_values :
[0 1 2]
institution :
University of North Carolina
coordinates :
longitude_vectorproc latitude_vectorproc
comment :
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.
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Conventions :
CF-1.7
title :
Level 2 KaRIn high rate pixel cloud vector attribute product
Water 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).
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.
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.
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].
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.
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.
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).
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.
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).
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.
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.
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.