SWOT Hydrology Dataset Exploration on a local machine¶
Accessing and Visualizing SWOT Datasets¶
Requirement:¶
Local compute environment e.g. laptop, server: this tutorial can be run on your local machine.
Learning Objectives:¶
- Access SWOT HR data products (archived in NASA Earthdata Cloud) by downloading to local machine
- Visualize accessed data for a quick check
SWOT Level 2 KaRIn High Rate Version 2.0 Datasets:¶
River Vector Shapefile - SWOT_L2_HR_RIVERSP_2.0
Lake Vector Shapefile - SWOT_L2_HR_LAKESP_2.0
Water Mask Pixel Cloud NetCDF - SWOT_L2_HR_PIXC_2.0
Water Mask Pixel Cloud Vector Attribute NetCDF - SWOT_L2_HR_PIXCVec_2.0
Raster NetCDF - SWOT_L2_HR_Raster_2.0
Notebook Author: Cassie Nickles, NASA PO.DAAC (Feb 2024) || Other Contributors: Zoe Walschots (PO.DAAC Summer Intern 2023), Catalina Taglialatela (NASA PO.DAAC), Luis Lopez (NASA NSIDC DAAC), Brent Williams (NASA JPL)
Last updated: 9 July 2024
Libraries Needed¶
In [39]:
import glob
import h5netcdf
import xarray as xr
import pandas as pd
import geopandas as gpd
import contextily as cx
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import zipfile
import earthaccess
Earthdata Login¶
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. If you don't already have one, 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.
In [40]:
auth = earthaccess.login()
Single File Access¶
1. River Vector Shapefiles¶
The https 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.
Alternatively, Earthdata Search (see tutorial) can be used to manually search in a GUI interface.
For additional tips on spatial searching of SWOT HR L2 data, see also PO.DAAC Cookbook - SWOT Chapter tips section.
Search for the data of interest¶
In [41]:
#Retrieves granule from the day we want, in this case by passing to `earthaccess.search_data` function the data collection shortname, temporal bounds, and filter by wildcards
river_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_RIVERSP_2.0',
temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time
granule_name = '*Reach*_287_NA*') # here we filter by Reach files (not node), pass=287, continent code=NA
Granules found: 8
Dowload, unzip, read the data¶
Let's download the first data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass.
In [42]:
earthaccess.download([river_results[0]], "./data_downloads")
Getting 1 granules, approx download size: 0.01 GB
QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 62.49it/s]
File SWOT_L2_HR_RiverSP_Reach_010_287_NA_20240204T060400_20240204T060409_PIC0_01.zip already downloaded
PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 996.51it/s] COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]
Out[42]:
['data_downloads\\SWOT_L2_HR_RiverSP_Reach_010_287_NA_20240204T060400_20240204T060409_PIC0_01.zip']
The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we must first extract the data to open it. First, we'll programmatically get the filename we just downloaded, and then extract all data to the data_downloads folder.
In [43]:
filename = earthaccess.results.DataGranule.data_links(river_results[0], access='external')
filename = filename[0].split("/")[-1]
filename
Out[43]:
'SWOT_L2_HR_RiverSP_Reach_010_287_NA_20240204T060400_20240204T060409_PIC0_01.zip'
In [44]:
with zipfile.ZipFile(f'data_downloads/{filename}', 'r') as zip_ref:
zip_ref.extractall('data_downloads')
Open the shapefile using geopandas
In [45]:
filename_shp = filename.replace('.zip','.shp')
In [46]:
SWOT_HR_shp1 = gpd.read_file(f'data_downloads/{filename_shp}')
#view the attribute table
SWOT_HR_shp1
Out[46]:
| reach_id | time | time_tai | time_str | p_lat | p_lon | river_name | wse | wse_u | wse_r_u | ... | p_wid_var | p_n_nodes | p_dist_out | p_length | p_maf | p_dam_id | p_n_ch_max | p_n_ch_mod | p_low_slp | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 71224800093 | -1.000000e+12 | -1.000000e+12 | no_data | 48.724265 | -92.406254 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | ... | 232341.227 | 90 | 47778.423 | 18013.132474 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-92.51093 48.70847, -92.51052 48.7... |
| 1 | 71224800101 | -1.000000e+12 | -1.000000e+12 | no_data | 48.739159 | -92.290054 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | ... | 767.700 | 6 | 48958.712 | 1180.288364 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-92.29723 48.73905, -92.29682 48.7... |
| 2 | 71224800114 | -1.000000e+12 | -1.000000e+12 | no_data | 48.743344 | -92.283320 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | ... | 2911.208 | 3 | 49549.648 | 590.936467 | -1.000000e+12 | 23000 | 2 | 1 | 0 | LINESTRING (-92.28569 48.74125, -92.28495 48.7... |
| 3 | 71224800123 | 7.603424e+08 | 7.603424e+08 | 2024-02-04T06:13:10Z | 48.751442 | -92.242669 | no_data | 3.585147e+02 | 2.006910e+00 | 2.004890e+00 | ... | 57688.777 | 31 | 55684.066 | 6134.417666 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-92.28196 48.74559, -92.28163 48.7... |
| 4 | 71224800133 | 7.603424e+08 | 7.603424e+08 | 2024-02-04T06:13:10Z | 48.762334 | -92.189341 | no_data | 3.579681e+02 | 1.451600e-01 | 1.138900e-01 | ... | 20821.463 | 13 | 58222.719 | 2538.653439 | -1.000000e+12 | 0 | 3 | 1 | 0 | LINESTRING (-92.20553 48.75837, -92.20512 48.7... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1058 | 77127000061 | 7.603418e+08 | 7.603419e+08 | 2024-02-04T06:04:09Z | 18.050684 | -98.761645 | no_data | 6.529558e+02 | 1.000896e+02 | 1.000896e+02 | ... | 784.041 | 67 | 667747.660 | 13493.202300 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-98.81280 18.06539, -98.81280 18.0... |
| 1059 | 77127000071 | -1.000000e+12 | -1.000000e+12 | no_data | 17.981704 | -98.686712 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | ... | 824.145 | 97 | 687123.984 | 19376.324005 | -1.000000e+12 | 0 | 3 | 1 | 0 | LINESTRING (-98.71239 18.03246, -98.71239 18.0... |
| 1060 | 77127000131 | 7.603418e+08 | 7.603419e+08 | 2024-02-04T06:04:09Z | 18.102586 | -98.771552 | no_data | 6.576003e+02 | 1.240586e+02 | 1.240586e+02 | ... | 281.012 | 77 | 683164.834 | 15417.173639 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-98.81280 18.06539, -98.81280 18.0... |
| 1061 | 77127000141 | -1.000000e+12 | -1.000000e+12 | no_data | 18.094132 | -98.694466 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | ... | 414.760 | 54 | 693896.634 | 10731.799933 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-98.71770 18.11625, -98.71764 18.1... |
| 1062 | 77127000151 | -1.000000e+12 | -1.000000e+12 | no_data | 18.097046 | -98.657280 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | ... | 436.883 | 54 | 704624.208 | 10727.574606 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-98.66628 18.07224, -98.66611 18.0... |
1063 rows × 127 columns
Quickly plot the SWOT river data¶
In [9]:
# Simple plot
fig, ax = plt.subplots(figsize=(7,5))
SWOT_HR_shp1.plot(ax=ax, color='black')
cx.add_basemap(ax, crs=SWOT_HR_shp1.crs, source=cx.providers.OpenTopoMap)
In [ ]:
# Another way to plot geopandas dataframes is with `explore`, which also plots a basemap
SWOT_HR_shp1.explore()
2. Lake Vector Shapefiles¶
The lake vector shapefiles can be accessed in the same way as the river shapefiles above.
For additional tips on spatial searching of SWOT HR L2 data, see also PO.DAAC Cookbook - SWOT Chapter tips section.
Search for data of interest¶
In [48]:
lake_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_LAKESP_2.0',
temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time
granule_name = '*Prior*_287_NA*') # here we filter by files with 'Prior' in the name (This collection has three options: Obs, Unassigned, and Prior), pass 287 and continent code=NA
Granules found: 8
Let's download the first data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass.
In [49]:
earthaccess.download([lake_results[0]], "./data_downloads")
Getting 1 granules, approx download size: 0.07 GB
QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 199.66it/s]
File SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.zip already downloaded
PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 986.43it/s] COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]
Out[49]:
['data_downloads\\SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.zip']
The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we must first extract the data to open it. First, we'll programmatically get the filename we just downloaded, and then extract all data to the SWOT_downloads folder.
In [50]:
filename2 = earthaccess.results.DataGranule.data_links(lake_results[0], access='external')
filename2 = filename2[0].split("/")[-1]
filename2
Out[50]:
'SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.zip'
In [51]:
with zipfile.ZipFile(f'data_downloads/{filename2}', 'r') as zip_ref:
zip_ref.extractall('data_downloads')
Open the shapefile using geopandas
In [52]:
filename_shp2 = filename2.replace('.zip','.shp')
filename_shp2
Out[52]:
'SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.shp'
In [53]:
SWOT_HR_shp2 = gpd.read_file(f'data_downloads/{filename_shp2}')
#view the attribute table
SWOT_HR_shp2
Out[53]:
| lake_id | reach_id | obs_id | overlap | n_overlap | time | time_tai | time_str | wse | wse_u | ... | lake_name | p_res_id | p_lon | p_lat | p_ref_wse | p_ref_area | p_date_t0 | p_ds_t0 | p_storage | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7120822822 | no_data | 712239R999998 | 99 | 1 | 7.603424e+08 | 7.603424e+08 | 2024-02-04T06:13:08Z | 5.281870e+02 | 1.500000e-02 | ... | no_data | -99999999 | -91.557528 | 47.616292 | -1.000000e+12 | 1.038600 | no_data | -1.000000e+12 | -1.000000e+12 | MULTIPOLYGON (((-91.56583 47.61200, -91.56589 ... |
| 1 | 7120822902 | no_data | no_data | no_data | no_data | -1.000000e+12 | -1.000000e+12 | no_data | -1.000000e+12 | -1.000000e+12 | ... | no_data | -99999999 | -91.623241 | 47.756499 | -1.000000e+12 | 0.113400 | no_data | -1.000000e+12 | -1.000000e+12 | None |
| 2 | 7120822932 | no_data | no_data | no_data | no_data | -1.000000e+12 | -1.000000e+12 | no_data | -1.000000e+12 | -1.000000e+12 | ... | TONY LAKE | -99999999 | -91.635242 | 47.726123 | -1.000000e+12 | 0.017100 | no_data | -1.000000e+12 | -1.000000e+12 | None |
| 3 | 7120822982 | no_data | no_data | no_data | no_data | -1.000000e+12 | -1.000000e+12 | no_data | -1.000000e+12 | -1.000000e+12 | ... | no_data | -99999999 | -91.665522 | 47.705366 | -1.000000e+12 | 0.026100 | no_data | -1.000000e+12 | -1.000000e+12 | None |
| 4 | 7120823182 | no_data | no_data | no_data | no_data | -1.000000e+12 | -1.000000e+12 | no_data | -1.000000e+12 | -1.000000e+12 | ... | HEART LAKE | -99999999 | -91.651807 | 47.769148 | -1.000000e+12 | 0.124200 | no_data | -1.000000e+12 | -1.000000e+12 | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 65495 | 7130133552 | no_data | 713244L999972;713244L000002 | 73;2 | 2 | 7.603424e+08 | 7.603424e+08 | 2024-02-04T06:14:01Z | 3.926960e+02 | 5.900000e-02 | ... | no_data | -99999999 | -90.889026 | 50.669027 | -1.000000e+12 | 0.695690 | no_data | -1.000000e+12 | -1.000000e+12 | POLYGON ((-90.89210 50.67709, -90.89185 50.677... |
| 65496 | 7130141612 | no_data | 713245L999974;713245L000001 | 31;5 | 2 | 7.603424e+08 | 7.603425e+08 | 2024-02-04T06:14:08Z | 3.741850e+02 | 8.200000e-02 | ... | LAKE ST JOSEPH;ST JOSEPH | -99999999 | -90.750682 | 51.061383 | -1.000000e+12 | 0.256500 | no_data | -1.000000e+12 | -1.000000e+12 | MULTIPOLYGON (((-90.75251 51.06305, -90.75228 ... |
| 65497 | 7420206383 | 74226000013;74227100043;74227100013;7422710006... | 742214L000175;742214L999934;742214L000500;7422... | 64;23;0;0 | 4 | 7.603421e+08 | 7.603422e+08 | 2024-02-04T06:09:01Z | 1.875740e+02 | 1.000000e-03 | ... | LAKE TEXOMA | 1135 | -96.688976 | 33.901142 | -1.000000e+12 | 257.028517 | no_data | -1.000000e+12 | -1.000000e+12 | MULTIPOLYGON (((-96.70899 33.82534, -96.70885 ... |
| 65498 | 7420280413 | 74246000423;74246000413;74246000404 | 742218L999996;742218L001654;742219L999885 | 4;0;0 | 3 | 7.603422e+08 | 7.603422e+08 | 2024-02-04T06:09:48Z | 1.941800e+02 | 2.100000e-02 | ... | OOLAGAHL LAKE;OOLOGAH LAKE | 1032 | -95.593848 | 36.550604 | -1.000000e+12 | 123.796498 | no_data | -1.000000e+12 | -1.000000e+12 | MULTIPOLYGON (((-95.67217 36.43803, -95.67157 ... |
| 65499 | 7710056183 | 77125000273;77125000263;77125000283;7712500030... | 771186L999995;771186L000013;771186L999993 | 12;1;0 | 3 | 7.603419e+08 | 7.603419e+08 | 2024-02-04T06:04:22Z | 4.586500e+02 | 3.400000e-02 | ... | PRESA EL CARACOL | 1384 | -99.861530 | 17.975030 | -1.000000e+12 | 35.410155 | no_data | -1.000000e+12 | -1.000000e+12 | MULTIPOLYGON (((-99.75753 18.02115, -99.75739 ... |
65500 rows × 51 columns
Quickly plot the SWOT lakes data¶
In [17]:
fig, ax = plt.subplots(figsize=(7,5))
SWOT_HR_shp2.plot(ax=ax, color='black')
cx.add_basemap(ax, crs=SWOT_HR_shp2.crs, source=cx.providers.OpenTopoMap)
Accessing the remaining files is different than the shp files above. We do not need to extract the shapefiles from a zip file because the following SWOT HR collections are stored in netCDF files in the cloud. For the rest of the products, we will open via xarray, not geopandas.
3. Water Mask Pixel Cloud NetCDF¶
Search for data collection and time of interest¶
In [54]:
pixc_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_PIXC_2.0',
temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time
bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX
Granules found: 229
Let's download one data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass.
In [55]:
earthaccess.download([pixc_results[0]], "./data_downloads")
Getting 1 granules, approx download size: 0.5 GB
QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 333.49it/s]
File SWOT_L2_HR_PIXC_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc already downloaded
PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<?, ?it/s] COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]
Out[55]:
['data_downloads\\SWOT_L2_HR_PIXC_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc']
Open data using xarray¶
The 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.
In [56]:
ds_PIXC = xr.open_mfdataset("data_downloads/SWOT_L2_HR_PIXC_*.nc", group = 'pixel_cloud', engine='h5netcdf')
ds_PIXC
Out[56]:
<xarray.Dataset> Size: 1GB
Dimensions: (points: 5332824, complex_depth: 2,
num_pixc_lines: 3277)
Coordinates:
latitude (points) float64 43MB dask.array<chunksize=(484803,), meta=np.ndarray>
longitude (points) float64 43MB dask.array<chunksize=(484803,), meta=np.ndarray>
Dimensions without coordinates: points, complex_depth, num_pixc_lines
Data variables: (12/61)
azimuth_index (points) float64 43MB dask.array<chunksize=(888804,), meta=np.ndarray>
range_index (points) float64 43MB dask.array<chunksize=(888804,), meta=np.ndarray>
interferogram (points, complex_depth) float32 43MB dask.array<chunksize=(1333206, 1), meta=np.ndarray>
power_plus_y (points) float32 21MB dask.array<chunksize=(888804,), meta=np.ndarray>
power_minus_y (points) float32 21MB dask.array<chunksize=(888804,), meta=np.ndarray>
coherent_power (points) float32 21MB dask.array<chunksize=(888804,), meta=np.ndarray>
... ...
pixc_line_qual (num_pixc_lines) float64 26kB dask.array<chunksize=(3277,), meta=np.ndarray>
pixc_line_to_tvp (num_pixc_lines) float32 13kB dask.array<chunksize=(3277,), meta=np.ndarray>
data_window_first_valid (num_pixc_lines) float64 26kB dask.array<chunksize=(3277,), meta=np.ndarray>
data_window_last_valid (num_pixc_lines) float64 26kB dask.array<chunksize=(3277,), meta=np.ndarray>
data_window_first_cross_track (num_pixc_lines) float32 13kB dask.array<chunksize=(3277,), meta=np.ndarray>
data_window_last_cross_track (num_pixc_lines) float32 13kB dask.array<chunksize=(3277,), meta=np.ndarray>
Attributes:
description: cloud of geolocated interferogram pixels
interferogram_size_azimuth: 3277
interferogram_size_range: 5622
looks_to_efflooks: 1.550384810089747
num_azimuth_looks: 7.0
azimuth_offset: 7For plotting PIXC using classification and quality flags¶
In [57]:
# mask to get good water pixels
mask = np.where(np.logical_and(ds_PIXC.classification > 2, ds_PIXC.geolocation_qual <16384))
plt.scatter(x=ds_PIXC.longitude[mask], y=ds_PIXC.latitude[mask], c=ds_PIXC.height[mask])
plt.colorbar().set_label('Height (m)')
4. Water Mask Pixel Cloud Vector Attribute NetCDF¶
Search for data of interest¶
In [58]:
#Let's plot the same pass and tile as the above
pixcvec_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_PIXCVEC_2.0',
granule_name = '*010_412_087L*') #The same cycle, pass, and tile as previously downloaded
Granules found: 1
Let's download the first data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass.
In [59]:
earthaccess.download([pixcvec_results[0]], "./data_downloads")
Getting 1 granules, approx download size: 0.39 GB
QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 125.49it/s]
File SWOT_L2_HR_PIXCVec_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc already downloaded
PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 996.98it/s] COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]
Out[59]:
['data_downloads\\SWOT_L2_HR_PIXCVec_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc']
Open data using xarray¶
First, we'll programmatically get the filename we just downloaded and then view the file via xarray.
In [60]:
ds_PIXCVEC = xr.open_mfdataset("data_downloads/SWOT_L2_HR_PIXCVec_*.nc", decode_cf=False, engine='h5netcdf')
ds_PIXCVEC
Out[60]:
<xarray.Dataset> Size: 416MB
Dimensions: (points: 5332824, 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 21MB dask.array<chunksize=(5332824,), meta=np.ndarray>
range_index (points) int32 21MB dask.array<chunksize=(5332824,), meta=np.ndarray>
latitude_vectorproc (points) float64 43MB dask.array<chunksize=(5332824,), meta=np.ndarray>
longitude_vectorproc (points) float64 43MB dask.array<chunksize=(5332824,), meta=np.ndarray>
height_vectorproc (points) float32 21MB dask.array<chunksize=(5332824,), meta=np.ndarray>
reach_id (points, nchar_reach_id) |S1 59MB dask.array<chunksize=(5332824, 11), meta=np.ndarray>
node_id (points, nchar_node_id) |S1 75MB dask.array<chunksize=(5332824, 14), meta=np.ndarray>
lake_id (points, nchar_lake_id) |S1 53MB dask.array<chunksize=(5332824, 10), meta=np.ndarray>
obs_id (points, nchar_obs_id) |S1 69MB dask.array<chunksize=(5332824, 13), meta=np.ndarray>
ice_clim_f (points) int8 5MB dask.array<chunksize=(5332824,), meta=np.ndarray>
ice_dyn_f (points) int8 5MB dask.array<chunksize=(5332824,), meta=np.ndarray>
Attributes: (12/45)
Conventions: CF-1.7
title: Level 2 KaRIn high rate pixel cloud vect...
short_name: L2_HR_PIXCVec
institution: CNES
source: Level 1B KaRIn High Rate Single Look Com...
history: 2024-02-12T08:03:34.974012Z: Creation
... ...
xref_prior_river_db_file:
xref_prior_lake_db_file: SWOT_LakeDatabase_Nom_412_20000101T00000...
xref_reforbittrack_files: SWOT_RefOrbitTrackTileBoundary_Nom_20000...
xref_param_l2_hr_laketile_file: SWOT_Param_L2_HR_LakeTile_20000101T00000...
ellipsoid_semi_major_axis: 6378137.0
ellipsoid_flattening: 0.0033528106647474805Simple plot¶
In [61]:
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 == 0] = np.nan
pixcvec_lonvals[pixcvec_lonvals == 0] = np.nan
In [62]:
plt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)
plt.colorbar().set_label('Height (m)')
5. Raster NetCDF¶
Search for data of interest¶
In [63]:
raster_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_Raster_2.0',
#temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time
granule_name = '*100m*010_412_*', #The same cycle and pass as previously downloaded', # here we filter by files with '100m' in the name (This collection has two resolution options: 100m & 250m)
bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX
Granules found: 1
Let's download one data file.
In [64]:
earthaccess.download([raster_results[0]], "./data_downloads")
Getting 1 granules, approx download size: 0.06 GB
QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 199.95it/s]
File SWOT_L2_HR_Raster_100m_UTM13S_N_x_x_x_010_412_044F_20240208T165837_20240208T165858_PIC0_01.nc already downloaded
PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 998.64it/s] COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]
Out[64]:
['data_downloads\\SWOT_L2_HR_Raster_100m_UTM13S_N_x_x_x_010_412_044F_20240208T165837_20240208T165858_PIC0_01.nc']
Open data with xarray¶
First, we'll programmatically get the filename we just downloaded and then view the file via xarray.
In [65]:
ds_raster = xr.open_mfdataset(f'data_downloads/SWOT_L2_HR_Raster*', engine='h5netcdf')
ds_raster
Out[65]:
<xarray.Dataset> Size: 2GB
Dimensions: (y: 3236, x: 3237)
Coordinates:
* x (x) float64 26kB 2.162e+05 2.163e+05 ... 6.525e+05
* y (y) float64 26kB 4.152e+06 4.152e+06 ... 7.101e+06
Data variables: (12/39)
crs (y, x) object 84MB b'1' b'1' b'1' ... b'1' b'1'
longitude (y, x) float64 84MB dask.array<chunksize=(502, 502), meta=np.ndarray>
latitude (y, x) float64 84MB dask.array<chunksize=(502, 502), meta=np.ndarray>
wse (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>
wse_qual (y, x) float32 42MB dask.array<chunksize=(1505, 3237), meta=np.ndarray>
wse_qual_bitwise (y, x) float64 84MB dask.array<chunksize=(753, 753), meta=np.ndarray>
... ...
load_tide_fes (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>
load_tide_got (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>
pole_tide (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>
model_dry_tropo_cor (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>
model_wet_tropo_cor (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>
iono_cor_gim_ka (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>
Attributes: (12/49)
Conventions: CF-1.7
title: Level 2 KaRIn High Rate Raster Data Product
source: Ka-band radar interferometer
history: 2024-02-12T10:15:16Z : Creation
platform: SWOT
references: V1.2.1
... ...
x_min: 216200.0
x_max: 366700.0
y_min: 4151600.0
y_max: 4302000.0
institution: CNES
product_version: 01Quick interactive plot with hvplot¶
Note: this is not filtered by quality
In [ ]:
ds_raster.wse.hvplot.image(y='y', x='x')