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ASCAT Wind Data for Irish Continental Shelf region

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ASCAT Wind Data for Irish Continental Shelf region

Introduction

The Copernicus Marine Service (CMS), or Copernicus Marine Environment Monitoring Service (CMEMS), is the marine component of the European Union Copernicus Earth Observation (EO) programme. It provides free, regular and systematic ocean data products on a global and regional scale. The CMS Surface Wind Thematic Assembly Center (Wind TAC) is responsible for the collection, processing, qualification and distribution of surface winds data products derived from scatterometer satellite missions, including near-real time (NRT) and delayed mode (REP) processing of global wind observations. The focus of this notebook is on the CMS wind speed and direction data products generated using the Advanced SCATterometer (ASCAT) instruments deployed on the Metop satellites. Further details and examples of ocean wind analysis using ASCAT data products are described in the following publications:

This notebook provides details of:

  1. ASCAT wind data products retrieval from the CMS.

  2. The creation of the ASCAT Zarr wind stores that are included in the EOOffshore catalog.

  3. A brief look at these Zarr stores, including a demonstration of wind speed calculation.

These ASCAT Zarr stores have been uploaded to Zenodo.

How to cite:

  1. O’Callaghan, D. and McBreen, S.: Scalable Offshore Wind Analysis With Pangeo, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2746, https://doi.org/10.5194/egusphere-egu22-2746, 2022.

  2. O’Callaghan, D. and McBreen, S.: EOOffshore: ASCAT Wind Data for the Irish Continental Shelf Region, (1.0.0) [Data set], Zenodo, 2022. https://doi.org/10.5281/zenodo.6976298

Note: more extensive usage of the EOOffshore ASCAT Zarr stores may be found in the following notebooks:

ASCAT Wind Data Products

The EOOffshore project uses the following CMS data sets providing ASCAT wind products:

Both data sets contain the following relevant variables:

Variable

Unit

Height (metres above sea level)

Description

wind_speed

\(m s^{-1}\)

10

stress equivalent wind speed

wind_to_dir

degree

10

wind direction

wvc_quality_flag

flag mask

n/a

wind vector cell quality

air_density

\(kg\) \(m^{-3}\)

10

air density

ASCAT products featuring 0.125 degree grids, based on 12.5 km scatterometer swath observations, are available for all combinations of Metop A/B (REP) and Metop A/B/C (NRT) satellites and ASCending, DEScending passes. Although products can be manually downloaded manually from the MOTU Service server, automated retrieval is achieved using the Python MOTU Client API. Monthly ASCAT REP and NRT products covering the Irish Continental Shelf (ICS) region coordinates were retrieved for all available satellite and pass combinations:

Reprocessed (REP)

Near Real Time (NRT)

Observation / Models

satellite-observation

satellite-observation

Product type

multi-year

near-real-time

Processing level

Level-3

Level-3

Data type

Gridded (latitude/longitude)

Gridded (latitude/longitude)

Horizontal coverage

ICS bounding box [58, -26, 46, -4]

ICS bounding box [58, -26, 46, -4]

Horizontal resolution

0.125° × 0.125°

0.125° × 0.125°

Vertical coverage

Single level

Single level

Temporal coverage

2007-01-01T22:00:20 to 2021-07-31T21:42:48

2016-01-01T10:56:24 to 2021-09-30T21:53:36

Temporal resolution

Daily instantaneous (1-4 times daily
for Metop-A/B, ASC/DES combinations

Daily instantaneous (1-6 times daily
for Metop-A/B/C, ASC/DES combinations

Update frequency

Quarterly

Daily

File format

NetCDF-4

NetCDF-4

Total retrieved products

412

324

Total products size

21G

16G

Note:

  • Metop-A and Metop-B REP products are available from 2007 and 2019 respectively. No REP products are currently available for Metop-C.

  • Metop-C NRT products are available from October 2019.

ASCAT Wind Zarr Stores

The time dimension for a particular month is distributed across:

  • REP: 1-4 NetCDF monthly products for the Metop-A/B and ASC/DES combinations

  • NRT: 1-6 NetCDF monthly products for the Metop-A/B/C and ASC/DES combinations

Consequently, each set of REP/NRT monthly products was concatenated into a single interim product, containing a new time dimension coordinate based on the measurement_time variable in the original products. As measurement_time isn’t uniform for a particular product across the grid latitude/longitude coordinates, the spatial mean was used. Next, these interim products were loaded using xarray.open_mfdataset(), combined by their grid coordinates and concatenated along the time dimension. A height dimension was also added for the wind_speed and wind_to_dir variables. Each data set was persisted to a single, chunked, compressed Zarr store (REP: 14GB, NRT: 11GB), which is a cloud-optimised format suitable for multi-dimensional arrays. A time chunk size was specified that resulted in a low number of time chunks, as this approach is more suitable for subsequent processing of variables over time for Areas Of Interest (AOIs).

As requested by the Copernicus Marine Service Service Commitments and Licence, these Zarr stores were:

ASCAT in EOOffshore Catalog

Imports required for subsequent processing

%matplotlib inline
import cartopy.crs as ccrs
from intake import open_catalog
import itertools
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import shapely.geometry as sgeom
import xarray as xr

sns.set_style('whitegrid', {'axes.labelcolor': '0', "text.color": '0', 'xtick.color': '0', 'ytick.color': '0', 'font.sans-serif': ['DejaVu Sans', 'Liberation Sans', 'Bitstream Vera Sans', 'sans-serif'],})
sns.set_context('notebook', font_scale=1)

Open the catalog and view the ASCAT metadata

All EOOffshore data sets, including the ASCAT Zarr stores described above, are accessible using the EOOffshore Intake catalog. Each catalog entry provides a description and metadata associated with the corresponding data set, defined in a YAML configuration file. The EOOffshore catalog configuration was originally influenced by the Pangeo Cloud Data Store atmosphere.yaml catalog configuration.

catalog = open_catalog('data/intake-catalogs/eooffshore_ics.yaml')

View the REProcessed ASCAT Zarr store metadata

catalog.eooffshore_ics_cms_WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005_MetOp_ASCAT

eooffshore_ics_cms_WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005_MetOp_ASCAT:
  args:
    storage_options: null
    urlpath: /data/eo/zarr/cmems/WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005/eooffshore_ics_cmems_WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005_MetOp_ASCAT.zarr
  description: EOOffshore Project 2016 - 2021 Concatenated Copernicus Marine Service
    WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_002 MetOp ASCAT Ascending/Descending products,
    for the Irish Continental Shelf. Original products time coordinates have been
    replaced with the satellite/pass measurement_time values. Generated using E.U.
    Copernicus Marine Service Information; https://doi.org/10.48670/moi-00183
  driver: intake_xarray.xzarr.ZarrSource
  metadata:
    catalog_dir: /opt/eooffshore/notebooks/datasets/data/intake-catalogs/
    tags:
    - atmosphere
    - wind
    - metop
    - ascat
    - ocean
    - cmems
    title: CMEMS Global Ocean - Wind - METOP-A/B ASCAT - 12km daily Ascending/Descending
      V2 combined products for Irish Continental Shelf
    url: https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005/INFORMATION

View the Near Real Time ASCAT Zarr store metadata

catalog.eooffshore_ics_cms_WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002_MetOp_ASCAT

eooffshore_ics_cms_WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002_MetOp_ASCAT:
  args:
    storage_options: null
    urlpath: /data/eo/zarr/cmems/WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002/eooffshore_ics_cmems_WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002_MetOp_ASCAT.zarr
  description: EOOffshore Project 2016 - 2021 Concatenated Copernicus Marine Service
    WIND_L3_NRT_OBSERVATIONS_012_002 MetOp ASCAT Ascending/Descending products, for
    the Irish Continental Shelf. Original products time coordinates have been replaced
    with the satellite/pass measurement_time values. Generated using E.U. Copernicus
    Marine Service Information; https://doi.org/10.48670/moi-00182
  driver: intake_xarray.xzarr.ZarrSource
  metadata:
    catalog_dir: /opt/eooffshore/notebooks/datasets/data/intake-catalogs/
    tags:
    - atmosphere
    - wind
    - metop
    - ascat
    - ocean
    - cmems
    title: CMEMS Global Ocean - Wind - METOP-A/B/C ASCAT - 12km daily Ascending/Descending
      V2 combined products for Irish Continental Shelf
    url: https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002/INFORMATION

Load the catalog ASCAT Zarr store into an xarray.Dataset

Intake catalog entries typically specify a driver to be used when loading the corresponding data set. The ASCAT entries specify intake_xarray.xzarr.ZarrSource, a driver implementation provided by the intake-xarray library. This enables NetCDF and Zarr data sets to be loaded using xarray, a library for processing N-D labeled arrays and datasets. As xarray labels take the form of dimensions, coordinates and attributes on top of NumPy-like arrays, it is particularly suited to data sets such as ASCAT whose variables feature latitude/longitude grid coordinates.

This intake driver will load the associated dataset into an xarray.Dataset. To enable support for potentially large data sets, the to_dask() function is used to load the underlying variable arrays with Dask, a parallel, out-of-core computing library. The ZarrSource implementation will load the data set variables into Dask arrays, which will be loaded and processed in parallel as chunks during subsequent computation. As discussed above, variable chunk sizes may be specified during Zarr store creation.

Here is the ASCAT REProcessed store loaded into an xarray.Dataset:

  • All variables have associated coordinate dimensions:

    • time - observation time

    • latitude and longitude - the corresponding coordinate grid

  • The wind_speed and wind_to_dir variables have a height coordinate dimension, reflecting the 10m (above sea level) variables in the products retrieved from the CMS.

  • A low number of time chunks have been specified, to support subsequent computation across time for smaller AOI grid coordinates.

Note: The REProcessed Zarr store is used in this notebook. However, all operations may be applied to the NRT store.

ds = catalog.eooffshore_ics_cms_WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005_MetOp_ASCAT.to_dask()

# Consistent coordinate naming
ds = ds.rename({'lat': 'latitude', 'lon': 'longitude'})

ds

<xarray.Dataset>
Dimensions:                   (time: 12467, latitude: 97, longitude: 208,
                               height: 1)
Coordinates:
  * height                    (height) int64 10
  * latitude                  (latitude) float32 46.06 46.19 ... 57.94 58.06
  * longitude                 (longitude) float32 334.1 334.2 ... 359.8 359.9
  * time                      (time) datetime64[ns] 2007-01-01T22:00:20.35560...
Data variables: (12/26)
    air_density               (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    bs_distance               (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    eastward_model_stress     (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    eastward_stress           (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    eastward_wind             (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    model_stress_curl         (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    ...                        ...
    wind_divergence           (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    wind_speed                (height, time, latitude, longitude) float32 dask.array<chunksize=(1, 1500, 97, 208), meta=np.ndarray>
    wind_stress_magnitude     (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    wind_to_dir               (height, time, latitude, longitude) float32 dask.array<chunksize=(1, 1500, 97, 208), meta=np.ndarray>
    wvc_index                 (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    wvc_quality_flag          (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
Attributes: (12/36)
    Conventions:                      CF-1.6
    History:                          Translated to CF-1.0 Conventions by Net...
    comment:                          Orbit period and inclination are consta...
    contents:                         ovw
    creation_date:                    2021-11-05
    creation_time:                    19:16:35
    ...                               ...
    start_date:                       2021-07-31
    start_time:                       00:00:00
    stop_date:                        2021-07-31
    stop_time:                        23:59:58
    title:                            Global Ocean - Wind - METOP-A ASCAT - 1...
    title_short_name:                 ASCATA-L3-Coastal

ASCAT wind speed (2007 - 2021)

Each variable in an ASCAT data set, for example, wind speed, is loaded into an xarray.DataArray:

ds.wind_speed

<xarray.DataArray 'wind_speed' (height: 1, time: 12467, latitude: 97,
                                longitude: 208)>
dask.array<open_dataset-04dae4c04ae40ecc36ddb6f3628e0703wind_speed, shape=(1, 12467, 97, 208), dtype=float32, chunksize=(1, 1500, 97, 208), chunktype=numpy.ndarray>
Coordinates:
  * height     (height) int64 10
  * latitude   (latitude) float32 46.06 46.19 46.31 46.44 ... 57.81 57.94 58.06
  * longitude  (longitude) float32 334.1 334.2 334.3 334.4 ... 359.7 359.8 359.9
  * time       (time) datetime64[ns] 2007-01-01T22:00:20.355604638 ... 2021-0...
Attributes:
    _ChunkSizes:    [1, 720, 1440]
    long_name:      stress equivalent wind speed at 10 m
    standard_name:  wind_speed
    units:          m s-1
    valid_max:      5000
    valid_min:      0

Calculate mean wind speed over time dimension at AOI grid coordinates

Using Dask, the data set loading process is lazy, where no data is loaded inititally. Instead, data loading is delayed until execution time, where variables will be loaded and processed in parallel according to the corresponding chunks specification. Dask arrays implement a subset of the NumPy ndarray interface using blocked algorithms, and the original variable arrays will be split into smaller chunk arrays, enabling computation on arrays larger than memory using all available cores. The blocked algorithms are coordinated using Dask graphs.

To perform some analysis at known AOI latitude/longitude coordinates, the xarray.DataArray.sel(..., method='nearest') function may be used to select a subset of the data array (or data set) at coordinates nearest to the specified parameters. Here, mean wind speed over the time dimension is determined for the specified coordinates, where Dask graph execution is triggered by calling compute().

Graph execution is managed by a task scheduler. The default scheduler (used for executing this notebook) executes computations with local threads. However, execution may also be performed on a distributed cluster without any change to the xarray code used here.

ds.wind_speed.sel(longitude=354.57, latitude=53.4836, method='nearest').mean(dim='time').compute()

<xarray.DataArray 'wind_speed' (height: 1)>
array([7.9561014], dtype=float32)
Coordinates:
  * height     (height) int64 10
    latitude   float32 53.44
    longitude  float32 354.6

ASCAT wind vector cell quality flag

ASCAT products contain a wvc_quality_flag variable, which reflects the quality of corresponding wind variables at a particular time.

Each variable in the ASCAT data sets, including wvc_quality_flag, is loaded into an xarray.DataArray:

ds.wvc_quality_flag

<xarray.DataArray 'wvc_quality_flag' (time: 12467, latitude: 97, longitude: 208)>
dask.array<open_dataset-04dae4c04ae40ecc36ddb6f3628e0703wvc_quality_flag, shape=(12467, 97, 208), dtype=float64, chunksize=(1500, 97, 208), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float32 46.06 46.19 46.31 46.44 ... 57.81 57.94 58.06
  * longitude  (longitude) float32 334.1 334.2 334.3 334.4 ... 359.7 359.8 359.9
  * time       (time) datetime64[ns] 2007-01-01T22:00:20.355604638 ... 2021-0...
Attributes:
    _ChunkSizes:    [1, 720, 1440]
    flag_masks:     [64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768,...
    flag_meanings:  distance_to_gmf_too_large data_are_redundant no_meteorolo...
    long_name:      wind vector cell quality
    standard_name:  status_flag
    valid_max:      8388607
    valid_min:      0

Create a mapping between quality flag meaning and corresponding flag mask using the wvc_quality_flag attributes

The ASCAT Wind Product User Manual (1.17, 01/12/2021) provides definitions of the quality flag values, where these are also contained in the wvc_quality_flag metadata attributes. The attribute definitions are also documented in the Copernicus Marine Service Product User Manual For Global Ocean L3 Wind (Issue 2.21, November 2021):

int wvc_quality_flag(time, lat, lon) ;
    wvc_quality_flag:_FillValue = -2147483647 ;
    wvc_quality_flag:missing_value = -2147483647 ;
    wvc_quality_flag:valid_min = 0 ;
    wvc_quality_flag:valid_max = 8388607 ;
    wvc_quality_flag:standard_name = "status_flag" ;
    wvc_quality_flag:long_name = "wind vector cell quality" ;
    wvc_quality_flag:coordinates = "time lat lon" ;
    wvc_quality_flag:flag_masks = 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304 ;
    wvc_quality_flag:flag_meanings = "distance_to_gmf_too_large data_are_redundant no_meteorological_background_used rain_detected rain_flag_not_usable small_wind_less_than_or_equal_to_3_m_s large_wind_greater_than_30_m_s wind_inversion_not_successful some_portion_of_wvc_is_over_ice some_portion_of_wvc_is_over_land variational_quality_control_fails knmi_quality_control_fails product_monitoring_event_flag product_monitoring_not_used any_beam_noise_content_above_threshold poor_azimuth_diversity not_enough_good_sigma0_for_wind_retrieval" ;

These attributes may be used to create mapping of flag meaning to flag mask value

quality_flag_masks = {flag_meaning:flag_mask for flag_meaning, flag_mask in zip(ds.wvc_quality_flag.flag_meanings.split(' '), ds.wvc_quality_flag.flag_masks)}
quality_flag_masks

{'distance_to_gmf_too_large': 64,
 'data_are_redundant': 128,
 'no_meteorological_background_used': 256,
 'rain_detected': 512,
 'rain_flag_not_usable': 1024,
 'small_wind_less_than_or_equal_to_3_m_s': 2048,
 'large_wind_greater_than_30_m_s': 4096,
 'wind_inversion_not_successful': 8192,
 'some_portion_of_wvc_is_over_ice': 16384,
 'some_portion_of_wvc_is_over_land': 32768,
 'variational_quality_control_fails': 65536,
 'knmi_quality_control_fails': 131072,
 'product_monitoring_event_flag': 262144,
 'product_monitoring_not_used': 524288,
 'any_beam_noise_content_above_threshold': 1048576,
 'poor_azimuth_diversity': 2097152,
 'not_enough_good_sigma0_for_wind_retrieval': 4194304}

Plot the percentage of time the quality flag masks are set for all grid coordinates

Here, using the time dimension, the percentage of observations featuring each quality flag is determined for all grid coordinates.

Map plots of variables with grid coordinates may be generated using xarray’s plotting capabilities, and other libraries. To plot mean owiWindQuality:

with open('data/linestring_ics_geo.json') as jf:
    icsgeo = json.load(jf)
icsline = sgeom.LineString(icsgeo['features'][0]['geometry']['coordinates'])

MAP_PROJECTION = ccrs.PlateCarree()

def plot_ics_variable(ax: mpl.axes.Axes, 
                      variable: xr.DataArray, 
                      title: str, 
                      vmin: float, 
                      vmax: float = None, 
                     ):
    variable.plot(cmap='viridis', vmin=vmin, vmax=vmax, ax=ax)
    ax.set_aspect('auto')

    # ICS boundary
    ax.add_geometries([icsline], MAP_PROJECTION, edgecolor = sns.color_palette()[0], facecolor='none')

    # Lat/lon gridlines
    gl = ax.gridlines(draw_labels=['left', 'bottom'], alpha=0.2, linestyle='--', formatter_kwargs=dict(direction_label=False))
    label_style = {'size': 10}
    gl.xlabel_style = label_style
    gl.ylabel_style = label_style
    
    # Ireland only
    ax.set_xlim((-11.5, -4.70))
    ax.set_ylim((51, 56))
        
    ax.coastlines(color='grey')
    ax.set_title(title);

Note:

  • Values of 0 (no flag mask bits set) and NaN (Missing flag) are present in the data.

  • A separate colorbar range is used for each plot, as the percentages can be very low for certain flags. In cases where particular flags are not set, a range of [0, 1] is used.

  • The plots are restricted to Irish coastal waters to increase legibility

fig, ax = plt.subplots(nrows=6, ncols=3, figsize=(19, 36), subplot_kw=dict(projection=MAP_PROJECTION))
ax = ax.flatten()

total_observations = ds.wvc_quality_flag.where(ds.wvc_quality_flag.fillna(-1)!=-1).count(dim='time').compute()

quality_flag_values = {'No flag mask set': ds.wvc_quality_flag.where(lambda x:x == 0)}
quality_flag_values.update({f'{flag_meaning} flag set': ds.wvc_quality_flag.astype(int).where(lambda x:x & flag_mask) for flag_meaning, flag_mask in quality_flag_masks.items()})
                    
for i, flag_label in enumerate(list(quality_flag_values)):
    # Calculate percentage of non-zero/nan observations for each coordinate where this flag was set
    flag_percentage = (100 * (quality_flag_values[flag_label].count(dim='time', keep_attrs=True)/total_observations)).compute()
    vmax = flag_percentage.max().item()
    vmax = 100 if vmax == 0 else vmax
    plot_ics_variable(variable=flag_percentage,
                      title=f'% {flag_label}',
                      vmin=0,
                      vmax=vmax,
                      ax=ax[i])
_images/ASCAT_ICS_Wind_Data_27_0.png

Filter ASCAT data using quality flags

These grid flag percentages may suggest that the ASCAT data could be filtered using the corresponding flag masks, for example, where all combinations of the following masks would be retained:

  • wvc_quality_flag = 0: Although this value isn’t documented, it is assumed that the corresponding data is of acceptable quality

  • wvc_quality_flag = 2048: Flags with the 'small_wind_less_than_or_equal_to_3_m_s' mask are accepted as these ensure that coastal AOIs (e.g. Irish Weather Buoy Network (IWB) buoys) are included, and that more suitable Weibull fits are subsequently generated.

  • wvc_quality_flag = 32768: Flags with the 'some_portion_of_wvc_is_over_land' mask are accepted as these ensure that coastal AOIs (e.g. IWB buoys) are included

retained_flag_meanings = ['small_wind_less_than_or_equal_to_3_m_s', 
                            'some_portion_of_wvc_is_over_land']
# Retain all flag mask combinations
retained_flag_masks = np.unique([sum(x) for x in itertools.combinations([0, 0] + [quality_flag_masks[x] for x in retained_flag_meanings], 2)])

filtered_ds = ds.where(ds.wvc_quality_flag.isin(retained_flag_masks))
filtered_ds

<xarray.Dataset>
Dimensions:                   (time: 12467, latitude: 97, longitude: 208,
                               height: 1)
Coordinates:
  * height                    (height) int64 10
  * latitude                  (latitude) float32 46.06 46.19 ... 57.94 58.06
  * longitude                 (longitude) float32 334.1 334.2 ... 359.8 359.9
  * time                      (time) datetime64[ns] 2007-01-01T22:00:20.35560...
Data variables: (12/26)
    air_density               (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    bs_distance               (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    eastward_model_stress     (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    eastward_stress           (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    eastward_wind             (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    model_stress_curl         (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    ...                        ...
    wind_divergence           (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    wind_speed                (height, time, latitude, longitude) float32 dask.array<chunksize=(1, 1500, 97, 208), meta=np.ndarray>
    wind_stress_magnitude     (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    wind_to_dir               (height, time, latitude, longitude) float32 dask.array<chunksize=(1, 1500, 97, 208), meta=np.ndarray>
    wvc_index                 (time, latitude, longitude) float32 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
    wvc_quality_flag          (time, latitude, longitude) float64 dask.array<chunksize=(1500, 97, 208), meta=np.ndarray>
Attributes: (12/36)
    Conventions:                      CF-1.6
    History:                          Translated to CF-1.0 Conventions by Net...
    comment:                          Orbit period and inclination are consta...
    contents:                         ovw
    creation_date:                    2021-11-05
    creation_time:                    19:16:35
    ...                               ...
    start_date:                       2021-07-31
    start_time:                       00:00:00
    stop_date:                        2021-07-31
    stop_time:                        23:59:58
    title:                            Global Ocean - Wind - METOP-A ASCAT - 1...
    title_short_name:                 ASCATA-L3-Coastal

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