# # # This source code is subject to the license referenced at
# # # https://github.com/NRLMMD-GEOIPS.
"""Read derived surface winds from KNMI scatterometer netcdf data."""
# Python Standard Libraries
from copy import deepcopy
from glob import glob
import logging
from os.path import basename
# Third-Party Libraries
import numpy
import xarray
# GeoIPS imports
from geoips.xarray_utils.time import (
get_min_from_xarray_time,
get_max_from_xarray_time,
)
LOG = logging.getLogger(__name__)
MS_TO_KTS = 1.94384
DEG_TO_KM = 111.321
interface = "readers"
family = "standard"
name = "scat_noaa_winds_netcdf"
source_names = ["ascat"]
[docs]def read_noaa_data(wind_xarray):
"""Reformat ascat xarray object appropriately.
* variables: latitude, longitude, time,
wind_speed_kts, wind_dir_deg_met
* attributes: source_name, platform_name, data_provider,
interpolation_radius_of_influence
"""
geoips_metadata = {}
geoips_metadata["data_provider"] = "noaa"
# Setting standard geoips attributes
LOG.info("Reading %s data", wind_xarray.source)
geoips_metadata["source_name"] = "ascat"
geoips_metadata["platform_name"] = wind_xarray.platform.lower()
# Manually construct the time array, since xarray cannot auto decode these data
from astropy.time import Time
from dateutil.relativedelta import relativedelta
utc_time = Time(wind_xarray.time_seconds, format="unix_tai").utc.datetime
# Seconds since time is w.r.t. 1990-01-01, need to offset 20 years
utc_time += relativedelta(years=20)
wind_xarray["time"].data = utc_time
wind_xarray["time"].attrs["units"] = "Time in UTC"
wind_xarray["time"].attrs.pop("valid_min")
wind_xarray["time"].attrs.pop("valid_max")
# In NOAA files, pixel size is only mentioned in the title
# e.g. L2OVW50kmASCAT
psize_string = wind_xarray.title
for string in ["L2OVW", "kmASCAT"]:
psize_string = psize_string.replace(string, "")
pixel_size = float(psize_string)
# Interpolation Radius of Influence
geoips_metadata["interpolation_radius_of_influence"] = pixel_size * 1000.0
geoips_metadata["sample_distance_km"] = pixel_size
# setting wind_speed_kts appropriately
wind_xarray["wind_speed_kts"] = wind_xarray["wind_speed"] * MS_TO_KTS
wind_xarray["wind_speed_kts"].attrs = wind_xarray["wind_speed"].attrs
wind_xarray["wind_speed_kts"].attrs["units"] = "kts"
# Save wind direction to standardized name across ascat readers
wind_xarray["wind_dir_deg_met"] = wind_xarray["wind_dir"]
wind_xarray["wind_dir_deg_met"].attrs = wind_xarray["wind_dir"].attrs
wind_xarray.wind_dir_deg_met.attrs["standard_name"] = "wind_from_direction"
wind_xarray.wind_dir_deg_met.attrs["valid_max"] = 360
# Set lat/lons/timestamp appropriately
# Also rename wind ambiguity keys
wind_xarray = wind_xarray.rename(
{
"lat": "latitude",
"lon": "longitude",
"wind_speed_sol": "wind_speed_ambiguity_kts",
"wind_dir_sol": "wind_dir_deg_ambiguity_met",
}
)
RAIN_FLAG_BIT = 9
if hasattr(xarray, "ufuncs"):
# ufuncs no longer available as of xarray v2022.06 (at least)
# ufuncs appears to be back as of v2025.3.1
wind_xarray["rain_flag"] = xarray.ufuncs.logical_and(
wind_xarray["wvc_quality_flag"], (1 << RAIN_FLAG_BIT)
)
# rf variable used downstream for determining ambiguities
rf = wind_xarray["rain_flag"]
else:
# Can not figure out how to do the logical and in xarray now -
# so do it in numpy.
# This may not be the most efficient, especially if we are trying to use
# dask / lazy processing.
# Dropping the ".to_masked_array()" appears to lose the nan values -
# but could perhaps do that then re-mask?
rf = numpy.logical_and(
wind_xarray["wvc_quality_flag"].to_masked_array(), (1 << RAIN_FLAG_BIT)
)
data_dims = {}
for dim in wind_xarray["wvc_quality_flag"].dims:
data_dims[dim] = wind_xarray.dims[dim]
wind_xarray["rain_flag"] = xarray.DataArray(
rf.astype(int), coords=wind_xarray.coords, dims=data_dims
)
# Create rain flag for ambiguity products
# The rain_flag variable is only 2D. We need to
# create a rain_flag_ambiguity variable that has
# the same dimensions as "wind_speed_ambiguity_kts"
ambs = wind_xarray["wind_speed_ambiguity_kts"]
# Create data_dims dictionary for ambiguity variable
data_dims = {x: wind_xarray.dims[x] for x in ambs.dims}
# Create 3D rain flag by stacking 2D rain_flag by number of ambiguities
rf_amb = numpy.dstack([rf] * data_dims["MaxAmbiguity"])
# Set 3D rain flag values to NaN where ambiguities are NaN
nan_ambs = numpy.isnan(ambs.data)
rf_amb[nan_ambs] = numpy.nan
# Store to xarray dataset
wind_xarray["rain_flag_ambiguity"] = xarray.DataArray(
rf_amb.astype(int), coords=wind_xarray.coords, dims=data_dims
)
wind_xarray = wind_xarray.set_coords(["time"])
return wind_xarray, geoips_metadata
[docs]def call(fnames, metadata_only=False, chans=None, area_def=None, self_register=False):
"""Read KNMI scatterometer derived winds from netcdf data.
Parameters
----------
fnames : list
* List of strings, full paths to files
metadata_only : bool, default=False
* NOT YET IMPLEMENTED
* Return before actually reading data if True
chans : list of str, default=None
* NOT YET IMPLEMENTED
* List of desired channels (skip unneeded variables as needed).
* Include all channels if None.
area_def : pyresample.AreaDefinition, default=None
* NOT YET IMPLEMENTED
* Specify region to read
* Read all data if None.
self_register : str or bool, default=False
* NOT YET IMPLEMENTED
* register all data to the specified dataset id (as specified in the
return dictionary keys).
* Read multiple resolutions of data if False.
Returns
-------
dict of xarray.Datasets
* dictionary of xarray.Dataset objects with required Variables and
Attributes.
* Dictionary keys can be any descriptive dataset ids.
See Also
--------
:ref:`xarray_standards`
Additional information regarding required attributes and variables
for GeoIPS-formatted xarray Datasets.
"""
final_wind_xarrays = {}
ingested = []
for fname in fnames:
wind_xarray = xarray.open_dataset(str(fname), decode_times=False)
LOG.info("Read data from %s", fname)
if hasattr(wind_xarray, "title") and "ASCAT" in wind_xarray.title:
wind_xarray, geoips_metadata = read_noaa_data(wind_xarray)
geoips_metadata["source_file_names"] = [basename(fname)]
geoips_metadata["minimum_coverage"] = 20
ingested += [(wind_xarray, geoips_metadata)]
for curr_xobj, curr_geoips_metadata in ingested:
curr_file_metadata = deepcopy(curr_xobj.attrs)
curr_xobj.attrs = {**curr_file_metadata, **curr_geoips_metadata}
if "WINDSPEED" not in final_wind_xarrays:
final_xobj = curr_xobj.copy()
final_xobj.attrs = curr_geoips_metadata
final_xobj.attrs["source_file_attributes"] = [curr_file_metadata]
final_xobj.attrs["source_file_names"] = curr_geoips_metadata[
"source_file_names"
].copy()
final_wind_xarrays["WINDSPEED"] = final_xobj
continue
final_xobj = final_wind_xarrays["WINDSPEED"]
for attr_name in ["source_name", "platform_name"]:
if final_xobj.attrs[attr_name] != curr_xobj.attrs[attr_name]:
raise ValueError(
f"Attribute '{attr_name}' must match on all data files"
f"Expected '{final_xobj.attrs[attr_name]}',"
f"Current value '{curr_xobj.attrs[attr_name]}'"
)
final_xobj = xarray.concat([final_xobj, curr_xobj], dim="NUMROWS")
final_xobj.attrs["source_file_names"] += curr_geoips_metadata[
"source_file_names"
]
final_xobj.attrs["source_file_attributes"] += [curr_file_metadata]
final_wind_xarrays["WINDSPEED"] = final_xobj
for wind_xarray in final_wind_xarrays.values():
LOG.info("Setting standard metadata")
wind_xarray.attrs["start_datetime"] = get_min_from_xarray_time(
wind_xarray, "time"
)
wind_xarray.attrs["end_datetime"] = get_max_from_xarray_time(
wind_xarray, "time"
)
if "wind_speed_kts" in wind_xarray.variables:
# These text files store wind speeds natively in kts
wind_xarray["wind_speed_kts"].attrs["units"] = "kts"
LOG.info(
"Read data %s start_dt %s source %s platform %s data_provider %s roi "
"%s native resolution",
wind_xarray.attrs["start_datetime"],
wind_xarray.attrs["source_name"],
wind_xarray.attrs["platform_name"],
wind_xarray.attrs["data_provider"],
wind_xarray.attrs["interpolation_radius_of_influence"],
wind_xarray.attrs["sample_distance_km"],
)
final_wind_xarrays["METADATA"] = wind_xarray[[]]
return final_wind_xarrays
[docs]def get_test_files(test_data_dir):
"""Generate test xarray from test files for unit testing."""
filepath = (
test_data_dir
+ "/test_data_scat/data/20230524_metopc_"
+ "noaa_class_tc2023wp02mawar/L2OVW25kmASCAT_v1r1_m03_s202305242348000*.nc"
)
filelist = glob(filepath)[:2]
tmp_xr = call(filelist)
if len(filelist) == 0:
raise NameError("No files found")
return tmp_xr
[docs]def get_test_parameters():
"""Generate test data key for unit testing."""
return [{"data_key": "WINDSPEED", "data_var": "wind_speed_kts"}]
