# # # This source code is subject to the license referenced at
# # # https://github.com/NRLMMD-GEOIPS.
"""Arctic Weather Satellite (aws) MWR Level 1B NetCDF reader.
Channel information is from the ESA webpage:
https://www.esa.int/Applications/Observing_the_Earth/Meteorological_missions/
Arctic_Weather_Satellite/The_instrument
All channels have a same array size (nscan,nfov).
Chan Freq(GHz) FOV(km) Utilisation
Band 1:
1 50.3 <= 40 Temp sounding
2 52.8 <= 40 Temp sounding
3 53.246 <= 40 Temp sounding
4 53.956 <= 40 Temp sounding
5 54.4 <= 40 Temp sounding
6 54.94 <= 40 Temp sounding
7 55.5 <= 40 Temp sounding
8 57.290 <= 40 Temp sounding
Band 2:
9 89 <= 20 Window and cloud detection
Band 3:
10 165.5 <= 10 Window and humidity sounding
11 176.31 <= 10 Humidity sounding
12 178.81 <= 10 Humidity sounding
13 180.31 <= 10 Humidity sounding
14 181.51 <= 10 Humidity sounding
15 182.31 <= 10 Humidity sounding
Band 4:
16 325.15 +- 1.2 <= 10 Humidity sounding/cloud detection
17 325.15 +- 2.4 <= 10 Humidity sounding/cloud detection
18 325.15 +- 4.1 <= 10 Humidity sounding/cloud detection
19 325.15 +- 6.6 <= 10 Humidity sounding/cloud detection
Note: We do not know actual assignments of channels to the four groups, due to lack of
official documentation on Arctic Weather Satellite data. Thus, we select current
channels for each band based on internal discussions and our experience.
We will make adjustments once there are any differences from official document is
available.
Also, variables (L1B_quality_flag and degraded_channels) are space holders for QC
purposes. They are not used for now, but will be applied later.
"""
from geoips.interfaces.class_based.readers import BaseReaderPlugin
import logging
from datetime import datetime
# import os
# from collections import OrderedDict
# import numpy as np
import xarray
LOG = logging.getLogger(__name__)
[docs]class AwsNetcdfReaderPlugin(BaseReaderPlugin):
"""AWS Netcdf reader plugin class."""
interface = "readers"
family = "standard"
name = "aws_netcdf"
# AWS sensor info
# setup variables
ROIs = {"Band1": 45000, "Band2": 25000, "Band3": 20000, "Band4": 20000}
group_names = ["/data/navigation", "/data/calibration", "/quality"]
list_vars = [
"aws_lat",
"aws_lon",
"aws_solar_zenith_angle",
"aws_solar_azimuth_angle",
"aws_satellite_zenith_angle",
"aws_satellite_azimuth_angle",
"L1B_quality_flag",
"degraded_channels",
"aws_toa_brightness_temperature",
]
source_names = ["mwr"]
[docs] def merge_xarrays(self, src_xobj, dst_xobj):
"""Merge one xarray into another.
Parameters
----------
src_xobj : xarray.Dataset
Input dataset to merge
dst_xobj : xarray.Dataset
Dataset where src_xobj will be merged
Returns
-------
xarray.Dataset
Merged xarray dataset
"""
tmp_xobj = xarray.Dataset()
for dvar in src_xobj.data_vars.keys():
dset = src_xobj[dvar]
if dvar not in dst_xobj:
dst_xobj[dvar] = dset
else:
tmp_xobj[dvar] = xarray.concat(
[dst_xobj[dvar], dset], dim=dst_xobj[dvar].dims[0]
)
dst_xobj = dst_xobj.drop(dvar)
try:
merged_xobj = xarray.merge([tmp_xobj, dst_xobj])
except ValueError:
tmp_dims = {x: tmp_xobj[x].size for x in tmp_xobj.dims}
dst_dims = {x: dst_xobj[x].size for x in dst_xobj.dims}
mismatching_dims = [x for x in dst_dims if tmp_dims[x] != dst_dims[x]]
merged_xobj = xarray.combine_nested(
[tmp_xobj, dst_xobj], concat_dim=mismatching_dims
)
return merged_xobj
[docs] def call(
self,
fnames,
metadata_only=False,
chans=None,
area_def=None,
self_register=False,
):
"""Read aws L1 netCDF data.
Parameters
----------
fci_files : list
list of files for a given scan time
metadata_only : bool, optional
Only return metadata, by default False
chans : list, optional
List of channels/variables to load, by default None
area_def : pyresample area definition, optional
Read in data for specific area definition, by default None
(currently unsupported)
self_register : bool, optional
Self register data to given resolution, by default False
(currently unsupported)
Returns
-------
dict
dictionary of xarray datasets
Raises
------
AttributeError
Encountered unknown platform name in source file
Notes
-----
data : grouped file
stored in groups (status, data, quality) and sub-groups
open_groups to open and decode a file for required fields
global attributes in home group '/'
'/' is an important character to access data
process only one orbit file
"""
xobj_group = xarray.Dataset()
for fname in fnames: # loop input files (only one for now)
LOG.info(f"Opening: {fname}")
# First grab metadata
ds = xarray.open_groups(fname)
available_groups = list(ds)
meta = xarray.Dataset(attrs=ds["/"].attrs)
try:
if "." in meta.attrs["sensing_start_time_utc"]:
tformat = "%Y-%m-%d %H:%M:%S.%f"
else:
tformat = "%Y-%m-%d %H:%M:%S"
start_datetime = datetime.strptime(
meta.attrs["sensing_start_time_utc"], tformat
)
end_datetime = datetime.strptime(
meta.attrs["sensing_end_time_utc"], tformat
)
except KeyError:
LOG.info("error in orbit data start and end time")
if hasattr(meta, "Spacecraft"):
pform = meta.attrs["Spacecraft"]
else:
raise AttributeError("Cannot identify platform name")
geoips_attrs = {
"area_definition": area_def,
"start_datetime": start_datetime,
"end_datetime": end_datetime,
"vertical_data_type": "surface",
"source_name": "mwr",
"platform_name": self.get_geoips_platform_name(pform),
"data_provider": "noaa",
"interpolation_radius_of_influence": 20000,
}
meta.attrs = dict(meta.attrs, **geoips_attrs)
if metadata_only is True:
LOG.info(
"metadata_only is True, reading only first"
+ "file for metadata information and returning"
)
return {"METADATA": meta}
# load data for list_vars from these groups
for group in self.group_names:
if group in available_groups:
available_vars_group = list(ds[group].data_vars.keys())
# Only load the requested variables from this group
drop_vars = list(
set(available_vars_group).difference(set(self.list_vars))
)
xr = xarray.load_dataset(
fname, group=group, drop_variables=drop_vars
)
# merge xarray
xobj_group = self.merge_xarrays(xr, xobj_group)
# setup 4-band xarrays
# Channel assignments for each band are based on internal discussions and
# our experience. They could be adjusted later once data documentations are
# available.
# quality_flag and degraded_channels are spare variables for QC purposes. They
# are not used now, but will be applied later.
xobj_band1 = xarray.Dataset()
xobj_band2 = xarray.Dataset()
xobj_band3 = xarray.Dataset()
xobj_band4 = xarray.Dataset()
xobj_band1["chan1"] = xobj_group["aws_toa_brightness_temperature"][:, :, 0]
xobj_band1["chan2"] = xobj_group["aws_toa_brightness_temperature"][:, :, 1]
xobj_band1["chan3"] = xobj_group["aws_toa_brightness_temperature"][:, :, 2]
xobj_band1["chan4"] = xobj_group["aws_toa_brightness_temperature"][:, :, 3]
xobj_band1["chan5"] = xobj_group["aws_toa_brightness_temperature"][:, :, 4]
xobj_band1["chan6"] = xobj_group["aws_toa_brightness_temperature"][:, :, 5]
xobj_band1["chan7"] = xobj_group["aws_toa_brightness_temperature"][:, :, 6]
xobj_band1["chan8"] = xobj_group["aws_toa_brightness_temperature"][:, :, 7]
xobj_band2["chan9"] = xobj_group["aws_toa_brightness_temperature"][:, :, 8]
xobj_band3["chan10"] = xobj_group["aws_toa_brightness_temperature"][:, :, 9]
xobj_band3["chan11"] = xobj_group["aws_toa_brightness_temperature"][:, :, 10]
xobj_band3["chan12"] = xobj_group["aws_toa_brightness_temperature"][:, :, 11]
xobj_band3["chan13"] = xobj_group["aws_toa_brightness_temperature"][:, :, 12]
xobj_band3["chan14"] = xobj_group["aws_toa_brightness_temperature"][:, :, 13]
xobj_band3["chan15"] = xobj_group["aws_toa_brightness_temperature"][:, :, 14]
xobj_band4["chan16"] = xobj_group["aws_toa_brightness_temperature"][:, :, 15]
xobj_band4["chan17"] = xobj_group["aws_toa_brightness_temperature"][:, :, 16]
xobj_band4["chan18"] = xobj_group["aws_toa_brightness_temperature"][:, :, 17]
xobj_band4["chan19"] = xobj_group["aws_toa_brightness_temperature"][:, :, 18]
xobj_band1["latitude"] = xobj_group["aws_lat"][:, :, 0]
xobj_band1["latitude"] = xobj_group["aws_lat"][:, :, 0]
xobj_band1["longitude"] = xobj_group["aws_lon"][:, :, 0]
xobj_band1["solar_zenith_angle"] = xobj_group["aws_solar_zenith_angle"][:, :, 0]
xobj_band1["solar_azimuth_angle"] = xobj_group["aws_solar_azimuth_angle"][
:, :, 0
]
xobj_band1["satellite_zenith_angle"] = xobj_group["aws_satellite_zenith_angle"][
:, :, 0
]
xobj_band1["satellite_azimuth_angle"] = xobj_group[
"aws_satellite_azimuth_angle"
][:, :, 0]
xobj_band1["quality_flag"] = xobj_group["L1B_quality_flag"]
xobj_band1["degraded_channels"] = xobj_group["degraded_channels"]
xobj_band2["latitude"] = xobj_group["aws_lat"][:, :, 1]
xobj_band2["latitude"] = xobj_group["aws_lat"][:, :, 1]
xobj_band2["longitude"] = xobj_group["aws_lon"][:, :, 1]
xobj_band2["solar_zenith_angle"] = xobj_group["aws_solar_zenith_angle"][:, :, 1]
xobj_band2["solar_azimuth_angle"] = xobj_group["aws_solar_azimuth_angle"][
:, :, 1
]
xobj_band2["satellite_zenith_angle"] = xobj_group["aws_satellite_zenith_angle"][
:, :, 1
]
xobj_band2["satellite_azimuth_angle"] = xobj_group[
"aws_satellite_azimuth_angle"
][:, :, 1]
xobj_band2["quality_flag"] = xobj_group["L1B_quality_flag"]
xobj_band2["degraded_channels"] = xobj_group["degraded_channels"]
xobj_band3["latitude"] = xobj_group["aws_lat"][:, :, 2]
xobj_band3["latitude"] = xobj_group["aws_lat"][:, :, 2]
xobj_band3["longitude"] = xobj_group["aws_lon"][:, :, 2]
xobj_band3["solar_zenith_angle"] = xobj_group["aws_solar_zenith_angle"][:, :, 2]
xobj_band3["solar_azimuth_angle"] = xobj_group["aws_solar_azimuth_angle"][
:, :, 2
]
xobj_band3["satellite_zenith_angle"] = xobj_group["aws_satellite_zenith_angle"][
:, :, 2
]
xobj_band3["satellite_azimuth_angle"] = xobj_group[
"aws_satellite_azimuth_angle"
][:, :, 2]
xobj_band3["quality_flag"] = xobj_group["L1B_quality_flag"]
xobj_band3["degraded_channels"] = xobj_group["degraded_channels"]
xobj_band4["latitude"] = xobj_group["aws_lat"][:, :, 3]
xobj_band4["latitude"] = xobj_group["aws_lat"][:, :, 3]
xobj_band4["longitude"] = xobj_group["aws_lon"][:, :, 3]
xobj_band4["solar_zenith_angle"] = xobj_group["aws_solar_zenith_angle"][:, :, 3]
xobj_band4["solar_azimuth_angle"] = xobj_group["aws_solar_azimuth_angle"][
:, :, 3
]
xobj_band4["satellite_zenith_angle"] = xobj_group["aws_satellite_zenith_angle"][
:, :, 3
]
xobj_band4["satellite_azimuth_angle"] = xobj_group[
"aws_satellite_azimuth_angle"
][:, :, 3]
xobj_band4["quality_flag"] = xobj_group["L1B_quality_flag"]
xobj_band4["degraded_channels"] = xobj_group["degraded_channels"]
# add attributes to each band (will make adjustments on roi etc later)
geoips_attrs["interpolation_radius_of_influence"] = self.ROIs["Band1"]
xobj_band1.attrs = geoips_attrs.copy()
geoips_attrs["interpolation_radius_of_influence"] = self.ROIs["Band2"]
xobj_band2.attrs = geoips_attrs.copy()
geoips_attrs["interpolation_radius_of_influence"] = self.ROIs["Band3"]
xobj_band3.attrs = geoips_attrs.copy()
geoips_attrs["interpolation_radius_of_influence"] = self.ROIs["Band4"]
xobj_band4.attrs = geoips_attrs.copy()
return {
"AWS_Band1": xobj_band1,
"AWS_Band2": xobj_band2,
"AWS_Band3": xobj_band3,
"AWS_Band4": xobj_band4,
"METADATA": meta,
}
PLUGIN_CLASS = AwsNetcdfReaderPlugin
