Source code for satpy.readers.ici_l1b_nc

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2022 Satpy developers
# satpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# satpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with satpy.  If not, see <>.

"""EUMETSAT EPS-SG Ice Cloud Imager (ICI) Level 1B products reader.

The format is explained in the
`EPS-SG ICI Level 1B Product Format Specification V3A`_.

This version is applicable for the ici test data released in Jan 2021.

.. _EPS-SG ICI Level 1B Product Format Specification V3A:


import datetime as dt
import logging
from enum import Enum
from functools import cached_property

import dask.array as da
import numpy as np
import xarray as xr
from geotiepoints.geointerpolator import GeoInterpolator

from satpy.readers.netcdf_utils import NetCDF4FileHandler

logger = logging.getLogger(__name__)

C1 = 1.191042e-5  # [mW/(sr·m2·cm-4)]
C2 = 1.4387752  # [K·cm]
MEAN_EARTH_RADIUS = 6371008.7714  # [m]

[docs] class InterpolationType(Enum): """Enum for interpolation types.""" LONLAT = 0 SOLAR_ANGLES = 1 OBSERVATION_ANGLES = 2
[docs] class IciL1bNCFileHandler(NetCDF4FileHandler): """Reader class for ICI L1B products in netCDF format.""" def __init__(self, filename, filename_info, filetype_info, **kwargs): """Read the calibration data and prepare the class for dataset reading.""" # noqa: E501 super().__init__( filename, filename_info, filetype_info, auto_maskandscale=True, ) # Read the variables which are required for the calibration measurement = "data/measurement_data" self._bt_conversion_a = self[f"{measurement}/bt_conversion_a"].values self._bt_conversion_b = self[f"{measurement}/bt_conversion_b"].values self._channel_cw = self[f"{measurement}/centre_wavenumber"].values self._n_samples = self[measurement].n_samples.size self._filetype_info = filetype_info self.orthorect = filetype_info.get("orthorect", True) @property def start_time(self): """Get observation start time.""" try: start_time = dt.datetime.strptime( self["/attr/sensing_start_time_utc"], "%Y%m%d%H%M%S.%f", ) except ValueError: start_time = dt.datetime.strptime( self["/attr/sensing_start_time_utc"], "%Y-%m-%d %H:%M:%S.%f", ) return start_time @property def end_time(self): """Get observation end time.""" try: end_time = dt.datetime.strptime( self["/attr/sensing_end_time_utc"], "%Y%m%d%H%M%S.%f", ) except ValueError: end_time = dt.datetime.strptime( self["/attr/sensing_end_time_utc"], "%Y-%m-%d %H:%M:%S.%f", ) return end_time @property def platform_name(self): """Return platform name.""" return self["/attr/spacecraft"] @property def sensor(self): """Return sensor.""" return self["/attr/instrument"] @property def ssp_lon(self): """Return subsatellite point longitude.""" # This parameter is not applicable to ICI? return None @property def observation_azimuth(self): """Get observation azimuth angles.""" observation_azimuth, _ = self.observation_azimuth_and_zenith return observation_azimuth @property def observation_zenith(self): """Get observation zenith angles.""" _, observation_zenith = self.observation_azimuth_and_zenith return observation_zenith @property def solar_azimuth(self): """Get solar azimuth angles.""" solar_azimuth, _ = self.solar_azimuth_and_zenith return solar_azimuth @property def solar_zenith(self): """Get solar zenith angles.""" _, solar_zenith = self.solar_azimuth_and_zenith return solar_zenith @property def longitude(self): """Get longitude coordinates.""" longitude, _ = self.longitude_and_latitude return longitude @property def latitude(self): """Get latitude coordinates.""" _, latitude = self.longitude_and_latitude return latitude @cached_property def observation_azimuth_and_zenith(self): """Get observation azimuth and zenith angles.""" return self._interpolate(InterpolationType.OBSERVATION_ANGLES) @cached_property def solar_azimuth_and_zenith(self): """Get solar azimuth and zenith angles.""" return self._interpolate(InterpolationType.SOLAR_ANGLES) @cached_property def longitude_and_latitude(self): """Get longitude and latitude coordinates.""" return self._interpolate(InterpolationType.LONLAT)
[docs] @staticmethod def _interpolate_geo( longitude, latitude, n_samples, ): """Perform the interpolation of geographic coordinates from tie points to pixel points. Args: longitude: xarray DataArray containing the longitude dataset to interpolate. latitude: xarray DataArray containing the longitude dataset to interpolate. n_samples: int describing number of samples per scan to interpolate onto. Returns: tuple of arrays containing the interpolate values, all the original metadata and the updated dimension names. """ third_dim_name = longitude.dims[2] horns = longitude[third_dim_name] n_scan = longitude.n_scan n_subs = longitude.n_subs lons = da.zeros((n_scan.size, n_samples, horns.size)) lats = da.zeros((n_scan.size, n_samples, horns.size)) n_subs = np.append( np.arange(0, n_samples, np.ceil(n_samples / n_subs.size)), n_samples - 1 ).astype(int) for horn in horns.values: satint = GeoInterpolator( (longitude.values[:, :, horn], latitude.values[:, :, horn]), (n_scan.values, n_subs), (n_scan.values, np.arange(n_samples)), ) lons_horn, lats_horn = satint.interpolate() lons[:, :, horn] = lons_horn lats[:, :, horn] = lats_horn dims = ["y", "x", third_dim_name] lon = xr.DataArray( lons, attrs=longitude.attrs, dims=dims, coords={third_dim_name: horns}, ) lat = xr.DataArray( lats, attrs=latitude.attrs, dims=dims, coords={third_dim_name: horns}, ) return lon, lat
[docs] def _interpolate_viewing_angle( self, azimuth, zenith, n_samples, ): """Perform the interpolation of angular coordinates from tie points to pixel points. Args: azimuth: xarray DataArray containing the azimuth angle dataset to interpolate. zenith: xarray DataArray containing the zenith angle dataset to interpolate. n_samples: int describing number of samples per scan to interpolate onto. Returns: tuple of arrays containing the interpolate values, all the original metadata and the updated dimension names. """ # interpolate onto spherical coords system with origin at equator azimuth, zenith = self._interpolate_geo(azimuth, 90. - zenith, n_samples) # transform back such that the origin is at the north pole zenith = 90. - zenith return azimuth, zenith
[docs] def _interpolate( self, interpolation_type, ): """Interpolate from tie points to pixel points.""" try: if interpolation_type is InterpolationType.SOLAR_ANGLES: var_key1 = self.filetype_info["solar_azimuth"] var_key2 = self.filetype_info["solar_zenith"] interp_method = self._interpolate_viewing_angle elif interpolation_type is InterpolationType.OBSERVATION_ANGLES: var_key1 = self.filetype_info["observation_azimuth"] var_key2 = self.filetype_info["observation_zenith"] interp_method = self._interpolate_viewing_angle else: var_key1 = self.filetype_info["longitude"] var_key2 = self.filetype_info["latitude"] interp_method = self._interpolate_geo return interp_method( self[var_key1], self[var_key2], self._n_samples, ) except KeyError: logger.warning(f"Datasets for {} interpolation not correctly defined in YAML file") # noqa: E501 return None, None
[docs] @staticmethod def _calibrate_bt(radiance, cw, a, b): """Perform the calibration to brightness temperature. Args: radiance: xarray DataArray or numpy ndarray containing the radiance values. cw: center wavenumber [cm-1]. a: temperature coefficient [-]. b: temperature coefficient [K]. Returns: DataArray: array containing the calibrated brightness temperature values. """ return b + (a * C2 * cw / np.log(1 + C1 * cw ** 3 / radiance))
[docs] def _calibrate(self, variable, dataset_info): """Perform the calibration. Args: variable: xarray DataArray containing the dataset to calibrate. dataset_info: dictionary of information about the dataset. Returns: DataArray: array containing the calibrated values and all the original metadata. """ calibration_name = dataset_info["calibration"] if calibration_name == "brightness_temperature": chan_index = dataset_info["chan_index"] cw = self._channel_cw[chan_index] a = self._bt_conversion_a[chan_index] b = self._bt_conversion_b[chan_index] calibrated_variable = self._calibrate_bt(variable, cw, a, b) calibrated_variable.attrs = variable.attrs elif calibration_name == "radiance": calibrated_variable = variable else: raise ValueError("Unknown calibration %s for dataset %s" % (calibration_name, dataset_info["name"])) # noqa: E501 return calibrated_variable
[docs] def _orthorectify(self, variable, orthorect_data_name): """Perform the orthorectification. Args: variable: xarray DataArray containing the dataset to correct for orthorectification. orthorect_data_name: name of the orthorectification correction data in the product. Returns: DataArray: array containing the corrected values and all the original metadata. """ try: # Convert the orthorectification delta values from meters to # degrees based on the simplified formula using mean Earth radius orthorect_data = self[orthorect_data_name] dim = self._get_third_dimension_name(orthorect_data) orthorect_data = orthorect_data.sel({dim: variable[dim]}) variable += np.degrees(orthorect_data.values / MEAN_EARTH_RADIUS) except KeyError: logger.warning("Required dataset %s for orthorectification not available, skipping", orthorect_data_name) # noqa: E501 return variable
[docs] @staticmethod def _standardize_dims(variable): """Standardize dims to y, x.""" if "n_scan" in variable.dims: variable = variable.rename({"n_scan": "y"}) if "n_samples" in variable.dims: variable = variable.rename({"n_samples": "x"}) if variable.dims[0] == "x": variable = variable.transpose("y", "x") return variable
[docs] def _filter_variable(self, variable, dataset_info): """Filter variable in the third dimension.""" dim = self._get_third_dimension_name(variable) if dim is not None and dim in dataset_info: variable = variable.sel({dim: dataset_info[dim]}) return variable
[docs] @staticmethod def _drop_coords(variable): """Drop coords that are not in dims.""" for coord in variable.coords: if coord not in variable.dims: variable = variable.drop_vars(coord) return variable
[docs] @staticmethod def _get_third_dimension_name(variable): """Get name of the third dimension of the variable.""" dims = variable.dims if len(dims) < 3: return None return dims[2]
[docs] def _fetch_variable(self, var_key): """Fetch variable.""" if var_key in [ "longitude", "latitude", "observation_zenith", "observation_azimuth", "solar_zenith", "solar_azimuth", ] and getattr(self, var_key) is not None: variable = getattr(self, var_key).copy() else: variable = self[var_key] return variable
[docs] def get_dataset(self, dataset_id, dataset_info): """Get dataset using file_key in dataset_info.""" var_key = dataset_info["file_key"] logger.debug(f"Reading in file to get dataset with key {var_key}.") try: variable = self._fetch_variable(var_key) except KeyError: logger.warning(f"Could not find key {var_key} in NetCDF file, no valid Dataset created") # noqa: E501 return None variable = self._filter_variable(variable, dataset_info) if dataset_info.get("calibration") is not None: variable = self._calibrate(variable, dataset_info) if self.orthorect: orthorect_data_name = dataset_info.get("orthorect_data", None) if orthorect_data_name is not None: variable = self._orthorectify(variable, orthorect_data_name) variable = self._manage_attributes(variable, dataset_info) variable = self._drop_coords(variable) variable = self._standardize_dims(variable) return variable
[docs] def _manage_attributes(self, variable, dataset_info): """Manage attributes of the dataset.""" variable.attrs.setdefault("units", None) variable.attrs.update(dataset_info) variable.attrs.update(self._get_global_attributes()) return variable
[docs] def _get_global_attributes(self): """Create a dictionary of global attributes.""" return { "filename": self.filename, "start_time": self.start_time, "end_time": self.end_time, "spacecraft_name": self.platform_name, "ssp_lon": self.ssp_lon, "sensor": self.sensor, "filename_start_time": self.filename_info["sensing_start_time"], "filename_end_time": self.filename_info["sensing_end_time"], "platform_name": self.platform_name, "quality_group": self._get_quality_attributes(), }
[docs] def _get_quality_attributes(self): """Get quality attributes.""" quality_group = self["quality"] quality_dict = {} for key in quality_group: # Add the values (as Numpy array) of each variable in the group # where possible try: quality_dict[key] = quality_group[key].values except ValueError: quality_dict[key] = None # Add the attributes of the quality group quality_dict.update(quality_group.attrs) return quality_dict