Source code for satpy.readers.iasi_l2_so2_bufr

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2019 Satpy developers
# This file is part of satpy.
# 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 MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE.  See the 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 <>.

r"""IASI L2 SO2 BUFR format reader.

The ``iasi_l2_so2_bufr`` reader reads IASI level2 SO2 data in BUFR format. The algorithm is described in the
Theoretical Basis Document, linked below.

Each BUFR file consists of a number of messages, one for each scan, each of which contains SO2 column amounts
in Dobson units for retrievals performed with plume heights of 7, 10, 13, 16 and 25 km.

Reader Arguments
A list of retrieval files, fnames, can be opened as follows::

  Scene(reader="iasi_l2_so2_bufr", filenames=fnames)

Here is an example how to read the data in satpy:

.. code-block:: python

    from satpy import Scene
    import glob

    filenames = glob.glob(
    scn = Scene(filenames=filenames, reader='iasi_l2_so2_bufr')
    scn.load(['so2_height_3', 'so2_height_4'])


.. code-block:: none

    <xarray.DataArray 'so2_height_3' (y: 23, x: 120)>
    dask.array<where, shape=(23, 120), dtype=float64, chunksize=(1, 120), chunktype=numpy.ndarray>
        crs      object +proj=latlong +datum=WGS84 +ellps=WGS84 +type=crs
    Dimensions without coordinates: y, x
        sensor:               IASI
        units:                dobson
        file_type:            iasi_l2_so2_bufr
        wavelength:           None
        modifiers:            ()
        platform_name:        METOP-2
        resolution:           12000
        fill_value:           -1e+100
        level:                None
        polarization:         None
        coordinates:          ('longitude', 'latitude')
        calibration:          None
        key:                  #3#sulphurDioxide
        name:                 so2_height_3
        start_time:           2020-02-04 09:14:55
        end_time:             2020-02-04 09:17:51
        area:                 Shape: (23, 120)\nLons: <xarray.DataArray 'longitud...
        ancillary_variables:  []

Algorithm Theoretical Basis Document:

# TDB: this reader is based on and

import datetime as dt
import logging

import dask.array as da
import numpy as np
import xarray as xr

    import eccodes as ec
except ImportError as e:
    raise ImportError(
        """Missing eccodes-python and/or eccodes C-library installation. Use conda to install eccodes.
           Error: """, e)

from satpy.readers.file_handlers import BaseFileHandler
from satpy.utils import get_legacy_chunk_size

logger = logging.getLogger("IASIL2SO2BUFR")
CHUNK_SIZE = get_legacy_chunk_size()
data_center_dict = {3: "METOP-1", 4: "METOP-2", 5: "METOP-3"}

[docs] class IASIL2SO2BUFR(BaseFileHandler): """File handler for the IASI L2 SO2 BUFR product.""" def __init__(self, filename, filename_info, filetype_info, **kwargs): """Initialise the file handler for the IASI L2 SO2 BUFR data.""" super(IASIL2SO2BUFR, self).__init__(filename, filename_info, filetype_info) start_time, end_time = self.get_start_end_date() sc_id = self.get_attribute("satelliteIdentifier") self.metadata = {} self.metadata["start_time"] = start_time self.metadata["end_time"] = end_time self.metadata["SpacecraftName"] = data_center_dict[sc_id] @property def start_time(self): """Return the start time of data acqusition.""" return self.metadata["start_time"] @property def end_time(self): """Return the end time of data acquisition.""" return self.metadata["end_time"] @property def platform_name(self): """Return spacecraft name.""" return "{}".format(self.metadata["SpacecraftName"])
[docs] def get_start_end_date(self): """Get the first and last date from the bufr file.""" fh = open(self.filename, "rb") i = 0 while True: # get handle for message bufr = ec.codes_bufr_new_from_file(fh) if bufr is None: break ec.codes_set(bufr, "unpack", 1) year = ec.codes_get(bufr, "year") month = ec.codes_get(bufr, "month") day = ec.codes_get(bufr, "day") hour = ec.codes_get(bufr, "hour") minute = ec.codes_get(bufr, "minute") second = ec.codes_get(bufr, "second") obs_time = dt.datetime(year=year, month=month, day=day, hour=hour, minute=minute, second=second) if i == 0: start_time = obs_time ec.codes_release(bufr) i += 1 end_time = obs_time fh.close() return start_time, end_time
[docs] def get_attribute(self, key): """Get BUFR attributes.""" # This function is inefficient as it is looping through the entire # file to get 1 attribute. It causes a problem though if you break # from the file early - dont know why but investigating - fix later fh = open(self.filename, "rb") while True: # get handle for message bufr = ec.codes_bufr_new_from_file(fh) if bufr is None: break ec.codes_set(bufr, "unpack", 1) attr = ec.codes_get(bufr, key) ec.codes_release(bufr) fh.close() return attr
[docs] def get_array(self, key): """Get all data from file for the given BUFR key.""" with open(self.filename, "rb") as fh: msgCount = 0 while True: bufr = ec.codes_bufr_new_from_file(fh) if bufr is None: break ec.codes_set(bufr, "unpack", 1) values = ec.codes_get_array( bufr, key, float) if len(values) == 1: values = np.repeat(values, 120) # if is the first message initialise our final array if (msgCount == 0): arr = da.from_array([values], chunks=CHUNK_SIZE) else: tmpArr = da.from_array([values], chunks=CHUNK_SIZE) arr = da.concatenate((arr, tmpArr), axis=0) msgCount = msgCount+1 ec.codes_release(bufr) if arr.size == 1: arr = arr[0] return arr
[docs] def get_dataset(self, dataset_id, dataset_info): """Get dataset using the BUFR key in dataset_info.""" arr = self.get_array(dataset_info["key"]) arr[arr == dataset_info["fill_value"]] = np.nan xarr = xr.DataArray(arr, dims=["y", "x"], name=dataset_info["name"]) xarr.attrs["sensor"] = "IASI" xarr.attrs["platform_name"] = self.platform_name xarr.attrs.update(dataset_info) return xarr