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High rate SEVIRI Level 1.5 Image Data MSG - Data Access

This notebook demonstrates how to access SEVIRI data using HDA and how to read, process and visualize it.

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Prerequisites:References:Credit:
This notebook uses Satpy
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Licensed under PyGNU GPL v3

This notebook demonstrates how to search and access MSG data using HDA and how to read, process and visualize it using satpy

The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) is MSG’s primary instrument and has the capacity to observe the Earth in 12 spectral channels.

Throughout this notebook, you will learn:

  1. Authenticate: How to authenticate for searching and access DEDL collections.

  2. Search: How to search DEDL data using datetime and bbox filters.

  3. Download: How to download DEDL data through HDA.

  4. Read and visualize MSG data: How to load process and visualize MSG data using Satpy.

Authenticate

pip install --user --upgrade --quiet destinelab
Note: you may need to restart the kernel to use updated packages.

import destinelab as deauth
import requests
import json
import os
import zipfile
import datetime
import shutil
from getpass import getpass
DESP_USERNAME = input("Please input your DESP username or email: ")
DESP_PASSWORD = getpass("Please input your DESP password: ")

auth = deauth.AuthHandler(DESP_USERNAME, DESP_PASSWORD)
access_token = auth.get_token()
if access_token is not None:
    print("DEDL/DESP Access Token Obtained Successfully")
else:
    print("Failed to Obtain DEDL/DESP Access Token")

auth_headers = {"Authorization": f"Bearer {access_token}"}
Please input your DESP username or email:  eum-dedl-user
Please input your DESP password:  ········

DEDL/DESP Access Token Obtained Successfully

Response code: 200
DEDL/DESP Access Token Obtained Successfully

HDA endpoint

HDA API is based on the Spatio Temporal Asset Catalog specification (STAC), it is convenient define a costant with its endpoint.

HDA_STAC_ENDPOINT="https://hda.data.destination-earth.eu/stac/v2"
COLLECTION_ID = "EO.EUM.DAT.MSG.HRSEVIRI"
response = requests.post(HDA_STAC_ENDPOINT+"/search", headers=auth_headers, json={
    "collections": [COLLECTION_ID],
    "datetime": "2023-08-06T08:00:00Z/2023-08-07T00:00:00Z"
})

from IPython.display import JSON

product = response.json()["features"][0]
JSON(product)

Download

from tqdm import tqdm
import time

# Define a list of assets to download
#assets = ["Oa08_radiance.nc", "Oa06_radiance.nc", "Oa02_radiance.nc"]
assets = ["downloadLink"]

for asset in assets:
    download_url = product["assets"][asset]["href"]
    print(download_url)
    filename = asset
    print(filename)
    response = requests.get(download_url, headers=auth_headers)
    total_size = int(response.headers.get("content-length", 0))

    print(f"downloading {filename}")

    with tqdm(total=total_size, unit="B", unit_scale=True) as progress_bar:
        with open(filename, 'wb') as f:
            for data in response.iter_content(1024):
                progress_bar.update(len(data))
                f.write(data)
https://hda-download.lumi.data.destination-earth.eu/data/eumetsat/EO.EUM.DAT.MSG.HRSEVIRI/MSG3-SEVI-MSG15-0100-NA-20230806081241.839000000Z-NA/downloadLink
downloadLink
downloading downloadLink

174MB [00:01, 109MB/s] 

zf=zipfile.ZipFile(filename)
with zipfile.ZipFile(filename, 'r') as zip_ref:
    zip_ref.extractall('.')

Satpy

The Python package satpy supports reading and loading data from many input files. For MSG data and the Native format, we can use the satpy reader 'seviri_l1b_native.

pip install --upgrade --quiet satpy dask distributed
Note: you may need to restart the kernel to use updated packages.

Import required libraries

from packaging.version import Version
import satpy
from satpy.scene import Scene

print(satpy.__version__)
if Version(satpy.__version__) < Version("0.57"):
    from satpy.composites import GenericCompositor
    from satpy.writers import to_image
    from satpy.resample import get_area_def
elif Version(satpy.__version__) == Version("0.57"):
    from satpy.composites import GenericCompositor
    from satpy.area import get_area_def
else:
    from satpy.composites.core import GenericCompositor
    from satpy.area import get_area_def
from satpy import available_readers

import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter(action = "ignore", category = RuntimeWarning)
0.60.0

file='MSG3-SEVI-MSG15-0100-NA-20230806081241.839000000Z-NA.nat'

Read and load data

We use the Scene constructor from the satpy library, a Scene object represents a single geographic region of data. Once loaded we can list all the available bands (spectral channel) for that scene.

# read the file
scn = Scene(reader='seviri_l1b_native', filenames=[file])
# print available datasets
scn.available_dataset_names()
['HRV', 'IR_016', 'IR_039', 'IR_087', 'IR_097', 'IR_108', 'IR_120', 'IR_134', 'VIS006', 'VIS008', 'WV_062', 'WV_073']

With the function load(), you can specify an individual band by name. If you then select the loaded band, you see the xarray.DataArray band object

# load bands 
scn.load(['HRV','IR_108'])
scn['IR_108']
Loading...
scn['IR_108'].attrs.keys()
dict_keys(['orbital_parameters', 'units', 'wavelength', 'standard_name', 'platform_name', 'sensor', 'georef_offset_corrected', 'time_parameters', 'start_time', 'end_time', 'reader', 'area', 'name', 'resolution', 'calibration', 'modifiers', '_satpy_id', 'ancillary_variables'])
scn['IR_108'].attrs['wavelength']
WavelengthRange(min=9.8, central=10.8, max=11.8, unit='µm')

Visualize data combining bands

The Satpy function available_composite_ids() returns a list of available composite IDs.

scn = Scene(reader="seviri_l1b_native", 
             filenames=[file])
scn.available_composite_ids()
[DataID(name='24h_microphysics'), DataID(name='airmass'), DataID(name='ash'), DataID(name='cloud_phase_distinction'), DataID(name='cloud_phase_distinction_raw'), DataID(name='cloudtop'), DataID(name='cloudtop_daytime'), DataID(name='colorized_ir_clouds'), DataID(name='convection'), DataID(name='day_microphysics'), DataID(name='day_microphysics_winter'), DataID(name='day_severe_storms'), DataID(name='day_severe_storms_tropical'), DataID(name='dust'), DataID(name='fog'), DataID(name='green_snow'), DataID(name='hrv_clouds'), DataID(name='hrv_fog'), DataID(name='hrv_severe_storms'), DataID(name='hrv_severe_storms_masked'), DataID(name='ir108_3d'), DataID(name='ir_cloud_day'), DataID(name='ir_overview'), DataID(name='ir_sandwich'), DataID(name='natural_color'), DataID(name='natural_color_nocorr'), DataID(name='natural_color_raw'), DataID(name='natural_color_raw_with_night_ir'), DataID(name='natural_color_with_night_ir'), DataID(name='natural_color_with_night_ir_hires'), DataID(name='natural_enh'), DataID(name='natural_enh_with_night_ir'), DataID(name='natural_enh_with_night_ir_hires'), DataID(name='natural_with_night_fog'), DataID(name='night_fog'), DataID(name='night_ir_alpha'), DataID(name='night_ir_with_background'), DataID(name='night_ir_with_background_hires'), DataID(name='night_microphysics'), DataID(name='night_microphysics_tropical'), DataID(name='overshooting_tops'), DataID(name='overview'), DataID(name='overview_raw'), DataID(name='realistic_colors'), DataID(name='rocket_plume_day'), DataID(name='rocket_plume_night'), DataID(name='snow'), DataID(name='vis_sharpened_ir')]
composite_id = ["natural_color"]
scn.load(composite_id, upper_right_corner="NE")

scn_cropped = scn.crop(ll_bbox=(-5, 31, 20, 51))
scn_cropped.show("natural_color")
<trollimage.xrimage.XRImage at 0x7f0c303305d0>

composite_id = ["colorized_ir_clouds"]
scn.load(composite_id, upper_right_corner="NE")
scn_cropped = scn.crop(ll_bbox=(-5, 31, 20, 51))
scn_cropped.show("colorized_ir_clouds")
<trollimage.xrimage.XRImage at 0x7f0c2d5e97d0>