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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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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.

Documentation DestinE Data Lake HDA s High Rate SEVIRI Level 1.5 Image Data - MSG - 0 degree

Authenticate on DESP

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:  ········

Response code: 200
DEDL/DESP Access Token Obtained Successfully

Search MSG data

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

from IPython.display import JSON

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

Download MSG

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.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.HRSEVIRI/items/MSG3-SEVI-MSG15-0100-NA-20230806081241.839000000Z-NA/download?provider=eumetsat
downloadLink
downloading downloadLink

100%|██████████| 174M/174M [00:00<00:00, 600MB/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 --quiet satpy
Note: you may need to restart the kernel to use updated packages.

Import required libraries

from satpy.scene import Scene
from satpy.composites import GenericCompositor
from satpy.writers import to_image
from satpy.resample import get_area_def
from satpy import available_readers

import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter(action = "ignore", category = RuntimeWarning)
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='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='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 0x7f8f70c2f6d0>

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 0x7f8e2b28d4d0>