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Data access and analysis of Metop-A/B/C Level 3 trace gas products relevant for wild firesĀ¶

Julian Meyer-Arnek (German Aerospace Center, DLR-DFD)

Abstract:

Scientific question: Determine tropospheric NO$_2$ that was released during the severe wild fires on the Iberian Peninsula in August 2025.

This Jupyter Notebook demonstrates how to

  • identify Level 3 data collections suitable for this context,
  • connect to a STAC service endpoint,
  • identify periods using STAC (discovery),
  • get the identified datasets into a locally available object (XArray),
  • let the server do any geographic extraction,
  • perform visualization and analysis.

This Jupyter Notebook was created for the "EUMeTrain Event Week on Forest Fires - June 2026" (https://www.eumetrain.org/event-weeks/event-week-forest-fires-june-2026)

Python importsĀ¶

InĀ [1]:
from pystac_client import Client
InĀ [2]:
from odc.stac import stac_load
import xarray as xr
InĀ [3]:
import numpy
InĀ [4]:
import math
InĀ [5]:
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
InĀ [6]:
import cartopy

Data discovery using STACĀ¶

STAC (SpatioTemporal Asset Catalogue) (https://spacspec.org) is a

  • metadata standard as well as a
  • metadata service.

What does this mean?

STAC defines

  • the metadata and
  • how the metadata is structured.

Secondly

  • STAC describes how the web interface needs to interact with clients in order to support STAC-specific queries on datasets.

And last but not least:

  • STAC client software (as used here in this notebook) is needed to
    • communicate with the STAC web interface and to
    • properly interpret the STAC metadata

In the recent past (2020-2026), STAC became a widely used standard for metadata management. Numerous data centers support discovery and access of their data holdings via STAC protocol and infrastructure. There are numerous STAC catalogue services available: For an overview just visit https://stacindex.org.

Find more about STAC and its specifications here: https://stacspec.org.

InĀ [7]:
# STAC endpoint
stacapi_endpoint = "https://geoservice.dlr.de/eoc/ogc/stac/v1/"

STAC API-HierarchyĀ¶

STAC organizes the metadata in an hierarchical way:

  • Collections: Group of similar products (timeseries)
    • Items: Individual (daily) products
      • Assets: https-Links to Datasets

First step in order to identify suitable data collections for your specific task:

Visit https://geoservice.dlr.de/eoc/ogc/stac/v1 with your browser. See a screenshot of the human-consumable representation of STAC metadata below.

Just browse the available data collections. For the purpose of this analysis, the focus is on STAC collections containing tropospheric NO2 derived from the Metop satellites.

Visual representation of STAC collection

This is the identical data collection when using the machine-to-machine interface providing pure STAC data:

{"type":"Collection","stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json","https://stac-extensions.github.io/web-map-links/v1.2.0/schema.json"],"id":"METOP_GOME2_L3_P1D_NO2TROPO","title":"MetOp-A/B/C GOME-2 L3 P1D Tropspheric Nitrogen Dioxide (NO2Tropo)","description":"MetOp-A/B/C GOME-2 L3 P1D data of Tropspheric Nitrogen Dioxide (NO2Tropo) with a global coverage.","crs":["http://www.opengis.net/def/crs/OGC/1.3/CRS84"],"keywords":["EOC","Atmos","GOME-2","MetOp","M....

Now let's dive into the topicĀ¶

At first a basic set of information is defined.

Required information is taken fron the EOC-Geoservice-STAC service web page (https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO):

  • collections: Identifier of the Metop-tropospheric NO2-data collection.
  • platforms: "Metop-C"

The geographic bounding box focuses the Iberian Peninsula.

InĀ [8]:
# Now we make some mission specific settings
collections = ["METOP_GOME2_L3_P1D_NO2TROPO"]
platform_b = "MetOp-B"
platform_c = "MetOp-C"
resolution=0.25 # original product resolution
InĀ [9]:
# Download the complete year
dates_from_to = ["2025-08-01", "2025-08-31"]
InĀ [10]:
# Area of interest is Iberian Pensinsula
bbox = [-60, 0, 30, 60] # W/S/E/N
First interaction with STAC: Connect to the STAC catalog.

This is just a "connect", no data is going to be downloaded.

InĀ [11]:
catalog = Client.open(
    url=stacapi_endpoint
)

Identify (discover) available datasetsĀ¶

Data discovery (catalog search) is performed according to our settings
InĀ [12]:
%%time

filter="platform='" + platform_c + "'"
stac_items_metopc = catalog.search(
    collections=collections, 
    datetime=dates_from_to, 
    method="GET", 
    filter=filter,
    max_items=1000
).item_collection()

CPU times: user 54.6 ms, sys: 2.45 ms, total: 57.1 ms
Wall time: 2.74 s
InĀ [13]:
stac_items_metopc
Out[13]:
<pystac.item_collection.ItemCollection object at 0x70620c6192b0>
  • type "FeatureCollection"
  • features[] 33 items
    • 0
      • type "Feature"
      • stac_version "1.1.0"
      • stac_extensions[] 3 items
        • 0 "https://stac-extensions.github.io/projection/v2.0.0/schema.json"
        • 1 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"
        • 2 "https://stac-extensions.github.io/raster/v1.1.0/schema.json"
      • id "GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop"
      • geometry
        • type "Polygon"
        • coordinates[] 1 items
          • 0[] 5 items
            • 0[] 2 items
              • 0 -180
              • 1 -90
            • 1[] 2 items
              • 0 180
              • 1 -90
            • 2[] 2 items
              • 0 180
              • 1 90
            • 3[] 2 items
              • 0 -180
              • 1 90
            • 4[] 2 items
              • 0 -180
              • 1 -90
      • bbox[] 4 items
        • 0 -180.0
        • 1 -90.0
        • 2 180.0
        • 3 90.0
      • properties
        • created "2025-10-10T06:24:43.242Z"
        • updated "2025-10-10T06:24:43.242Z"
        • datetime "2025-09-01T12:13:09Z"
        • start_datetime "2025-08-31T23:58:09Z"
        • end_datetime "2025-09-02T00:28:09Z"
        • platform "MetOp-C"
        • constellation "MetOp"
        • instruments "GOME-2"
        • license "CC-BY 4.0"
        • processing:facility "DLR Deutsches Zentrum fuer Luft und Raumfahrt"
        • processing:level "L3"
        • processing:software
          • G2_L3_NO2 "v1"
        • proj:bbox[] 4 items
          • 0 -180.0
          • 1 -90.0
          • 2 180.0
          • 3 90.0
        • proj:shape[] 2 items
          • 0 720
          • 1 1440
        • proj:code "EPSG:4326"
      • links[] 17 items
        • 0
          • rel "collection"
          • href "METOP_GOME2_L3_P1D_NO2TROPO"
          • type "application/json"
        • 1
          • rel "self"
          • href "/ida/ac-saf/gome2c/level3/NO2/2025/09/01/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop.json"
          • type "application/json"
        • 2
          • rel "root"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/"
          • type "application/json"
          • title "EOC EO Products Service"
        • 3
          • rel "parent"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1"
          • type "application/json"
          • title "Parent"
        • 4
          • rel "self"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop"
          • type "application/geo%2Bjson"
          • title "Self"
        • 5
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop"
          • type "application/geo%2Bjson"
          • title "Item"
        • 6
          • rel "alternate"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop"
          • type "text/html"
          • title "Item as HTML"
        • 7
          • rel "items"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items"
          • type "application/geo%2Bjson"
          • title "Items"
        • 8
          • rel "items"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items"
          • type "text/html"
          • title "Items as HTML"
        • 9
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO"
          • type "application/json"
          • title "Collection"
        • 10
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO"
          • type "text/html"
          • title "Collection as HTML"
        • 11
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections"
          • type "application/json"
          • title "Collections"
        • 12
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections"
          • type "text/html"
          • title "Collections as HTML"
        • 13
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/oseo/search?parentIdentifier=METOP_GOME2_L3_P1D_NO2TROPO&identifier=GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop&httpAccept=application%2Fgeo%252Bjson"
          • type "application/geo+json"
          • title "OpenSearch GeoJSON"
        • 14
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/oseo/search?parentIdentifier=METOP_GOME2_L3_P1D_NO2TROPO&identifier=GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop&httpAccept=application%2Fatom%2Bxml"
          • type "application/atom+xml"
          • title "OpenSearch Atom XML"
        • 15
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/oseo/metadata?parentId=METOP_GOME2_L3_P1D_NO2TROPO&uid=GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop&httpAccept=application%2Fgml%2Bxml"
          • type "application/gml+xml"
          • title "OpenSearch O&M XML"
        • 16
          • rel "http://www.opengis.net/def/rel/ogc/1.0/queryables"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/collections/METOP_GOME2_L3_P1D_NO2TROPO/queryables"
          • type "application/schema+json"
          • title "Queryables"
      • assets
        • metadata
          • href "https://download.geoservice.dlr.de/METOP/files//2025/09/01/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop/GOME_NO2_L3_20250901_METOPC_DLR_v1.nc"
          • type "application/netcdf"
          • title "gome2c Metadata"
          • description "Original gome2c NO2Tropo NetCDF file. Can also be loaded as data file and contains important metadata information."
          • roles[] 2 items
            • 0 "metadata"
            • 1 "data"
        • no2trop_c
          • href "https://download.geoservice.dlr.de/METOP/files//2025/09/01/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop.tif"
          • type "image/tiff; application=geotiff; profile=cloud-optimized"
          • title "gome2c NO2Tropo"
          • description "MetOp-C/GOME-2 L3 data of Tropspheric Nitrogen Dioxide (NO2Tropo) with a global coverage."
          • raster:bands[] 1 items
            • 0
              • data_type "float32"
              • scale 1.0
              • offset 0.0
              • sampling "area"
              • nodata 9.969209968386869e+36
              • statistics
                • mean 303968762726051.8
                • minimum -4795724512362496.0
                • maximum 3.4633838885863424e+16
                • stddev 1096830685525026.1
                • valid_percent 24.70836639404297
              • histogram
                • count 11
                • min -4795724512362496.0
                • max 3.4633838885863424e+16
                • buckets[] 10 items
                  • 0 7923
                  • 1 119340
                  • 2 1957
                  • 3 226
                  • 4 56
                  • 5 18
                  • 6 11
                  • 7 4
                  • 8 3
                  • 9 5
          • eo:bands[] 1 items
            • 0
              • name "b1"
              • description "gray"
          • proj:shape[] 2 items
            • 0 720
            • 1 1440
          • proj:transform[] 9 items
            • 0 0.25
            • 1 0.0
            • 2 -180.0
            • 3 0.0
            • 4 -0.25
            • 5 90.0
            • 6 0.0
            • 7 0.0
            • 8 1.0
          • proj:geometry
            • type "Polygon"
            • coordinates[] 1 items
              • 0[] 5 items
                • 0[] 2 items
                  • 0 -180.0
                  • 1 -90.0
                • 1[] 2 items
                  • 0 180.0
                  • 1 -90.0
                • 2[] 2 items
                  • 0 180.0
                  • 1 90.0
                • 3[] 2 items
                  • 0 -180.0
                  • 1 90.0
                • 4[] 2 items
                  • 0 -180.0
                  • 1 -90.0
          • proj:bbox[] 4 items
            • 0 -180.0
            • 1 -90.0
            • 2 180.0
            • 3 90.0
          • proj:code "EPSG:4326"
          • roles[] 1 items
            • 0 "data"
        • thumbnail
          • href "https://download.geoservice.dlr.de/METOP/files//2025/09/01/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop/GOME_NO2_L3_20250901_METOPC_DLR_v1_no2trop_thumbnail.png"
          • type "image/png"
          • title "gome2c NO2Tropo Thumbnail"
          • description "Thumbnail of the gome2c NO2Tropo variable."
          • roles[] 1 items
            • 0 "thumbnail"
      • collection "METOP_GOME2_L3_P1D_NO2TROPO"
    • 1
      • type "Feature"
      • stac_version "1.1.0"
      • stac_extensions[] 3 items
        • 0 "https://stac-extensions.github.io/projection/v2.0.0/schema.json"
        • 1 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"
        • 2 "https://stac-extensions.github.io/raster/v1.1.0/schema.json"
      • id "GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop"
      • geometry
        • type "Polygon"
        • coordinates[] 1 items
          • 0[] 5 items
            • 0[] 2 items
              • 0 -180
              • 1 -90
            • 1[] 2 items
              • 0 180
              • 1 -90
            • 2[] 2 items
              • 0 180
              • 1 90
            • 3[] 2 items
              • 0 -180
              • 1 90
            • 4[] 2 items
              • 0 -180
              • 1 -90
      • bbox[] 4 items
        • 0 -180.0
        • 1 -90.0
        • 2 180.0
        • 3 90.0
      • properties
        • created "2025-10-10T06:15:36.186Z"
        • updated "2025-10-10T06:15:36.186Z"
        • datetime "2025-08-31T12:34:03Z"
        • start_datetime "2025-08-31T00:19:03Z"
        • end_datetime "2025-09-01T00:49:03Z"
        • platform "MetOp-C"
        • constellation "MetOp"
        • instruments "GOME-2"
        • license "CC-BY 4.0"
        • processing:facility "DLR Deutsches Zentrum fuer Luft und Raumfahrt"
        • processing:level "L3"
        • processing:software
          • G2_L3_NO2 "v1"
        • proj:bbox[] 4 items
          • 0 -180.0
          • 1 -90.0
          • 2 180.0
          • 3 90.0
        • proj:shape[] 2 items
          • 0 720
          • 1 1440
        • proj:code "EPSG:4326"
      • links[] 17 items
        • 0
          • rel "collection"
          • href "METOP_GOME2_L3_P1D_NO2TROPO"
          • type "application/json"
        • 1
          • rel "self"
          • href "/ida/ac-saf/gome2c/level3/NO2/2025/08/31/GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop.json"
          • type "application/json"
        • 2
          • rel "root"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/"
          • type "application/json"
          • title "EOC EO Products Service"
        • 3
          • rel "parent"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1"
          • type "application/json"
          • title "Parent"
        • 4
          • rel "self"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items/GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop"
          • type "application/geo%2Bjson"
          • title "Self"
        • 5
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items/GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop"
          • type "application/geo%2Bjson"
          • title "Item"
        • 6
          • rel "alternate"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items/GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop"
          • type "text/html"
          • title "Item as HTML"
        • 7
          • rel "items"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items"
          • type "application/geo%2Bjson"
          • title "Items"
        • 8
          • rel "items"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO/items"
          • type "text/html"
          • title "Items as HTML"
        • 9
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO"
          • type "application/json"
          • title "Collection"
        • 10
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/METOP_GOME2_L3_P1D_NO2TROPO"
          • type "text/html"
          • title "Collection as HTML"
        • 11
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections"
          • type "application/json"
          • title "Collections"
        • 12
          • rel "collection"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections"
          • type "text/html"
          • title "Collections as HTML"
        • 13
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/oseo/search?parentIdentifier=METOP_GOME2_L3_P1D_NO2TROPO&identifier=GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop&httpAccept=application%2Fgeo%252Bjson"
          • type "application/geo+json"
          • title "OpenSearch GeoJSON"
        • 14
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/oseo/search?parentIdentifier=METOP_GOME2_L3_P1D_NO2TROPO&identifier=GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop&httpAccept=application%2Fatom%2Bxml"
          • type "application/atom+xml"
          • title "OpenSearch Atom XML"
        • 15
          • rel "item"
          • href "https://geoservice.dlr.de/eoc/oseo/metadata?parentId=METOP_GOME2_L3_P1D_NO2TROPO&uid=GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop&httpAccept=application%2Fgml%2Bxml"
          • type "application/gml+xml"
          • title "OpenSearch O&M XML"
        • 16
          • rel "http://www.opengis.net/def/rel/ogc/1.0/queryables"
          • href "https://geoservice.dlr.de/eoc/ogc/stac/collections/METOP_GOME2_L3_P1D_NO2TROPO/queryables"
          • type "application/schema+json"
          • title "Queryables"
      • assets
        • metadata
          • href "https://download.geoservice.dlr.de/METOP/files//2025/08/31/GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop/GOME_NO2_L3_20250831_METOPC_DLR_v1.nc"
          • type "application/netcdf"
          • title "gome2c Metadata"
          • description "Original gome2c NO2Tropo NetCDF file. Can also be loaded as data file and contains important metadata information."
          • roles[] 2 items
            • 0 "metadata"
            • 1 "data"
        • no2trop_c
          • href "https://download.geoservice.dlr.de/METOP/files//2025/08/31/GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop/GOME_NO2_L3_20250831_METOPC_DLR_v1_no2trop.tif"
          • type "image/tiff; application=geotiff; profile=cloud-optimized"
          • title "gome2c NO2Tropo"
          • description "MetOp-C/GOME-2 L3 data of Tropspheric Nitrogen Dioxide (NO2Tropo) with a global coverage."
          • raster:bands[] 1 items
            • 0
              • data_type "float32"
              • scale 1.0
              • offset 0.0
              • sampling "area"
              • nodata 9.969209968386869e+36
              • statistics
                • mean 0.0
                • minimum 0.0
                • maximum 0.0
                • stddev 0.0
                • valid_percent 0.0
              • histogram
                • count 11
                • min 0.0
                • max 1.0
                • buckets[] 10 items
                  • 0 0
                  • 1 0
                  • 2 0
                  • 3 0
                  • 4 0
                  • 5 0
                  • 6 0
                  • 7 0
                  • 8 0
                  • 9 0
          • eo:bands[] 1 items
            • 0
              • name "b1"
              • description "gray"
          • proj:shape[] 2 items
            • 0 720
            • 1 1440
          • proj:transform[] 9 items
            • 0 0.25
            • 1 0.0
            • 2 -180.0
            • 3 0.0
            • 4 -0.25
            • 5 90.0
            • 6 0.0
            • 7 0.0
            • 8 1.0
          • proj:geometry
            • type "Polygon"
            • coordinates[] 1 items
              • 0[] 5 items
                • 0[] 2 items
                  • 0 -180.0
                  • 1 -90.0
                • 1[] 2 items
                  • 0 180.0
                  • 1 -90.0
                • 2[] 2 items
                  • 0 180.0
                  • 1 90.0
                • 3[] 2 items
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              • nodata 9.969209968386869e+36
              • statistics
                • mean 444056188501915.4
                • minimum -3762646901850112.0
                • maximum 4.336148730924237e+16
                • stddev 1234040599324587.8
                • valid_percent 22.08385467529297
              • histogram
                • count 11
                • min -3762646901850112.0
                • max 4.336148730924237e+16
                • buckets[] 10 items
                  • 0 94329
                  • 1 20673
                  • 2 579
                  • 3 131
                  • 4 36
                  • 5 24
                  • 6 4
                  • 7 5
                  • 8 1
                  • 9 1
          • eo:bands[] 1 items
            • 0
              • name "b1"
              • description "gray"
          • proj:shape[] 2 items
            • 0 720
            • 1 1440
          • proj:transform[] 9 items
            • 0 0.25
            • 1 0.0
            • 2 -180.0
            • 3 0.0
            • 4 -0.25
            • 5 90.0
            • 6 0.0
            • 7 0.0
            • 8 1.0
          • proj:geometry
            • type "Polygon"
            • coordinates[] 1 items
              • 0[] 5 items
                • 0[] 2 items
                  • 0 -180.0
                  • 1 -90.0
                • 1[] 2 items
                  • 0 180.0
                  • 1 -90.0
                • 2[] 2 items
                  • 0 180.0
                  • 1 90.0
                • 3[] 2 items
                  • 0 -180.0
                  • 1 90.0
                • 4[] 2 items
                  • 0 -180.0
                  • 1 -90.0
          • proj:bbox[] 4 items
            • 0 -180.0
            • 1 -90.0
            • 2 180.0
            • 3 90.0
          • proj:code "EPSG:4326"
          • roles[] 1 items
            • 0 "data"
        • thumbnail
          • href "https://download.geoservice.dlr.de/METOP/files//2025/07/31/GOME_NO2_L3_20250731_METOPC_DLR_v1_no2trop/GOME_NO2_L3_20250731_METOPC_DLR_v1_no2trop_thumbnail.png"
          • type "image/png"
          • title "gome2c NO2Tropo Thumbnail"
          • description "Thumbnail of the gome2c NO2Tropo variable."
          • roles[] 1 items
            • 0 "thumbnail"
      • collection "METOP_GOME2_L3_P1D_NO2TROPO"

--> Now we identified the relevant datasets which we need for our research.

Download discovered datasets to local storage (data cube)Ā¶

This is the second step. Up to now, only metadata was considered.

Now it is time to download the data of the identifed data sets to the local client.

You can concentrate on your work. All ugly work such as

  • re-projection or
  • area-slicing

is done on the server.

InĀ [17]:
%%time

no2trop_metopc = stac_load(
    stac_items_metopc,
    nodata=numpy.nan,
    bands=["no2trop_c"],
    # crs="EPSG:4326",
    resolution=0.125,
    lon=(bbox[0], bbox[2]),
    lat=(bbox[1], bbox[3]),)

print ("Data is extracted on the server and downloaded to local storage.")

Data is extracted on the server and downloaded to local storage.
CPU times: user 279 ms, sys: 3.65 ms, total: 283 ms
Wall time: 281 ms

This is a trick:

Since the masking of "undefined" values does not always work properly with the Python libraries used here, we assume that all values above 1e+36 are "undefined".

InĀ [26]:
no2trop_metopc = no2trop_metopc.where(no2trop_metopc <= 1e36, other=numpy.nan)

Representation of data cube (XArray object)Ā¶

Now the data of the identifed data sets is downloaded to the local client and is accessible as XArray object.
InĀ [27]:
no2trop_metopc
Out[27]:
<xarray.Dataset> Size: 47MB
Dimensions:      (time: 33, latitude: 480, longitude: 720)
Coordinates:
  * time         (time) datetime64[us] 264B 2025-07-31T12:21:39 ... 2025-09-0...
  * latitude     (latitude) float64 4kB 59.94 59.81 59.69 ... 0.1875 0.0625
  * longitude    (longitude) float64 6kB -59.94 -59.81 -59.69 ... 29.81 29.94
    spatial_ref  int32 4B 4326
Data variables:
    no2trop_c    (time, latitude, longitude) float32 46MB nan nan ... nan nan
    average      (latitude, longitude) float32 1MB 1.462e+14 ... 3.174e+14
xarray.Dataset
    • time: 33
    • latitude: 480
    • longitude: 720
    • time
      (time)
      datetime64[us]
      2025-07-31T12:21:39 ... 2025-09-...
      array(['2025-07-31T12:21:39.000000', '2025-08-01T12:51:29.500000',
       '2025-08-02T12:30:35.000000', '2025-08-03T12:09:40.500000',
       '2025-08-04T12:39:31.000000', '2025-08-05T12:18:40.000000',
       '2025-08-06T12:48:27.500000', '2025-08-07T12:27:36.000000',
       '2025-08-08T12:06:45.000000', '2025-08-09T12:36:32.500000',
       '2025-08-10T12:15:44.000000', '2025-08-11T12:45:32.000000',
       '2025-08-12T12:24:40.500000', '2025-08-13T12:03:49.000000',
       '2025-08-14T12:33:36.500000', '2025-08-15T12:12:48.000000',
       '2025-08-16T12:42:36.000000', '2025-08-17T12:21:44.500000',
       '2025-08-18T12:00:53.000000', '2025-08-19T12:30:44.000000',
       '2025-08-20T12:09:52.500000', '2025-08-21T12:39:43.000000',
       '2025-08-22T12:18:51.500000', '2025-08-23T11:58:00.000000',
       '2025-08-24T12:27:51.000000', '2025-08-25T12:06:59.500000',
       '2025-08-26T12:36:53.000000', '2025-08-27T12:15:59.000000',
       '2025-08-28T11:55:10.500000', '2025-08-29T12:25:01.000000',
       '2025-08-30T12:54:51.500000', '2025-08-31T12:34:03.000000',
       '2025-09-01T12:13:09.000000'], dtype='datetime64[us]')
    • latitude
      (latitude)
      float64
      59.94 59.81 59.69 ... 0.1875 0.0625
      units :
      degrees_north
      resolution :
      -0.125
      crs :
      EPSG:4326
      array([59.9375, 59.8125, 59.6875, ...,  0.3125,  0.1875,  0.0625], shape=(480,))
    • longitude
      (longitude)
      float64
      -59.94 -59.81 ... 29.81 29.94
      units :
      degrees_east
      resolution :
      0.125
      crs :
      EPSG:4326
      array([-59.9375, -59.8125, -59.6875, ...,  29.6875,  29.8125,  29.9375],
      shape=(720,))
    • spatial_ref
      ()
      int32
      4326
      spatial_ref :
      GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]
      crs_wkt :
      GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]
      semi_major_axis :
      6378137.0
      semi_minor_axis :
      6356752.314245179
      inverse_flattening :
      298.257223563
      reference_ellipsoid_name :
      WGS 84
      longitude_of_prime_meridian :
      0.0
      prime_meridian_name :
      Greenwich
      geographic_crs_name :
      WGS 84
      horizontal_datum_name :
      World Geodetic System 1984 ensemble
      grid_mapping_name :
      latitude_longitude
      GeoTransform :
      -60 0.125 0 60 0 -0.125
      array(4326, dtype=int32)
    • no2trop_c
      (time, latitude, longitude)
      float32
      nan nan nan nan ... nan nan nan nan
      nodata :
      nan
      array([[[          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        ...,
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]],

       [[          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
...
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]],

       [[          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        ...,
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]]],
      shape=(33, 480, 720), dtype=float32)
    • average
      (latitude, longitude)
      float32
      1.462e+14 1.462e+14 ... 3.174e+14
      nodata :
      nan
      array([[ 1.4615698e+14,  1.4615698e+14,  1.4939574e+14, ...,
         3.8960408e+15,  4.0389150e+15,  4.0389150e+15],
       [ 1.4615698e+14,  1.4615698e+14,  1.4939574e+14, ...,
         3.8960408e+15,  4.0389150e+15,  4.0389150e+15],
       [ 1.3326434e+14,  1.3326434e+14,  5.1975413e+14, ...,
         2.3020061e+15,  1.7881358e+15,  1.7881358e+15],
       ...,
       [ 6.3431010e+14,  6.3431010e+14,  7.2656485e+14, ...,
                   nan,            nan,            nan],
       [-3.0408429e+14, -3.0408429e+14, -1.9628670e+14, ...,
         3.1741684e+14,  3.1741684e+14,  3.1741684e+14],
       [-3.0408429e+14, -3.0408429e+14, -1.9628670e+14, ...,
         3.1741684e+14,  3.1741684e+14,  3.1741684e+14]],
      shape=(480, 720), dtype=float32)

Visualize data cube contents (XArray object)Ā¶

Now Metop-C data of tropospheric NO2 is time-averaged and visualized.
InĀ [41]:
no2trop_metopc["average"] = no2trop_metopc_2["no2trop_c"]\
    .sel(time=slice("2025-08-01", "2025-08-31"))\
    .mean(dim="time")
InĀ [31]:
colors = [(1,1,1,0.1),     # fully transparent white          
          (1,0,1,0.7),     # semi-transparent violet
          (0.2,0,1,0.9),   # blue
          (1,0,0,1.0),     # red
          (0.8,0,0,1.0)]   # dark red

mycmap = LinearSegmentedColormap.from_list('mycmap2', colors, N=256)
InĀ [52]:
%%time

proj = cartopy.crs.PlateCarree()

fig, ax = plt.subplots(subplot_kw={"projection": proj}, figsize=(10,8))

no2trop_metopc_2["average"].plot (
    ax=ax, 
    levels=[1e14,1e15,2e15,3e15,4e15,5e15,6e15,7e15,8e15,1e16],
    cmap=mycmap) # LinearSegmentedColormap.from_list("", ["white", "violet", "red","blue"]))

ax.add_feature(cartopy.feature.LAND, facecolor='#CCCCCC', edgecolor='#000000')
# ax.add_feature(cartopy.feature.COASTLINE.with_scale('110m'))
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
ax.set_extent([-20, 10, 30, 60]) # W/E/S/N
ax.set_title ("Tropospheric NO$_2$ (Metop C) in August 2025 (including the wildfires episode) [molec/cm$^2$]")

CPU times: user 91.6 ms, sys: 3.4 ms, total: 95.1 ms
Wall time: 97.6 ms
Out[52]:
Text(0.5, 1.0, 'Tropospheric NO$_2$ (Metop C) in August 2025 (including the wildfires episode) [molec/cm$^2$]')
No description has been provided for this image

This map shows the tropospheric NO$_2$ as derived from Metop-C/GOME-2 during August 2025. NO$_2$ hotspots are induced by industry (at the Benelux area) as well as by wildfire emissions.

Data Analysis: Compute Difference between Wild Fire Episode and Average ConditionsĀ¶

In the above viewgraph we see enhanced tropospheric NO$_2$ columns over industrial areas as well over wildfire-affected areas (Iberian Peninsula). To identify the wildfires, we substract the average NO$_2$ conditions (1st to 31st of August 2025) from the conditions during the wildfire period (11.-21.08.2025):

tropNO$_2$ (wildfireInduced) = tropNO$_2$ (wildfirePeriod) - tropNO$_2$ (average)

Timeline-nonWildfires-Wildfires

InĀ [39]:
no2trop_metopc["Wildfires"] = no2trop_metopc_2["no2trop_c"]\
    .sel(time=slice("2025-08-12", "2025-08-21"))\
    .mean(dim="time")
InĀ [43]:
no2trop_metopc["wildfireInduced"] = no2trop_metopc["Wildfires"] - no2trop_metopc["average"]
InĀ [44]:
no2trop_metopc
Out[44]:
<xarray.Dataset> Size: 51MB
Dimensions:          (time: 33, latitude: 480, longitude: 720)
Coordinates:
  * time             (time) datetime64[us] 264B 2025-07-31T12:21:39 ... 2025-...
  * latitude         (latitude) float64 4kB 59.94 59.81 59.69 ... 0.1875 0.0625
  * longitude        (longitude) float64 6kB -59.94 -59.81 ... 29.81 29.94
    spatial_ref      int32 4B 4326
Data variables:
    no2trop_c        (time, latitude, longitude) float32 46MB nan nan ... nan
    average          (latitude, longitude) float32 1MB 3.494e+14 ... 1.236e+14
    Wildfires        (latitude, longitude) float32 1MB 9.227e+14 ... nan
    noWildfires      (latitude, longitude) float32 1MB 1.462e+14 ... 3.174e+14
    wildfireInduced  (latitude, longitude) float32 1MB 5.732e+14 ... nan
xarray.Dataset
    • time: 33
    • latitude: 480
    • longitude: 720
    • time
      (time)
      datetime64[us]
      2025-07-31T12:21:39 ... 2025-09-...
      array(['2025-07-31T12:21:39.000000', '2025-08-01T12:51:29.500000',
       '2025-08-02T12:30:35.000000', '2025-08-03T12:09:40.500000',
       '2025-08-04T12:39:31.000000', '2025-08-05T12:18:40.000000',
       '2025-08-06T12:48:27.500000', '2025-08-07T12:27:36.000000',
       '2025-08-08T12:06:45.000000', '2025-08-09T12:36:32.500000',
       '2025-08-10T12:15:44.000000', '2025-08-11T12:45:32.000000',
       '2025-08-12T12:24:40.500000', '2025-08-13T12:03:49.000000',
       '2025-08-14T12:33:36.500000', '2025-08-15T12:12:48.000000',
       '2025-08-16T12:42:36.000000', '2025-08-17T12:21:44.500000',
       '2025-08-18T12:00:53.000000', '2025-08-19T12:30:44.000000',
       '2025-08-20T12:09:52.500000', '2025-08-21T12:39:43.000000',
       '2025-08-22T12:18:51.500000', '2025-08-23T11:58:00.000000',
       '2025-08-24T12:27:51.000000', '2025-08-25T12:06:59.500000',
       '2025-08-26T12:36:53.000000', '2025-08-27T12:15:59.000000',
       '2025-08-28T11:55:10.500000', '2025-08-29T12:25:01.000000',
       '2025-08-30T12:54:51.500000', '2025-08-31T12:34:03.000000',
       '2025-09-01T12:13:09.000000'], dtype='datetime64[us]')
    • latitude
      (latitude)
      float64
      59.94 59.81 59.69 ... 0.1875 0.0625
      units :
      degrees_north
      resolution :
      -0.125
      crs :
      EPSG:4326
      array([59.9375, 59.8125, 59.6875, ...,  0.3125,  0.1875,  0.0625], shape=(480,))
    • longitude
      (longitude)
      float64
      -59.94 -59.81 ... 29.81 29.94
      units :
      degrees_east
      resolution :
      0.125
      crs :
      EPSG:4326
      array([-59.9375, -59.8125, -59.6875, ...,  29.6875,  29.8125,  29.9375],
      shape=(720,))
    • spatial_ref
      ()
      int32
      4326
      spatial_ref :
      GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]
      crs_wkt :
      GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]
      semi_major_axis :
      6378137.0
      semi_minor_axis :
      6356752.314245179
      inverse_flattening :
      298.257223563
      reference_ellipsoid_name :
      WGS 84
      longitude_of_prime_meridian :
      0.0
      prime_meridian_name :
      Greenwich
      geographic_crs_name :
      WGS 84
      horizontal_datum_name :
      World Geodetic System 1984 ensemble
      grid_mapping_name :
      latitude_longitude
      GeoTransform :
      -60 0.125 0 60 0 -0.125
      array(4326, dtype=int32)
    • no2trop_c
      (time, latitude, longitude)
      float32
      nan nan nan nan ... nan nan nan nan
      nodata :
      nan
      array([[[          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        ...,
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]],

       [[          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
...
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]],

       [[          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        ...,
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]]],
      shape=(33, 480, 720), dtype=float32)
    • average
      (latitude, longitude)
      float32
      3.494e+14 3.494e+14 ... 1.236e+14
      nodata :
      nan
      array([[ 3.4944300e+14,  3.4944300e+14,  4.9468648e+14, ...,
         2.3669584e+15,  2.8553120e+15,  2.8553120e+15],
       [ 3.4944300e+14,  3.4944300e+14,  4.9468648e+14, ...,
         2.3669584e+15,  2.8553120e+15,  2.8553120e+15],
       [ 3.7727147e+14,  3.7727147e+14,  5.0586621e+14, ...,
         3.0501913e+15,  2.9196900e+15,  2.9196900e+15],
       ...,
       [ 6.4118842e+14,  6.4118842e+14,  7.6376719e+14, ...,
                   nan,  8.3528571e+14,  8.3528571e+14],
       [ 6.9375426e+14,  6.9375426e+14,  5.0201487e+14, ...,
        -1.4991840e+14,  1.2364279e+14,  1.2364279e+14],
       [ 6.9375426e+14,  6.9375426e+14,  5.0201487e+14, ...,
        -1.4991840e+14,  1.2364279e+14,  1.2364279e+14]],
      shape=(480, 720), dtype=float32)
    • Wildfires
      (latitude, longitude)
      float32
      9.227e+14 9.227e+14 ... nan nan
      nodata :
      nan
      array([[9.22656015e+14, 9.22656015e+14, 9.25576592e+14, ...,
        2.18438305e+15, 2.92192101e+15, 2.92192101e+15],
       [9.22656015e+14, 9.22656015e+14, 9.25576592e+14, ...,
        2.18438305e+15, 2.92192101e+15, 2.92192101e+15],
       [1.06479306e+15, 1.06479306e+15, 1.03094288e+15, ...,
        3.34946568e+15, 3.37231142e+15, 3.37231142e+15],
       ...,
       [5.75627892e+14, 5.75627892e+14, 4.96460370e+14, ...,
                   nan,            nan,            nan],
       [6.07848736e+14, 6.07848736e+14, 5.50953070e+14, ...,
                   nan,            nan,            nan],
       [6.07848736e+14, 6.07848736e+14, 5.50953070e+14, ...,
                   nan,            nan,            nan]],
      shape=(480, 720), dtype=float32)
    • noWildfires
      (latitude, longitude)
      float32
      1.462e+14 1.462e+14 ... 3.174e+14
      nodata :
      nan
      array([[ 1.4615698e+14,  1.4615698e+14,  1.4939574e+14, ...,
         3.8960408e+15,  4.0389150e+15,  4.0389150e+15],
       [ 1.4615698e+14,  1.4615698e+14,  1.4939574e+14, ...,
         3.8960408e+15,  4.0389150e+15,  4.0389150e+15],
       [ 1.3326434e+14,  1.3326434e+14,  5.1975413e+14, ...,
         2.3020061e+15,  1.7881358e+15,  1.7881358e+15],
       ...,
       [ 6.3431010e+14,  6.3431010e+14,  7.2656485e+14, ...,
                   nan,            nan,            nan],
       [-3.0408429e+14, -3.0408429e+14, -1.9628670e+14, ...,
         3.1741684e+14,  3.1741684e+14,  3.1741684e+14],
       [-3.0408429e+14, -3.0408429e+14, -1.9628670e+14, ...,
         3.1741684e+14,  3.1741684e+14,  3.1741684e+14]],
      shape=(480, 720), dtype=float32)
    • wildfireInduced
      (latitude, longitude)
      float32
      5.732e+14 5.732e+14 ... nan nan
      nodata :
      nan
      array([[ 5.7321305e+14,  5.7321305e+14,  4.3089011e+14, ...,
        -1.8257530e+14,  6.6609037e+13,  6.6609037e+13],
       [ 5.7321305e+14,  5.7321305e+14,  4.3089011e+14, ...,
        -1.8257530e+14,  6.6609037e+13,  6.6609037e+13],
       [ 6.8752159e+14,  6.8752159e+14,  5.2507666e+14, ...,
         2.9927439e+14,  4.5262137e+14,  4.5262137e+14],
       ...,
       [-6.5560528e+13, -6.5560528e+13, -2.6730682e+14, ...,
                   nan,            nan,            nan],
       [-8.5905520e+13, -8.5905520e+13,  4.8938200e+13, ...,
                   nan,            nan,            nan],
       [-8.5905520e+13, -8.5905520e+13,  4.8938200e+13, ...,
                   nan,            nan,            nan]],
      shape=(480, 720), dtype=float32)
InĀ [48]:
%%time

proj = cartopy.crs.PlateCarree()

fig, ax = plt.subplots(subplot_kw={"projection": proj}, figsize=(10,8))

no2trop_metopc["wildfireInduced"].plot (
    ax=ax, 
    levels=[1e14,1e15,2e15,3e15,4e15,5e15,6e15,7e15,8e15,1e16],
    cmap=mycmap) # LinearSegmentedColormap.from_list("", ["white", "violet", "red","blue"]))

ax.add_feature(cartopy.feature.LAND, facecolor='#CCCCCC', edgecolor='#000000')
# ax.add_feature(cartopy.feature.COASTLINE.with_scale('110m'))
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
ax.set_extent([-20, 10, 30, 60]) # W/E/S/N
ax.set_title ("Wildfire-Induced Tropospheric NO$_2$ (Metop C) [molec/cmĀ²]")

CPU times: user 72.7 ms, sys: 3.94 ms, total: 76.6 ms
Wall time: 74.2 ms
Out[48]:
Text(0.5, 1.0, 'Wildfire-Induced Tropospheric NO$_2$ (Metop C) [molec/cmĀ²]')
No description has been provided for this image

This viewgraph shows the difference between the averaged wildfire tropNO2 and the average overall tropNO2 burden of August 2025. The major signals (areas with enhanced tropospheric NO2) are induced by wildfire emissions. The mostly constant anthropogenically induced background is substracted.

InĀ [24]:
# End of Notebook