Get forecast data from meteoblue using EODAG

Get forecast data from meteoblue using EODAG#

In this tutorial we will show you how to use eodag to get forecast data from meteoblue. The provider configuration and this tutorial have been developed in the context of DOMINO-X.

[1]:

import datetime
from eodag import EODataAccessGateway, setup_logging

setup_logging(1)  # 0: nothing, 1: only progress bars, 2: INFO, 3: DEBUG
dag = EODataAccessGateway()
dag.set_preferred_provider("meteoblue")

1. Search (get data availability and build download request)#

There are two use-cases, a search for a product already configured in EODAG, or a search for a dataset not already configured, where you will have a little more to do.

1.a. Search from an existing product type:#

[2]:

tomorrow = (datetime.date.today() + datetime.timedelta(days=1)).isoformat()
after_tomorrow = (datetime.date.today() + datetime.timedelta(days=2)).isoformat()
aoi_bbox = [-2, 42, 3, 45]

products_from_product_type = dag.search(
    start=tomorrow,
    end=after_tomorrow,
    geom=aoi_bbox,
    productType="NEMSGLOBAL_TCDC",
)
print(
    "%s product built %s,\n using queries=%s\n"
    % (
        products_from_product_type.number_matched,
        products_from_product_type[0],
        products_from_product_type[0].properties["queries"],
    )
)

None product built EOProduct(id=NEMSGLOBAL_TCDC_20250115_20250116_fd21a295b1c5795ca4449f051c20faf1884d39cb, provider=meteoblue),
 using queries=[{'domain': 'NEMSGLOBAL', 'gapFillDomain': None, 'timeResolution': 'daily', 'codes': [{'code': 71, 'level': 'sfc', 'aggregation': 'mean'}]}]

[3]:

products_from_product_type[0].properties["storageStatus"], products_from_product_type[0].properties["datapoints"]

[3]:

('ONLINE', 2328)

We can see that the product is OFFLINE, which means that it is not directly avalaible for download and will need to be ordered first.

We also displayed the datapoints property (credits), needed for this download (search does not consume datapoints).

1.b. Search using a custom request:#

Here we use a set of custom parameters corresponding to NEMSGLOBAL_TCDC, which should result to the same request sent to meteoblue.

You can compose your own request using meteoblue documentation: dataset API, available datasets/models, available variables, or also meteoblue dataset API configurator.

[4]:

meteoblue_req_params = {
    "queries":[
        {
            "domain":"NEMSGLOBAL","gapFillDomain":None,"timeResolution":"daily",
            "codes":[{"code":71,"level":"sfc","aggregation":"mean"}],
        }
    ],
    "format": "netCDF",
    "units":{"temperature":"C","velocity":"km/h","length":"metric","energy":"watts"},
    "timeIntervalsAlignment": None,
}

products_from_custom_req = dag.search(
    start=tomorrow,
    end=after_tomorrow,
    geom=aoi_bbox,
    **meteoblue_req_params,
)
# downloadLink property must be the same with the two request methods,
# as they are built from the same custom request arguments
if (
    products_from_custom_req[0].properties["downloadLink"]
    == products_from_product_type[0].properties["downloadLink"]
):
    print(
        "Request using productType or directly meteoblue query result to the\n",
        "same downloadLink %s"
        % (
            products_from_custom_req[0].properties["downloadLink"],
        )
    )

Request using productType or directly meteoblue query result to the
 same downloadLink https://my.meteoblue.com/dataset/query?{"format": "netCDF", "geometry": {"coordinates": [[[-2.0, 42.0], [-2.0, 45.0], [3.0, 45.0], [3.0, 42.0], [-2.0, 42.0]]], "type": "Polygon"}, "queries": [{"codes": [{"aggregation": "mean", "code": 71, "level": "sfc"}], "domain": "NEMSGLOBAL", "gapFillDomain": null, "timeResolution": "daily"}], "timeIntervals": ["2025-01-15/2025-01-15"], "units": {"energy": "watts", "length": "metric", "temperature": "C", "velocity": "km/h"}}

2. Open dataset with `to_xarray` from eodag-cube and plot over a map using `cartopy`#

[5]:

# Get XarrayDict
xd = products_from_product_type[0].to_xarray()
xd

[5]:

XarrayDict (1)

'NEMSGLOBAL_TCDC_20250115_20250116_fd21a295b1c5795ca4449f051c20faf1884d39cb.nc': xarray.Dataset (x: 99, y: 1, time: 1) Size: 2kB

<xarray.Dataset> Size: 2kB
Dimensions:            (x: 99, y: 1, time: 1)
Coordinates:
  * time               (time) float64 8B 1.737e+09
Dimensions without coordinates: x, y
Data variables:
    lat                (x, y) float32 396B ...
    lon                (x, y) float32 396B ...
    asl                (x, y) float32 396B ...
    Cloud Cover Total  (x, y, time) float32 396B ...
Attributes: (12/24)
    domain:                           NEMSGLOBAL
    keywords:                         meteoblue,NEMS,NEMSGLOBAL,CLOUD,COVER,T...
    license:                          proprietary
    title:                            NEMSGLOBAL_TCDC_20250115_20250116_fd21a...
    missionStartDate:                 1984-01-01T00:00:00Z
    _id:                              NEMSGLOBAL_TCDC
    ...                               ...
    format:                           netCDF
    timeIntervalsAlignment:           None
    orderLink:                        https://my.meteoblue.com/dataset/query?...
    timeIntervals:                    ['2025-01-15/2025-01-15']
    qs:                               {'format': 'netCDF', 'geometry': {'coor...
    _dc_qs:                           %7B%22format%22%3A+%22netCDF%22%2C+%22g...

[6]:

# Dataset from XarrayDict first value
ds = next(iter(xd.values()))
ds

[7]:

from datetime import datetime, UTC
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

# data and coordinates from dataset
cct = ds["Cloud Cover Total"][:, 0, 0]
lats = ds.lat[:, 0]
lons = ds.lon[:, 0]
title = datetime.fromtimestamp(int(ds.time), UTC).isoformat()

fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})

# filled contour plot
tcf = ax.tricontourf(lons, lats, cct)
ax.set_title(title)
ax.coastlines()

# colorbar
cbar = plt.colorbar(tcf, ax=ax, shrink=0.7, label=cct.name)

# gridlines
gridlines = ax.gridlines(draw_labels=True)
gridlines.right_labels = False
gridlines.top_labels = False

plt.show()

../../_images/notebooks_tutos_tuto_meteoblue_11_0.png

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