You can run this notebook in Binder, in Colab, in Deepnote or in Kaggle.

[1]:
!pip install --quiet climetlab

BUFR data

[2]:
import climetlab as cml
[3]:
source = cml.load_dataset("sample-bufr-data")
[4]:
source
[4]:

sample-bufr-data

Home page-
DocumentationSample BUFR file containing TEMP messages
Citation
-
Licence-

See https://github.com/ecmwf/pdbufr

[5]:
pd = source.to_pandas(
    columns=(
        "stationNumber",
        "latitude",
        "longitude",
        "data_datetime",
        "pressure",
        "airTemperature",
    ),
    filters={},
)
[6]:
pd.head()
[6]:
stationNumber latitude longitude pressure airTemperature data_datetime
0 907 58.47 -78.08 100300.0 258.3 2008-12-08 12:00:00
1 907 58.47 -78.08 100000.0 259.7 2008-12-08 12:00:00
2 907 58.47 -78.08 99800.0 261.1 2008-12-08 12:00:00
3 907 58.47 -78.08 99100.0 261.7 2008-12-08 12:00:00
4 907 58.47 -78.08 92500.0 258.1 2008-12-08 12:00:00
[7]:
pd.tail()
[7]:
stationNumber latitude longitude pressure airTemperature data_datetime
26191 968 25.03 121.52 10000.0 197.9 2008-12-08 12:00:00
26192 968 25.03 121.52 9520.0 196.3 2008-12-08 12:00:00
26193 968 25.03 121.52 7000.0 201.5 2008-12-08 12:00:00
26194 968 25.03 121.52 5000.0 209.1 2008-12-08 12:00:00
26195 968 25.03 121.52 3000.0 217.3 2008-12-08 12:00:00
[8]:
cml.plot_map(pd, projection="global")
../_images/examples_08-bufr-data_9_0.png