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Published November 6, 2025 | Version 0.0.1
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Meteorological time series in Brazil - automatic stations

Authors/Creators

  • 1. ROR icon Universidade Federal do Rio Grande do Sul

Description

Hourly meteorological time series from automatic climate stations in Brazil.

This dataset is a mirror of official and public data released by Brazil's meteorological public services (INMET). The dataset here is streamlined for processing and managing large volumes of data.

Files

DATABASE.gpkg -- spatial database with the following layers:

  • stations -- points of climate stations with attributes;
  • fields -- table of field names and metadata;
  • data -- time series of meteorological varibales.

Note: data values are scaled by a factor to improve storage efficiency. See fields layer.

query.py -- standalone python script for data retrieval. See docstring for instructions.

Info

Number of stations in catalog: 100

Start of sampling: 2000-01-01 00:00:00

End of sampling: 2025-01-01 00:00:00

Meteorological variables:

  • ppt -- Total hourly precipitation (mm);
  • ap_loc -- Atmospheric pressure at the station level hourly (MB);
  • rad -- Global radiation (kJ/m²)
  • tas_db -- Dry bulb hourly air temperature (°C);
  • tas_dp -- Dew point hourly temperature (°C);
  • hur -- Relative hourly air moisture (%)
  • wind_dir -- Wind hourly direction (°);
  • wind_gust -- Wind hourly maximal gust speed (m/s);
  • wind -- Wind hourly speed (m/s);

Query data

SQL tool

Data can be retrieved in QGIS via SQL tool with this query:
SELECT 
d.*,
s.cd_station
FROM data AS d
LEFT JOIN stations AS s
ON d.id_station = s.id_station
-- define station code
WHERE s.cd_station = 'A001'
-- define datetime range
AND d.datetime BETWEEN '2024-01-01' AND '2024-12-31'
ORDER BY d.datetime;
This query can be imported as a table to QGIS and then exported as a CSV file.
Warning: data values are scaled. Check the fields layer for scale constants.

Python script

Using the terminal, go to folder with DATABASE.gpkg and query.py

>>> cd path/to/folder
Then call the query passing the arguments:
>>> python -m query -o "path/to/output" -s "A001" --start "2020-01-01" --end "2022-01-01"
Where -s is the station code string.
 
This will yield a CSV file for the query in the output folder. 
Note: This method already converts the values to the actual numerical range.
Warning: Pandas and Geopandas are required dependencies.
 

Source

Data was sourced from:

INMET (2025). Meteorological Database for Education and Research (BDMEP) from National Institute of Meteorology (Instituto Nacional de Meteorologia - INMET). Available at: https://bdmep.inmet.gov.br/ Latest access: February of 2025.

 

Logs

0.0.1 -- Data updated to 2025-01-01.

Files

DATABASE.zip

Files (219.9 MB)

Name Size Download all
md5:41c8924adb504079380e43735087e6cb
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md5:47e49b6d92fb8ed0303234697216e976
4.2 kB Download

Additional details

Related works

Is supplemented by
Dataset: 10.5281/zenodo.17543686 (DOI)

References

  • INMET (2025). Meteorological Database for Education and Research (BDMEP) from National Institute of Meteorology (Instituto Nacional de Meteorologia - INMET). Available at: https://bdmep.inmet.gov.br/ Latest access: February of 2025.