Meteorological time series in Brazil - automatic stations

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_stationFROM data AS dLEFT JOIN stations AS sON d.id_station = s.id_station-- define station codeWHERE s.cd_station = 'A001'-- define datetime rangeAND 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.