Daily meteorological time series from conventional 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 CONV_DATABASE.gpkg -- spatial database with the following layers: fields -- table of field names and metadata; stations -- points of climate stations with attributes; series -- 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: 135 Start of sampling: 1961-01-01 00:00:00 End of sampling: 2025-01-01 00:00:00 Meteorological variables: p -- Total daily precipitation (mm); evp -- Daily Piché evaporation (mm); sun -- Total sunshine daily (h); tas_max -- Maximum daily temperature (°C); tas_mean -- Compensated daily average temperature (°C); tas_min -- Minimum daily temperature (°C); hur_mean -- Relative air humidity daily average (%); hur_min -- Relative air humidity daily minimum (%); winds_mean -- Wind daily average speed (m/s); Query data SQL tool Data can be retrieved in QGIS via SQL tool with this query: SELECT d.*,s.cd_stationFROM series AS dLEFT JOIN stations AS sON d.id_station = s.id_station-- define station codeWHERE s.cd_station = '83377'-- 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 CONV_DATABASE.gpkg and query.py >>> cd path/to/folder Then call the query passing the arguments: >>> python -m query -o "path/to/output" -s "83377" --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 1.0.0 -- Operational release. Changes in some fields names. Changes in file name. Changes in layer names (data changed to series). Changes in no-data value (-1 changed to -9999). 0.0.1 -- Testing release. Data updated to 2025-01-01.