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Iñigo EcheverríaGeologist · Geospatial developer
All work

MeteoDMC

Monthly climate anomalies for Chile’s weather stations, built automatically from the national weather service’s records to show where the fire season is running hotter or drier than normal.

Organisation
CONAF
Role
Lead developer
Period
2024
Services
  • GIS analysis
  • Data pipelines
Heatmap of the monthly maximum temperature anomaly at 29 weather stations, ordered north to south from Putre to Puerto Williams, January 2025 to May 2026. Most cells are pale; strong red bands mark warm months, especially across the south in December 2025 and January 2026, and blue patches cooler ones in March 2026.
Maximum temperature anomaly at 29 stations from north to south, January 2025 to May 2026, against the 1991–2020 normal. Redrawn from MeteoDMC’s output, without the per-cell values.

Before and during the fire season, CONAF needs to know where the country is running hotter or drier than usual. The Dirección Meteorológica de Chile (DMC) publishes the records of its stations, but one station at a time, in tables and downloadable files that had to be compiled by hand. MeteoDMC turns them into ready-to-use figures with a single command. I built it in 2024, by hand, without AI coding assistance.

What it does

  • Collects the records. Downloads monthly and daily data for about 30 stations, from Putre to Puerto Williams, from the DMC’s climate data service: compressed CSV files, HTML tables and its station catalogue.
  • Compares them with the normal. Each month is set against the station’s 1991–2020 normal: in degrees for maximum and minimum temperature, as a percentage for precipitation. Where the DMC publishes no normal, a reference table fills the gap, and stations still missing one are listed in a report instead of silently dropped.
  • Draws the figures. Anomaly heatmaps over the last year and a half, on a diverging scale centred on the normal, and the rain accumulated since the start of the fire season compared with the previous season and the climatology.
  • Exports the numbers. Every figure comes with Excel tables of the data behind it.

Python code from meteodmc/variables.py: a dictionary maps friendly names such as tmax and pp to DMC variable codes such as TxPM and RRR6, and each code defines its stations file, the DMC file path, its columns and units, how to aggregate it, how to compute its anomaly (a difference for temperature, a percentage for precipitation) and how to plot it.

How MeteoDMC describes each DMC variable (excerpt, simplified). Where its files are, how to read them, how to compute the anomaly and how to draw it all live in one definition per variable.

How it is built

A small Python package with one command per product, managed with uv. Collecting, data handling and plotting are separate modules, and each variable defines its own anomaly formula, colour scale and labels, so adding one doesn’t touch the rest.