SILAM on AWS S3 - Finnish Meteorological Institute

SILAM Global Air Quality forecast

SILAM (System for Integrated modelLling of Atmospheric coMposition) is a global-to-meso-scale dispersion model developed for atmospheric composition, air quality, and emergency decision support applications, as well as for inverse dispersion problem solution.

The data is licenced by the Creative Commons Attribution 4.0 International license (CC BY 4.0).

FMI provides operational 20-km global AQ forecast of 7 regulated constituents for free download from AWS S3 bucket.

The 168-hour long forecast is generated once a day every evening with start time 00Z of previous day to accommodate the effect of actually observed emissions of e.g. forest fires. The forecast is available around 06:00 UTC next day with 121 hourly steps form 00Z D0.

Example presentation of SILAM Global Air Quality forecast

Accessing the Data on AWS

The data is uploaded into two AWS S3 Buckets:

fmi-opendata-silam-surface-netcdf (for surface data)
fmi-opendata-silam-surface-zarr (for pressure levels data)

Every model run is stored in separate directories divided into files based on parameters. Every file contains all valid times for one parameter and one model run.

Files are named with a convention:

silam_glob_v5_6_<year><month><day>_<parameter>_<forecast_day>.<format>

For example:

silam_glob_v5_6_20190819_CO_d0.zarr
silam_glob_v5_6_20190819_CO_d0.netcdf

The data is stored into following directory structure:

/global/<year><month><day>/

Where times are derived from origin time of the model run.

Content of the buckets can be browsed here:

For more information on Amazon S3 event message structure, visit http://docs.aws.amazon.com/AmazonS3/latest/dev/notification-content-structure.html.

More Information

Silam home page: http://silam.fmi.fi

Some usage examples: https://github.com/fmidev/opendata-resources/tree/master/examples/python