FMI provides two data set in AWS S3: Hirlam numerical weather model and SILAM global weather model. The data are publicly accessible in S3.

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

Forecast Content - HIRLAM

The current horizontal resolution of the HIRLAM RCR model is 7.5 km. At present, the current operational setup produces daily four 54 hour regional forecasts for extended European area (Figure below). The model is initiated by the ECMWF boundary condition files.

The data is divided in two sets: surface data and pressure levels data. The surface level data provide data for surface of the earth and pressure level data for constant air pressure levels in the atmosphere. The following pressure levels are provided: 1000, 925, 850, 700, 500, 400, 300, 250, 200, 100, 50 hPa.

The data is in projection epsg:4326. Parameters

Following parameters are available in the surface data:

Parameter	GRIB ID	GRIB Name	Level Type	Other Information
Pressure	151	msl	meanSea
GeopHeight	256	Z
Temperature	167	2t	heightAboveGround
DewPoint	168	2d	heightAboveGround
Humidity	157	r	heightAboveGround
WindUMS	131	u	heightAboveGround
WindVMS	132	v	heightAboveGround
PrecipitationAmount	201113	rain_con
TotalCloudCover	164	N	244
LowCloudCover	186	Cl	214
MediumCloudCover	187	Cm	224
HighCloudCover	188	Ch	234
Precipitation1h	2059	rr1h	entireAtmosphere
MaximumWind	201187	MaximumWind	heightAboveGround
WindGust	29	WindGust	105
RadiationGlobalAccumulation	169	ssrd		"templatenumber": 8 / "aggregatetype" : "accum" / "aggregatelength" : 60
RadiationLWAccumulation	175	strd		"templatenumber": 8 / "aggregatetype" : "accum" / "aggregatelength" : 60
RadiationNetSurfaceLWAccumulation	177	str		"templatenumber": 8 / "aggregatetype" : "accum" / "aggregatelength" : 60
RadiationNetSurfaceSWAccumulation	228242	fdif

And following in the pressure level data:

Parameter	GRIB ID	GRIB Name
GeopHeight	256	Z
Temperature	167	2t
DewPoint	168	2d
Humidity	157	r
WindUMS	131	u
WindVMS	132	v
VelocityPotential	135	w
PseudoAdiabaticPotentialTemperature	3014

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: