Hirlam Weather Model
HIRLAM (High Resolution Limited Area Model) is an operational synoptic and mesoscale weather prediction model. The model is developed by an international program formed by institutes from Sweden, Norway, Denmark, Iceland, the Netherlands, Ireland, Spain, Estonia and Lithuania. There is also close co-operation with France.
FMI provides its operational Hirlam model runs to be downloaded freely from AWS S3 bucket. The data is licenced by the Creative Commons Attribution 4.0 International license (CC BY 4.0).
The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corresponding model runs are available roughly five hours after analysis time (~ after model run has started).

HIRLAM model runs end
The use of the HIRLAM weather forecasting model at the Finnish Meteorological Institute will be discontinued in September 2022, after which its forecasts are no longer available through the open data services.
During the past fifteen years, the main focus in forecast model development has been on a more accurate model with improved resolution. The resulting HARMONIE (MEPS) model is capable of 2.5 km horizontal resolution (compared to 7.5 km of HIRLAM) and able to predict small scale phenomena more accurately. HARMONIE (MEPS) forecasts are now available through the open data services.
More information will be made available later, but already now it is worth switching to use HARMONIE (MEPS) model if possible.
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-rcrhirlam-surface-grib (for surface data)
fmi-opendata-rcrhirlam-pressure-grib (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:
numerical-hirlam74-forecast-<parameter>-<year><month><day>T<HOUR>0000Z.grb2
For example:
numerical-hirlam74-forecast-DewPoint-20170322T000000Z.grb2
The data is stored into following directory structure:
<year>/<month>/<day>/<hour>
Where times are derived from origin time of the model run.
Content of the buckets can be browsed here:
http://fmi-opendata-rcrhirlam-surface-grib.s3-website-eu-west-1.amazonaws.com/
http://fmi-opendata-rcrhirlam-pressure-grib.s3-website-eu-west-1.amazonaws.com/
SNS
Public Amazon SNS topics are available for every new object added to the Amazon S3.
arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-surface-grib
arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-pressure-grib
For more information on subscribing to SNS topics, visit http://docs.aws.amazon.com/sns/latest/dg/SubscribeTopic.html.
For more information on Amazon S3 event message structure, visit http://docs.aws.amazon.com/AmazonS3/latest/dev/notification-content-structure.html.
Tools to handle the data
The data is stored in GRIB2-files. Below is listed few possible tools to handle the data.
PanoPly
PanoPly provides an easy UI to view GRIB, NetCDF and HDF files.
Integrated Data Viewer (IDV)
Compared to PanoPly Integrated Data Viewer (IDV) provides a slightly more complicated but more versatile user interface for browsing and analysing geographical data.
GRIB-tools
Bunch of tools exist for batch processing the data:
ECMWF GRIB tools provides number of convenient command line tools to process the data. For more information, please consult their wiki:
NOAA's WGRIB2 (and WGRIB for GRIB1 files) can for example: inventory and read grib2 files, create subsets and export data to ieee, text, binary, CSV, netcdf and mysql.
http://www.cpc.ncep.noaa.gov/products/wesley/wgrib2/
Geospatial Data Abstraction Library GDAL supports GRIB as well
http://www.gdal.org/frmt_grib.html
SmartMet Server
If you are using the data in an operational manner and live in web environment, SmartMet Server is a great resource to extract the data as JSON or GML and visualize it via WMS.
For more information, please consult: https://github.com/fmidev/smartmet-server
Additional Resources
http://en.ilmatieteenlaitos.fi/numerical-weather-prediction http://en.ilmatieteenlaitos.fi/open-data-manual-forecast-models https://aws.amazon.com/public-datasets/fmi-hirlam/