Ensemble precipitation forecasts made with Quantile Regression Forests and deterministic Harmonie-Arome inputs

A gridded 50-member ensemble of precipitation forecasts that are created using a tree-based machine learning method, quantile regression forests, and inputs from the deterministic Harmonie-Arome (HA) forecasts. The target data set is rain-gauge-adjusted radar data that is upscaled by taking 3x3 km means and then a maximum is taken in a 7.5 x 7.5 km box. Inputs to the machine learning model include HA precipitation, and indices of atmospheric instability. Spatial and temporal dependencies are restored using the Schaake Shuffle. Forecasts are available during the extended summer period (mid-April to mid-October). Hourly forecasts are issued 4 times per day (00, 06, 12 en 18 UTC) for 48-hours into the future.

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Data and Resources

Additional Info

Field Value
Dataset name QRF-RT-SSh
Dataset version v2021
Dataset edition 1
Dataset manager Kirien Whan
Maintainer KNMI Data Services
Publication timestamp 2022-04-15T00:00:00Z
Reference system identifier EPSG4326
North bound latitude 53.504435
East bound longitude 7.356895
South bound latitude 50.753635
West bound longitude 3.263585
Dataset start time 2022-04-15
Dataset end time Unlimited
Update frequency daily
Status onGoing
Identifier urn:xkdc:ds:nl.knmi::QRF-RT-SSh/v2021/
License https://creativecommons.org/publicdomain/zero/1.0/
Lineage statement Bi-linear interpolation from lambert to regular lat-lon
Purpose Calibrated and skilful gridded ensemble forecasts of summer precipitation in the Netherlands
Use limitation pre-operational, so no guaranteed delivery yet
Last metadata update April 13, 2022, 06:48 (UTC)