Abstract

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.


Metadata

Dataset name QRF-RT-SSh
Dataset version v2021
Status onGoing
Last metadata update December 5, 2023, 14:42 (UTC)
Update frequency daily
License https://creativecommons.org/licenses/by/4.0/
North bound latitude 53.504435
East bound longitude 7.356895
South bound latitude 50.753635
West bound longitude 3.263585
Dataset edition 1
Dataset manager Kirien Whan
Maintainer KNMI Data Services
Publication timestamp 2022-04-15T00:00:00Z
Reference system identifier EPSG4326
Dataset start time 2022-04-15
Dataset end time Unlimited
Identifier urn:xkdc:ds:nl.knmi::QRF-RT-SSh/v2021/
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
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