computerruimte van het KNMI (Bron Tineke Dijkstra)

Weather & Climate Models

The department Research and Development of Weather and Climate models (RDWK) investigates and develops research tools for weather and air quality prediction applications and climate models. We work on detailed physical processes, data assimilation, long term climate projections and practical applications including storm surge forecasts and statistics of extremes. RDWK participates in a number of international projects directed towards a variety of weather and climate related research and development areas and acts as the Netherlands Focal Point to the IPCC.

RDWK is structured in 3 clusters: Mesoscale modelling develops tools for regional numerical weather prediction (NWP) and climate analyses; Large scale modelling focuses on global climate and atmospheric chemistry; Postprocessing and Analysis develops statistical analyses, applications and climate services.

RDWK consists of about 45 research professionals (including PhD and technical support staff). We have a strong international network, and most activities are executed in collaboration with partners in e.g. HIRLAM/Harmonie, EC-Earth, ECMWF and universities.

Contracts from Rijkswaterstaat, Ministry of Infrastructure and Water Management, funding organisation NWO, the European Copernicus program and European research programs provide roughly half of the annual portfolio. The other half is basic funding for servicing the weather forecast centre, climate scenarios and strategic research.

Some recent and ongoing activities and projects: development of the NWP model system Harmonie, and its tailoring to the needs of the KNMI Early Warning Centre is embedded in the HIRLAM consortium work program, and focuses on data assimilation and ensemble prediction. The KNMI’14 climate change scenarios will be followed by a new generation of generic and specific scenarios in the timeframe 2018-2020, focusing on future weather applications, urban scenarios and sea level rise. Contributions to the international EC-Earth program focuses on the development of high resolution projections and the coupling of atmospheric chemistry in the Earth System Model configuration. Observations and climate models are used to attribute causes of past climate change and extreme weather events.  The KNMI Climate Explorer is a web-based climate data browser, used by many students, researchers and practicioners worldwide. Other research topics include air quality forecasting, extreme precipitation statistics, sea level rise, and evaluation of weather alerts.

Infographic KNMI weather and climate models


  • Crowdsourced data matches well with official data

    Crowdsourced wind observations can be an invaluable data resource for meteorological studies

    Can citizen scientists help KNMI to improve forecasts and warnings for extreme wind events? The answer is yes according to the new paper by Jieyu Chen, Dr. Kirien Whan, and Dr. Kate Saunders, published in the Quarterly Journal of the Royal Meteorological Society. The research shows that by checking the quality of the data and adjusting for biases there is value in crowdsourced data and that it can be used to complement official observations.
  • topography map of java

    Calibration of seasonal precipitation forecasts in Java (Indonesia) using bias-corrected precipitation and climate indices

    Seasonal rainfall forecasts help farmers make informed planning decisions about their livelihoods. Skilful rainfall forecasts can improve farming strategies in rain-fed agricultural production. In Indonesia, large-scale modes of climate variability have strong relationships with the seasonal rainfall. This makes them natural candidates for use as potential predictors in a statistical post-processing application. It is not known whether using climate indices as additional predictors in the statistical post-processing of ECMWF Seasonal Forecast System 5 (SEAS5) precipitation can improve skill. Lead author Dian Nur Ratri says "Indices of El Niño and the Indian Ocean Dipole are not needed as extra predictors to improve monthly precipitation forecasts for the first lead month in Java - Indonesia, except for September. However, for longer lead times in September and October, advanced statistical models that use only the climate indices are as skilful as models that use bias-corrected precipitation as the inputs".
  • A figure showing that some WOW-NL stations recorded much more rainfall than the AWS station in Maastricht

    Insights about extreme precipitation in Limburg from crowdsourced data

    Weather observations are the cornerstone of climatological and meteorological services. Crowdsourced data can complement the existing official KNMI network by providing observations with both a high spatial and temporal resolution. We examine precipitation on July 13th and 14th measured by the WOW-NL network to show how the acquisition of crowdsourced measurements can help KNMI to better understand extreme events.
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