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

Highlights

  • 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.
  • Hypothetical distribution of a selected climate variable or process

    Global changes in extreme events attributed to changes in mean climate and climate variability

    Extreme weather events are projected to change due to climate change, the risks to societies are therefore also changing. In a new study published in Communications Earth and Environment, Dr. Karin van der Wiel (KNMI) and Prof. Richard Bintanja (KNMI, Univ. Groningen) demonstrate that the increased occurrence of monthly extreme heat events is predominantly caused by a warming mean climate. In contrast, future changes in monthly heavy rainfall events depend to a considerable degree on changes in climate variability. Examining the origin of changes in extreme events, changing mean or changing variability, provides valuable insights into the processes driving these important climatic changes.
  • bams logo

    A review of state-of-the-art machine learning methods for improving probabilistic weather forecasts

    KNMI scientists, Dr Maurice Schmeits and Dr Kirien Whan, have contributed to a review of the latest methods to improve weather forecasts using statistical and machine learning methods. The review was led by Dr Stéphane Vannitsem from the Belgium Royal Meteorological Institute (RMI) and had contributions from scientists working on statistical post-processing at 11 national weather services (NWSs) in Europe, as well as Karlsruhe Institute of Technology, and the European Center for Medium-Range Weather Forecasts.
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