Research & development

Our unique task is the gathering of information about the atmosphere and the subsurface and the translation of that information to risks to the community



  • 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 NiƱo 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.
  • 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.

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About KNMI

The Royal Netherlands Meteorological Institute (KNMI)

Is the dutch national weather service. Primary tasks of KNMI are weather forecasting, and monitoring of weather, climate and seismic activity. KNMI is also the national research and information centre for meteorology, climate, air quality, and seismology