Researchers from KNMI, TU Delft, WUR, VU, and UU came together on Thursday 7 November at the Delft Institute of Applied Mathematics at TU Delft, hosted by Prof. dr. ir. Geurt Jongbloed, who also attended the workshop. They discussed the state of statistical post-processing research in The Netherlands. The researchers all work in close collaboration with KNMI to improve weather forecasts at a variety of scales. These collaborations between a government research organisation and universities that have different research focuses, from statistical to domain knowledge, are valuable as they allow the contribution from different perspectives to a common research question.
What is statistical post-processing?
Weather forecasts are made with numerical weather prediction (NWP) models. These computer models estimate the initial state of the atmosphere, and then solve physical equations to forecast the future state of the atmosphere. The NWP models, that are run by KNMI and other government or inter-governmental research organisations (like ECMWF), are able to skilfully forecast many weather situations. However these computer models contain errors that reduce forecast skill. The systematic errors can be corrected by statistical post-processing.
Statistical post-processing is an area of research that focuses on improving weather forecasts using statistical or machine learning methods. Researchers find statistical relationships between observations and predictor variables from the weather model over the historical period, and use these relationships to improve future forecasts.
Statistical post-processing at KNMI
Led by Dr Maurice Schmeits, researchers working with KNMI focus on improving weather forecasts at a number of time scales, from the next few hours to the seasonal scale, and for a variety of variables, including precipitation, wind speed, solar radiation, temperature, storm surge, and slippery roads.
“Our research focuses on better probabilistic forecasts of extreme weather, because these can enable people to take preventive action in order to reduce damage. Statistical and machine learning methods that post-process NWP model forecasts can help to improve these forecasts”, says Dr Maurice Schmeits.
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