13 Papers
197 Citations
Kees Kok is an academic researcher from Royal Netherlands Meteorological Institute. The author has contributed to research in topics: Numerical weather prediction & Model output statistics. The author has an hindex of 7, co-authored 11 publications.
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Papers
A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts
Maurice Schmeits,Kees Kok +1 more
Abstract: Using a 20-yr ECMWF ensemble reforecast dataset of total precipitation and a 20-yr dataset of a dense precipitation observation network in the Netherlands, a comparison is made between the raw ensemble output, Bayesian model averaging (BMA), and extended logistic regression (LR). A previous study indicated that BMA and conventional LR are successful in calibrating multimodel ensemble forecasts of precipitation for a single forecast projection. However, a more elaborate comparison between these methods has not yet been made. This study compares the raw ensemble output, BMA, and extended LR for single-model ensemble reforecasts of precipitation; namely, from the ECMWF ensemble prediction system (EPS). The raw EPS output turns out to be generally well calibrated up to 6 forecast days, if compared to the area-mean 24-h precipitation sum. Surprisingly, BMA is less skillful than the raw EPS output from forecast day 3 onward. This is due to the bias correction in BMA, which applies model output statisti...
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A Combined Physical–Statistical Approach for the Downscaling of Model Wind Speed
Wim C. de Rooy,Kees Kok +1 more
TL;DR: In this paper, a combined physical-statistical approach for the downscaling of model wind speed is assessed, which decomposes the total error into a small-scale representation mismatch (RM) and a large-scale model error (ME).
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Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics
TL;DR: In this article, the authors derived and verified logistic regression equations for the (conditional) probability of (severe) thunderstorms in the warm half-year (from mid-April to mid-October) in the Netherlands.
Visualizing flood forecasting uncertainty: some current European EPS platforms-COST731 working group 3
Michael Bruen,P. Krahe,Massimiliano Zappa,Jonas Olsson,Bertel Vehviläinen,Kees Kok,K. Daamen +6 more
TL;DR: COST731 was established to study the propagation of uncertainty from hydrometeorological observations through meteorological and hydrological models to the final flood forecast and has assembled a number of demonstrations/case studies that illustrate a variety of practical approaches.
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Probabilistic Forecasts of (Severe) Thunderstorms for the Purpose of Issuing a Weather Alarm in the Netherlands
TL;DR: In this paper, the development and verification of a new model output statistics (MOS) system is described; this system is intended to help forecasters decide whether a weather alarm for severe thunderstorms, based on high total lightning intensity, should be issued in the Netherlands.
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