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    Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods 

    Source: Monthly Weather Review:;2018:;volume 146:;issue 011:;page 3651
    Author(s): Whan, Kirien; Schmeits, Maurice
    Publisher: American Meteorological Society
    Abstract: AbstractProbabilistic forecasts, which communicate forecast uncertainties, enable users to make better weather-based decisions. Using precipitation and numerous instability indices from the deterministic model HARMONIE?AROME ...
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    A Comparative Verification of Raw and Bias-Corrected ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia) 

    Source: Journal of Applied Meteorology and Climatology:;2019:;volume 058:;issue 008:;page 1709
    Author(s): Ratri, Dian Nur; Whan, Kirien; Schmeits, Maurice
    Publisher: American Meteorological Society
    Abstract: AbstractDynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using ...
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    The Influence of Atmospheric Blocking on Extreme Winter Minimum Temperatures in North America 

    Source: Journal of Climate:;2016:;volume( 029 ):;issue: 012:;page 4361
    Author(s): Whan, Kirien; Zwiers, Francis; Sillmann, Jana
    Publisher: American Meteorological Society
    Abstract: egional climate models (RCMs) are the primary source of high-resolution climate projections, and it is of crucial importance to evaluate their ability to simulate extreme events under current climate conditions. Many extreme ...
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    Calibration of ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia) Using Bias-Corrected Precipitation and Climate Indices 

    Source: Weather and Forecasting:;2021:;volume( 036 ):;issue: 004:;page 1375
    Author(s): Ratri, Dian Nur;Whan, Kirien;Schmeits, Maurice
    Publisher: American Meteorological Society
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    Statistical Postprocessing of Wind Speed Forecasts Using Convolutional Neural Networks 

    Source: Monthly Weather Review:;2021:;volume( 149 ):;issue: 004:;page 1141
    Author(s): Veldkamp, Simon;Whan, Kirien;Dirksen, Sjoerd;Schmeits, Maurice
    Publisher: American Meteorological Society
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    Correcting Subseasonal Forecast Errors with an Explainable ANN to Understand Misrepresented Sources of Predictability of European Summer Temperatures 

    Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 003
    Author(s): van Straaten, Chiem; Whan, Kirien; Coumou, Dim; van den Hurk, Bart; Schmeits, Maurice
    Publisher: American Meteorological Society
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    Classifying Microwave Radiometer Observations over the Netherlands into Dry, Shallow, and Nonshallow Precipitation Using a Random Forest Model 

    Source: Journal of Hydrometeorology:;2024:;volume( 025 ):;issue: 006:;page 881
    Author(s): Bogerd, Linda; Kidd, Chris; Kummerow, Christian; Leijnse, Hidde; Overeem, Aart; Petkovic, Veljko; Whan, Kirien; Uijlenhoet, Remko
    Publisher: American Meteorological Society
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    Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands 

    Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 004
    Author(s): Cambier van Nooten, Charlotte; Schreurs, Koert; Wijnands, Jasper S.; Leijnse, Hidde; Schmeits, Maurice; Whan, Kirien; Shapovalova, Yuliya
    Publisher: American Meteorological Society
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