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Statistical Postprocessing of Numerical Weather Prediction Forecasts using Machine Learning

Schulz, Benedikt ORCID iD icon 1
1 Institut für Stochastik (STOCH), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Nowadays, weather prediction is based on numerical models of the physics of the atmosphere. These models are usually run multiple times based on randomly perturbed initial conditions. The resulting so-called ensemble forecasts represent distinct scenarios of the future and provide probabilistic projections. However, these forecasts are subject to systematic errors such as biases and they are often unable to quantify the forecast uncertainty adequately. Statistical postprocessing methods aim to exploit structure in past pairs of forecasts and observations to correct these errors when applied to future forecasts.
In this thesis, we develop statistical postprocessing methods based on the central paradigm of probabilistic forecasting, that is, to maximize the sharpness subject to calibration. A wide range of statistical and machine learning methods is presented with a focus on novel neural network-based postprocessing techniques. In particular, we analyze the aggregation of distributional forecasts from neural network ensembles and develop statistical postprocessing methods for ensemble forecasts of wind gusts, with a focus on European winter storms.

Volltext §
DOI: 10.5445/IR/1000158905
Veröffentlicht am 25.05.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
Institut für Stochastik (STOCH)
Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Hochschulschrift
Publikationsdatum 25.05.2023
Sprache Englisch
Identifikator KITopen-ID: 1000158905
Verlag Karlsruher Institut für Technologie (KIT)
Art der Arbeit Dissertation
Fakultät Fakultät für Mathematik (MATH)
Institut Institut für Stochastik (STOCH)
Prüfungsdatum 17.05.2023
Projektinformation SFB/TRR 165/2 (DFG, DFG KOORD, TRR 165/2 2019)
Schlagwörter Statistical Postprocessing, Neural Networks, Machine Learning, Ensemble Forecasts, Wind Gust Prediction, Probabilistic Forecasting
Referent/Betreuer Gneiting, Tilmann
Knippertz, Peter
Lerch, Sebastian
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