Statistical post processing of extreme weather forecasts

Doctoral thesis (2022)

Authors

J.J. Velthoen Statistics - EEMCS
Research Group
Statistics (EEMCS) (TU Delft)
Copyright: © 2022 J.J. Velthoen
DOI: https://doi.org/10.4233/uuid:f6a6096d-9eb1-4a77-b376-34e01b817011
Random Forest Extreme value theory Extreme quantile regression Statistical post-processing Extreme conditional quantile Variable selection Gradient boosting
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

Research Group

Statistics

Abstract

In this thesis we develop several statistical methods to estimate high conditional quantiles to use for statistical post-processing of weather forecasts. We propose methodologies that combine theory from extreme value statistics and machine learning algorithms in order to estimate high conditional quantiles in large covariate spaces. In applications of weather forecasting we show improved predictive skill for precipitation forecasts.

Files

Dissertation.pdf
(.pdf | 12.1 Mb)