Print Email Facebook Twitter Statistical post processing of extreme weather forecasts Title Statistical post processing of extreme weather forecasts Author Velthoen, J.J. (TU Delft Statistics) Contributor Jongbloed, G. (promotor) Cai, J. (copromotor) Degree granting institution Statistics Date 2022-09-14 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. Subject Extreme quantile regressionStatistical post-processingExtreme value theoryExtreme conditional quantileVariable selectionRandom ForestGradient boosting To reference this document use: https://doi.org/10.4233/uuid:f6a6096d-9eb1-4a77-b376-34e01b817011 Part of collection Institutional Repository Document type doctoral thesis Rights © 2022 J.J. Velthoen Files PDF dissertation.pdf 12.14 MB Close viewer /islandora/object/uuid:f6a6096d-9eb1-4a77-b376-34e01b817011/datastream/OBJ/view