Searched for: subject%3A%22Statistical%255C+post%255C-processing%22
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Velthoen, J.J. (author)
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...
doctoral thesis 2022
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Velthoen, J.J. (author), Cai, J. (author), Jongbloed, G. (author), Schmeits, Maurice (author)
Aiming to estimate extreme precipitation forecast quantiles, we propose a nonparametric regression model that features a constant extreme value index. Using local linear quantile regression and an extrapolation technique from extreme value theory, we develop an estimator for conditional quantiles corresponding to extreme high probability...
journal article 2019
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Verkade, J.S. (author), Brown, J. D. (author), Davids, F. (author), Reggiani, P. (author), Weerts, A. H. (author)
Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) ‘dressing’ of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) ‘dressing’ of an ensemble streamflow forecast by adding an estimate of hydrological...
journal article 2017