 |
| Titel |
Probabilistic forecasting of extreme weather events based on extreme value theory |
| VerfasserIn |
Hans Van de Vyver, Bert Van Schaeybroeck |
| Konferenz |
EGU General Assembly 2016
|
| Medientyp |
Artikel
|
| Sprache |
en
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250124956
|
| Publikation (Nr.) |
 EGU/EGU2016-4469.pdf |
|
|
|
|
|
| Zusammenfassung |
| \begin{document}
Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance.
%{\color{blue} We consider the combined events when an observation variable $Y$ (for instance wind speed) exceeds a high threshold $y$ and its corresponding deterministic forecasts $X$ also exceeds a high forecast threshold $y$. More specifically two problems are addressed:}
We consider pairs $(X,Y)$ of extreme events where $X$ represents a deterministic forecast, and $Y$ the observation variable (for instance wind speed). More specifically two problems are addressed:
\begin{enumerate}
\item Given a high forecast $X=x_0$, what is the probability that $Y>y$? In other words: {\em provide inference on the conditional probability}:
\[
\mbox{Pr}\{Y>y|X=x_0\}.
\]
\item Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events.
\end{enumerate}
These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007).
We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair $(X,Y)$. The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008).
\vspace{1cm}
\begin{itemize}
\item Coles, S. (2001) {\em An Introduction to Statistical modelling of Extreme Values}. Springer-Verlag.
\item Ferro, C.A.T. (2007) A probability model for verifying deterministic forecasts of extreme events. {\em Wea.~Forecasting} {\bf 22}, 1089--1100.
\item Hagedorn, R. (2008) {\em Using the ECMWF reforecast dataset to calibrate EPS forecasts}. ECMWF Newsletter, {\bf 117}, 8-13.
\item Ramos, A., Ledford, A. (2009) A new class of models for bivariate joint tails. {\em J.R. Statist. Soc. B} {\bf 71}, 219--241.
\end{itemize}
\end{document} |
|
|
|
|
|
|