Abstract
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 levels. We establish uniform consistency and asymptotic normality of the estimators. In a simulation study, we examine the performance of our estimator on finite samples in comparison with a method assuming linear quantiles. On a precipitation data set in the Netherlands, these estimators have greater predictive skill compared to the upper member of ensemble forecasts provided by a numerical weather prediction model.
Original language | English |
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Pages (from-to) | 599-622 |
Number of pages | 24 |
Journal | Extremes |
Volume | 22 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Dec 2019 |
Externally published | Yes |
Funding
The authors would like to sincerely thank the two referees and the associate editor for the constructive comments which led to a substantial improvement of this paper. This work is part of the research project “Probabilistic forecasts of extreme weather utilizing advanced methods from extreme value theory” with project number 14612 which is financed by the Netherlands Organisation for Scientific Research (NWO).
Funders | Funder number |
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Nederlandse Organisatie voor Wetenschappelijk Onderzoek |
Keywords
- Asymptotics
- Extreme conditional quantile
- Extreme precipitation
- Forecast skill
- Local linear quantile regression
- Statistical post-processing