Improving precipitation forecasts using extreme quantile regression

Improving precipitation forecasts using extreme quantile regression

Velthoen, J.J. (TU Delft Statistics)
Cai, J. (TU Delft Statistics)
Jongbloed, G. (TU Delft Delft Institute of Applied Mathematics)
Schmeits, Maurice (Royal Netherlands Meteorological Institute (KNMI))

2019

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.

Asymptotics
Extreme conditional quantile
Extreme precipitation
Forecast skill
Local linear quantile regression
Statistical post-processing

http://resolver.tudelft.nl/uuid:27f83b6b-c823-4f13-b3a1-fd55ce567ffc

https://doi.org/10.1007/s10687-019-00355-1

1386-1999

Extremes: statistical theory and applications in science, engineering and economics, 22 (4), 599-622

Institutional Repository

journal article

© 2019 J.J. Velthoen, J. Cai, G. Jongbloed, Maurice Schmeits