Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression

Friederichs, P.; Hense, A.
June 2007
Monthly Weather Review;Jun2007, Vol. 135 Issue 6, p2365
Academic Journal
A statistical downscaling approach for extremes using censored quantile regression is presented. Conditional quantiles of station data (e.g., daily precipitation sums) in Germany are estimated by means of the large-scale circulation as represented by the NCEP reanalysis data. It is shown that a mixed discrete–continuous response variable, such as a daily precipitation sum, can be statistically modeled by a censored variable. Furthermore, a conditional quantile skill score is formulated to assess the relative gain of a quantile forecast compared with a reference forecast. Just like multiple regression for expectation values, quantile regression provides a tool to formulate a model output statistics system for extremal quantiles.


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