A method for statistical downscaling of seasonal ensemble predictions

Abstract

A model output statistics based method for downscaling seasonal ensemble predictions is outlined, and examples of ensemble predictions of precipitation and 2-m temperature are verified against observing stations in Scandinavia, Europe, north-western America, the contiguous United States and Australia. The downscaling from seasonal ensemble predictions from coupled ocean/atmosphere general circulation models to daily precipitation time series for individual observing stations is performed in three steps: (i) a spatial downscaling of ensemble mean seasonal means from dynamical model output to station level by means of patterns derived from a singular value decomposition analysis of model output and observations; (ii) application of the downscaling transformation to the model output ensemble and subsequent calibration of the downscaled ensemble; (iii) a stochastic generation of daily precipitation conditioned on predictions of the probability of a wet day in the season and daily persistence. In the majority of the examples, the downscaling is found to provide more skilful predictions than the raw dynamical model output. Copyright © Blackwell Munksgaard, 2005.