Statistical downscaling of daily precipitation over Greece

Abstract

AbstractThe capability of a multi‐site non‐homogeneous hidden Markov model (NHMM) to downscale winter daily precipitation over Greece is explored for the period 1971–2000. The input variables were selected from NCEP Reanalysis Data following an optimization procedure and include the precipitation rate as predictor. The most parsimonious NHMM identified five weather states. Successively, the forecast skill of the model was tested against sequentially withdrawn data. The NHMM reproduced successfully both the station‐scale statistics like the occurrence and amount of precipitation as well as the spatial pattern of precipitation such as the correlations between stations and the Log‐Odds ratios. Also, the temporal correlation in the data at most of the rain gauges was successfully captured at seasonal timescales. Finally, the model was capable of reproducing extreme precipitation events. It modelled successfully the dry spells as well as most of the indices corresponding to ‘very wet events’, including the maximum 5‐day precipitation amount, the number of extreme precipitation events and their corresponding fraction of the total precipitation amount, better to the stations where the very wet events are not associated with local scale features that could not be captured by the large‐scale predictors. The weakest reproduced indices correspond to the ‘mean wet day precipitation’ and the ‘90th percentile of precipitation on wet days’ where the model captured their magnitude but not their interannual variability. Copyright © 2007 Royal Meteorological Society