Statistical Downscaling of Precipitation in Korea Using Multimodel Output Variables as Predictors

Abstract

A pattern projection downscaling method is applied to predict summer precipitation at 60 stations over Korea. The predictors are multiple variables from the output of six operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction was made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of six model downscaled precipitation forecasts using the best predictors and will be referred to as ''DMME.'' It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse-resolution predictions of general circulation models. Although Korea's precipitation is strongly influenced by local mountainous terrain, DMME performs well at 59 stations with correlation skill significant at the 95% confidence level. The improvement of the prediction skill is attributed to three steps: coupled pattern selection, optimal predictor selection, and the multimodel downscaled precipitation ensemble. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected, can be used to make skillful predictions of the local precipitation by using appropriate statistical downscaling methods.

Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection

“…We adopt a simple composite MME method (Peng et al 2002;Kang et al 2009;Lee et al 2008Lee et al , 2009Lee et al , 2011aLee et al , 2013a, which assigns equal weights to the ensemble mean predictions of individual models. The performance of this method is on par with the best available operational MME techniques ).…”

Six month–lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

“…The regression-based coupled pattern projection method with optimal predictor selection was used for statistical downscaling [Kang et al, 2009;Sohn et al, 2012b]. The novelty of this approach is the use of model output statistics [Wilks, 1995] for predicting meteorological variables on the station scale.…”

“…Using a single predictor therefore might not be adequate for specifying climate variations for all stations. Also, to avoid overestimation of skill scores, the above downscaling procedure was carried out based on a "leave-one-out" cross-validation framework [Kang et al, 2009;Sohn et al, 2012b]. Finally, cross-validated correlation coefficients were computed in order to assess the skill based on each individual predictor, and the best predictor as well as the associated transfer function was adopted for statistical downscaling.…”

“…This study evaluates the potential of using one-tier multimodel ensemble (MME) products for long-lead drought predictions. Kang et al [2009] used statistically downscaled global model outputs to derive regional climate information. Here, we developed a 6 month-lead prediction system for hydrological extremes over 60 stations in South Korea based on downscaled MME (DMME) rainfall and temperature products.…”

Assessment of Multimodel Ensemble Seasonal Hindcasts for Satellite-Based Rice Yield Prediction

“…These six dynamical seasonal prediction models are summarized in Table 1. A simple composite method (SCM), which is a simple ensemble method assigning equal weights to each GCM (Kang et al, 2009;Lee et al, 2011;Peng et al, 2002), was used to construct multimodel ensemble predictions for this study. The performance of this equal weighing system is comparable to that of the best available operational MME techniques (Peng et al, 2002;Lee et al, 2009).…”