Statistical downscaling methods based on
            APCC
            multi‐model ensemble for seasonal prediction over South Korea

Abstract The present study investigates the predictive skill of the North American Multi-Model Ensemble (NMME) system for intraseasonal-to-interannual (ISI) prediction with focus on southeastern U.S. precipitation. The southeastern United States is of particular interest because of the typically short-lived nature of above- and below-normal extended rainfall events allowing for focus on seasonal prediction, as well as the tendency for more predictability in the winter months. Included in this study is analysis of the forecast quality of the NMME system when predicting above- and below-normal rainfall and individual rainfall events, with particular emphasis on results from the 2007 dry period. Both deterministic and probabilistic measures of skill are utilized in order to gain a more complete understanding of how accurately the system predicts precipitation at both short and long lead times and to investigate the multimodel aspect of the system as compared to using an individual predictive model. The NMME system consistently shows low systematic error and relatively high skill in predicting precipitation, particularly in winter months as compared to individual model results.

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Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in eastern Australia

Weber, T., McPhee, M.J. and Anderssen, R.S. (eds) MODSIM2015, 21st International Congress on Modelling and Simulation ◽

10.36334/modsim.2015.g4.wang ◽

2015 ◽

Keyword(s):

Temperature Change ◽

Statistical Downscaling ◽

Extreme Temperature ◽

Model Ensemble ◽

Statistical Downscaling Method ◽

Downscaling Method ◽

Eastern Australia ◽

Ensemble Projections

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Predictability of Persistent Thailand Rainfall during the Mature Monsoon Season in 2011 Using Statistical Downscaling of CGCM Seasonal Prediction

Monthly Weather Review ◽

10.1175/mwr-d-14-00228.1 ◽

2015 ◽

Vol 143 (4) ◽

pp. 1166-1178 ◽

Cited By ~ 2

Author(s):

Yukiko Imada ◽

Shinjiro Kanae ◽

Masahide Kimoto ◽

Masahiro Watanabe ◽

Masayoshi Ishii

Keyword(s):

Statistical Downscaling ◽

El Niño ◽

Rainy Season ◽

El Nino ◽

Seasonal Prediction ◽

Precipitation Pattern ◽

Prediction System ◽

Central Pacific ◽

Central Pacific El Niño ◽

Local Rainfall

Abstract Predictability of above-normal rainfall over Thailand during the rainy season of 2011 was investigated with a one-tier seasonal prediction system based on an atmosphere–ocean coupled general circulation model (CGCM) combined with a statistical downscaling method. The statistical relationship was derived using singular value decomposition analysis (SVDA) between observed regional rainfall and the hindcast of tropical sea surface temperature (SST) from the seasonal prediction system, which has an ability to forecast oceanic variability for lead times up to several months. The downscaled product of 2011 local rainfall was obtained by combining rainfall patterns derived from significant modes of SVDA. This method has the advantage in terms of flexibility that phenomenon-based statistical relationships, such as teleconnections associated with El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), or the newly recognized central Pacific El Niño, are considered separately in each SVDA mode. The downscaled prediction initialized from 1 August 2011 reproduced the anomalously intense precipitation pattern over Indochina including northern Thailand during the latter half of the rainy season, even though the direct hindcast from the CGCM failed to predict the local rainfall distribution and intensity. Further analysis revealed that this method is applicable to the other recent events such as heavy rainfall during the rainy seasons of 2002 and 2008 in Indochina.

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