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Development of a multimodel‐based seasonal prediction system for extreme droughts and floods: a case study for South Korea
Author(s) -
Sohn SooJin,
Tam ChiYung,
Ahn JoongBae
Publication year - 2012
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.3464
Subject(s) - downscaling , climatology , environmental science , precipitation , flood myth , forecast skill , variance (accounting) , quantitative precipitation forecast , scale (ratio) , warning system , meteorology , computer science , geography , telecommunications , cartography , accounting , archaeology , business , geology
An experimental, district‐level system was developed to forecast droughts and floods over South Korea to properly represent local precipitation extremes. The system is based on the Asia‐Pacific Economic Cooperation (APEC) Climate Center (APCC) multimodel ensemble (MME) seasonal prediction products. Three‐month lead precipitation forecasts for 60 stations in South Korea for the season of March to May are first obtained from the coarse‐scale MME prediction using statistical downscaling. Owing to the relatively small variance of the MME and regression‐based downscaling outputs, the downscaled MME (DMME) products need to be subsequently inflated. The final station‐scale precipitation predictions are then used to produce drought and flood forecasts on the basis of the Standardized Precipitation Index (SPI). The performance of three different inflation schemes was also assessed. Of these three schemes, the method that simply rescales the variance of predicted rainfall to that based on climate records, irrespective of the prediction skill or the DMME variance itself at a particular station, gives the best overall improvement in the SPI predictions. However, systematic biases in the prediction system cannot be removed by variance inflation. This implies that DMME techniques must be further improved to correct the bias in extreme drought/flood predictions. Overall, it is seen that DMME, in conjunction with variance inflation, can predict hydrological extremes with reasonable skill. Our results could inform the development of a reliable early warning system for droughts and floods, which is invaluable to policy makers and stakeholders in agricultural and water management sectors, and so forth and is important for mitigation and adaptation measures. Copyright © 2012 Royal Meteorological Society