A Case Study of Bias Correction and the Dynamical Downscaling of CFSv2 S2S Forecasts Using a WRF Model: Heatwave in 2018 over South Korea

Abstract

Extreme weather events caused by climate change affect the growth of crops, requiring reliable weather forecasts. In order to provide day-to-season seamless forecasting data for the agricultural sector, improving the forecasting performance of the S2S period is necessary. A number of studies have been conducted to improve prediction performance based on the bias correction of systematic errors in GCM or by producing high-resolution data via dynamic detailing. In this study, a daily simple mean bias correction technique is applied on CFSv2 (∼100 km) data. We then use case studies to evaluate how beneficial the precision of the high-resolution RCM simulation is in improving S2S prediction performance using the bias-corrected lateral boundary. Based on our examination of 45-day sequences of WRF simulations with 27–9–3 km resolution, it can be concluded that a higher resolution is correlated with better prediction in the case of the extreme heatwave in Korea in 2018. However, the effect of bias correction in improving predictive performances is not significant, suggesting that further studies on more cases are necessary to obtain more solid conclusions in the future.