The added value of dynamical downscaling of global Climate Forecast System (CFSv2) using WRF double-nested modeling system customized in Korea

Abstract:

This study investigates the added value of very high resolution simulations in Korea against global driving forcings based on the comparison with in-site observational data. The global meteorological fields obtained from the NOAA Climate Forecast System (CFSv2) are dynamically downscaled using the Weather Research and Forecasting (WRF) double-nested modeling system. It consists a mother domain (20-km resolution) and nested domain (5-km resolution) in order to better capture the region-specific climate pattern in Korea that is largely influenced by the area’s complex geographical features. In addition to optimizing the physical parameterizations, the impact of the spectral nudging is investigated whether the further constraint of large-scale forcing helps improve the performance of downscaled results. Our analysis focuses on quantifying the added value that can be obtained from the downscaled results over the driving global forecast system. Not only is an in-depth validation covering mean to extreme statistics performed against in-situ observational data, but the process-oriented validation (e.g. the partitioning of convective precipitation) is also investigated as a proxy for the improved realism related to small-scale processes.

Acknowledgments

This study was carried out with the support of “Research Program for Agricultural Science & Technology Development (Project No. PJ014882)”, National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.