An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

This study evaluates the performance of dynamical downscaling global prediction generated from NOAA Climate Forecast System (CFSv2) at subseasonal time-scale against dense in-situ observational data in Korea. The Weather Research and Forecasting (WRF) double-nested modeling system customized over Korea is adopted to produce very high resolution simulation that presumably better resolves geographically diverse climate features. Two ensemble members CFSv2 starting with different initial conditions are downscaled for summer season (June-July-August) during past 10-year (2011–2020). comparison simulations nested domain (5 km resolution) WRF driving (0.5°) clearly demonstrates manner which can drastically improve daily mean temperature (Tmean) maximum (Tmax) both quantitative qualitative aspects. not only regional variability strongly tied topographical elevation, but also substantially lowers systematic cold bias seen CFSv2. added value far more evident Tmax than Tmean, indicates a skillful capturing extreme events. Accordingly, results show reasonable simulating plant heat stress index counts number days above 30°C degree accumulate exceeding using hourly temperature. potential capture variation Tmean-based represents accumulation reproductive growth mid-late maturing rice cultivars As likelihood hot temperatures projected increase Korea, skill predict ago-meteorological indices measuring effect on crop could have significant implications agriculture management practice.