Development of updateable model output statistics (UMOS) system for air temperature over South Korea

In this study, a Updateable Model Output Statistics (UMOS) system has been developed for the forecast of 3-h temperature over South Korea using two significantly different models’ (Regional Data Assimilation and Prediction System (RDAPS) and Korea Meteorological Administration (KMA) Weather Research and Forecasting (WRF) model (KWRF)) outputs based on the Canadian UMOS system (Wilson and Vallee, 2002; 2003). The UMOS system is designed to consider the local climatology and the model’s forecasting skills. The 20 most frequently selected potential predictors for each season, station, and forecast projection time from the 67 potential predictors of the Model Output Statistics (MOS) system, were used as potential predictors of the UMOS system. The UMOS equations are developed by a weighted blending of the new and old model data, with weights chosen to emphasize the new model data while including enough old model data in the development to ensure stable equations and a smooth transition to dependency on the new model. The UMOS equations were updated regularly at a predefined time interval to consider the changes of covariance structure between the new model output and observations as the new model data increase. The validation results showed that seasonal mean bias, Root Mean Square Error (RMSE), and correlation coefficients for the total forecast projection times are ?0.379～0.055°C, 1.951～2.078°C, and 0.741～0.965, respectively. Although, the forecasting skills of UMOS system are very consistent without regard to the season and geographic location, the performance is slightly better in autumn and winter than in spring and summer, and better in coastal regions than in inland region. When we take into account the significant differences of the RDAPS and KWRF, the UMOS system can be used as a supplementary forecasting tool of the MOS system for 3-h temperature over South Korea. However, the UMOS system is very sensitive to the selected number and/or types of predictors. Therefore, more work is needed to enable the use of the UMOS system in operation, including tuning of the number and types of potential predictors and automation of the updating processes of the UMOS equations.