Prediction skill of Asian Dust Generation in hindcast data of Asian Dust Seasonal Forecasting Model (GloSea5-ADAM)

Abstract

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation using hindcasts of GloSea5-ADAM for the period of 1991~2016 for East Asia. GloSea5-ADAM incorporates the dust generation algorithm of the Asian Dust and Aerosol Model (ADAM) into the Global Seasonal Forecasting System version 5 (GloSea5). The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) depending on the combination of the initial dates in the sub-seasonal scale were compared to that from synoptic observation and ERA5 reanalysis data. The evaluation criteria used Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC). The Asian dust and meteorological variables in the source region (35~44°N, 90~115°E) showed high ACC in the prediction scale within one month. The best performances for all variables showed when the use of the initial dates closest to the prediction month based on MBE, RMSE, and ACC. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions. This result showed that the performances could be improved by adjusting the number of ensembles considering the combination of the initial date.