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http://dx.doi.org/10.14191/Atmos.2022.32.2.149

Assessment of Performance on the Asian Dust Generation in Spring Using Hindcast Data in Asian Dust Seasonal Forecasting Model  

Kang, Misun (Forecast Research Department, National Institute of Meteorological Sciences)
Lee, Woojeong (Forecast Research Department, National Institute of Meteorological Sciences)
Chang, Pil-Hun (Forecast Research Department, National Institute of Meteorological Sciences)
Kim, Mi-Gyeong (Forecast Research Department, National Institute of Meteorological Sciences)
Boo, Kyung-On (Climate Research Department, National Institute of Meteorological Sciences)
Publication Information
Atmosphere / v.32, no.2, 2022 , pp. 149-162 More about this Journal
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 for the period of 1991~2016. Additionally, we evaluated the prediction skill of those variables depending on the combination of the initial dates in the sub-seasonal scale for the dust source region affecting South Korea. The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) from GloSea5-ADAM were compared to that from Synoptic observation and European Centre for medium range weather forecasts reanalysis v5, respectively, based on Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC) as evaluation criteria. In general, the Asian dust and meteorological variables in the source region showed high ACC in the prediction scale within one month. For all variables, the use of the initial dates closest to the prediction month led to the best performances based on MBE, RMSE, and ACC, and the performances could be improved by adjusting the number of ensembles considering the combination of the initial date. 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.
Keywords
Asian dust; Subseasonal prediction; GloSea5-ADAM; Hindcast;
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