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Seasonal Asian Dust Forecasting Using GloSea5-ADAM

National Institute of Meteorological Sciences, 33, Seohobuk-ro, Seogwipo-si, Jeju-do 63568, Korea
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(5), 526;
Received: 6 April 2020 / Revised: 14 May 2020 / Accepted: 15 May 2020 / Published: 20 May 2020
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
The springtime dust events in Northeast Asia pose many economic, social, and health-related risks. Statistical models in the forecasting of seasonal dust events do not fully account for environmental variations in dust sources due to climate change. The Korea Meteorological Administration (KMA) recently developed the GloSea5-ADAM, a numerically based seasonal dust forecasting model, by incorporating the Asian Dust and Aerosol Model (ADAM)’s emission algorithm into Global Seasonal Forecasting Model version 5 (GloSea5). The performance of GloSea5 and GloSea5-ADAM in forecasting seasonal Asian dust events in source (China) and leeward (South Korea) regions was compared. The GloSea5-ADAM solved the limitations of GloSea5, which were mainly attributable to GloSea5′s low bare-soil fraction, and successfully simulated 2017 springtime dust emissions over Northeast Asia. The results show that GloSea5-ADAM’s 2017 and 2018 forecasts were consistent with surface PM10 mass concentrations observed in China and South Korea, while there was a large gap in 2019. This study shows that the geographical distribution and physical properties of soil in dust source regions are important. The GloSea5-ADAM model is only a temporary solution and is limited in its applicability to Northeast Asia; therefore, a globally applicable dust emission algorithm that considers a wide variety of soil properties must be developed. View Full-Text
Keywords: Asian dust; springtime; seasonal forecasting; GloSea5; GloSea5-ADAM; ADAM Asian dust; springtime; seasonal forecasting; GloSea5; GloSea5-ADAM; ADAM
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MDPI and ACS Style

Ryoo, S.-B.; Lim, Y.-K.; Park, Y.-S. Seasonal Asian Dust Forecasting Using GloSea5-ADAM. Atmosphere 2020, 11, 526.

AMA Style

Ryoo S-B, Lim Y-K, Park Y-S. Seasonal Asian Dust Forecasting Using GloSea5-ADAM. Atmosphere. 2020; 11(5):526.

Chicago/Turabian Style

Ryoo, Sang-Boom, Yun-Kyu Lim, and Young-San Park. 2020. "Seasonal Asian Dust Forecasting Using GloSea5-ADAM" Atmosphere 11, no. 5: 526.

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