Improved Dust Forecast by Assimilating MODIS IR-Based Nighttime AOT in the ADAM2 Model

Lee, Sang-Sam; Lee, Eun-Hee; Sohn, Byung-Ju; Lee, Hee Choon; Cho, Jeong Hoon; Ryoo, Sang-Boom

doi:10.2151/sola.2017-035

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…The Korea Meteorological Administration (KMA) has operated the Asian Dust Aerosol Model (ADAM) to forecast Asian dust events since 2007. ADAM has been gradually improved in various studies (Park et al, 2010;Lee et al, 2017Lee et al, , 2019Ryoo et al, 2020aRyoo et al, , 2020b; version 3 (ADAM3) is currently in use. ADAM3 has been extended via one-way coupling with the Ensemble Prediction System for Global (EPSG) to provide a probabilistic forecast of Asian dust events and deliver more reliable information for forecasting severe aerosol events.…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of ADAM3 Ensemble Prediction System

Kim

Cho

Ryoo

2023

SOLA

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Forecasts of discrete events, such as precipitation, dust storms, and typhoons, can be deterministic, categorical, or probabilistic. Ensemble-based probabilistic predictions generate more consistent forecasts than individual deterministic models. We introduce and evaluate the Ensemble Prediction System of Asian Dust Aerosol Model 3, which generates probability forecasts of Asian dust. Probability forecasts were produced for 300 μgm -3 PM10 mass concentrations, according to the air-quality standards of the Ministry of Environment of South Korea and Korea Meteorological Administration. Crisis-level information was produced to categorize the dust risk level for Asia, using a risk matrix. The model's performance was evaluated using a 2 × 2 contingency table, the Brier skill score, and a reliability diagram. For skill score evaluation via the contingency table, the average hit rate and threat score were 0.46 and 0.34 for the best three sites, Jurihe, Erenhot, Kim et al., Development and assessment of ADAM3 Ensemble Prediction System 2 and Wulatezhongqi. The Brier skill score was positive for approximately 60% of stations, with the highest (0.401) and lowest (-2.038) values in Erenhot and Yanan, respectively. The reliability diagram revealed overestimated Asian dust frequencies for all stations.Although the stations were located within the same regions, their skill scores differed.Regional characteristics of skill scores should be further investigated in the future.

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Section: Introductionmentioning

confidence: 99%

Development and Assessment of ADAM3 Ensemble Prediction System

Kim

Cho

Ryoo

2023

SOLA

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“…The Korea Meteorological Administration (KMA) has operated an Asian Dust Aerosol Model (ADAM) for forecasting Asian dust events since 2007. ADAM performance has been improved gradually by various studies [11][12][13][14][15]. Version 3 of ADAM (ADAM3) is currently an intercomparison model for Asian dust prediction within the Asian node of the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) of the WMO [1].…”

Section: Introductionmentioning

confidence: 99%

Improvements of ADAM3 by Incorporating New Dust Emission Reduction Formulations Based on Real-Time MODIS NDVI

2021

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Dust events in Northeast Asia have several adverse effects on human health, agricultural land, infrastructure, and transport. Wind speed is the most important factor in determining the total dust emission at the land surface; however, various land-surface conditions must be considered as well. Recently, the Korea Meteorological Administration updated the dust emission reduction factor (RF) in the Asian Dust Aerosol Model 3 (ADAM3) using data from the normalized difference vegetation index (NDVI) of the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the improvements of ADAM3 according to soil types. We incorporated new RF formulations in the evaluation based on real-time MODIS NDVI data obtained over the Asian dust source regions in northern China during spring 2017. This incorporation improved the simulation performance of ADAM3 for the PM10 mass concentration in Inner Mongolia and Manchuria for all soil types, except Gobi. The ADAM3 skill scores for sand, loess, and mixed types in a 24 h forecast increased by 6.6%, 20.4%, and 13.3%, respectively, compared with those in forecasts employing the monthly RF based on the NDVI data. As surface conditions in the dust source regions continually change, incorporating real-time vegetation data is critical to improving performance of dust forecast models such as ADAM3.

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“…The Korea Meteorological Administration (KMA) has also operated the Asian Dust Aerosol Model (ADAM) for forecasting Asian dust events since 2007. The ADAM model has been continuously improved through various research projects [17][18][19]. Most recently, KMA developed version 3 of the ADAM model (ADAM3) to improve operational Asian dust forecasting by incorporating anthropogenic emissions, the daily dust emissions reduction factor [20], and assimilations of particulate matter (PM) data from surface stations and satellite-observed aerosol optical depth (AOD) data into the ADAM2 model.…”

Section: Introductionmentioning

confidence: 99%

“…To remove the effect of anthropogenic aerosols, Hong et al [21] used the difference between PM10 and PM2.5 in determining an Asian dust day. Note that the PM2.5 data in A daily dust emission reduction factor was applied to ADAM3 on the basis of the updated moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data in order to reflect recent vegetation effects in dust emissions in the Asian dust source regions [19]. A sensitivity study [19] showed that the new reduction factor contributes positively to the forecast skill in the dust source as well the downwind regions.…”

Section: Introductionmentioning

confidence: 99%

Performance of KMA-ADAM3 in Identifying Asian Dust Days over Northern China

2020

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Recently, the Korea Meteorological Administration developed Asian Dust Aerosol Model version 3 (ADAM3) by incorporating additional parameters into ADAM2, including anthropogenic particulate matter (PM) emissions, modification of dust generation by considering real-time surface vegetation, and assimilations of surface PM observations and satellite-measured aerosol optical depth. This study evaluates the performance of ADAM3 in identifying Asian dust days over the dust source regions in Northern China and their variations according to regions and soil types by comparing its performance with ADAM2 (from January to June of 2017). In all regions the performance of ADAM3 was markedly improved, especially for Northwestern China, where the threat score (TS) and the probability of detection (POD) improved from 5.4% and 5.5% to 30.4% and 34.4%, respectively. ADAM3 outperforms ADAM2 for all soil types, especially for the sand-type soil for which TS and POD are improved from 39.2.0% and 50.7% to 48.9% and 68.2%, respectively. Despite these improvements in regions and surface soil types, Asian dust emission formulas in ADAM3 need improvement for the loess-type soils to modulate the overestimation of Asian dust events related to anthropogenic emissions in the Huabei Plain and Manchuria.

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Improved Dust Forecast by Assimilating MODIS IR-Based Nighttime AOT in the ADAM2 Model

Cited by 10 publications

References 26 publications

Development and Assessment of ADAM3 Ensemble Prediction System

Development and Assessment of ADAM3 Ensemble Prediction System

Improvements of ADAM3 by Incorporating New Dust Emission Reduction Formulations Based on Real-Time MODIS NDVI

Performance of KMA-ADAM3 in Identifying Asian Dust Days over Northern China

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