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Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods

Cited 9 time in wos
Cited 11 time in scopus
Title
Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods
Other Titles
다양한 자료동화 방법을 이용한 에어로졸 광학 두께 관측자료 동화의 북아프리카와 동대서양 먼지 예보에의 영향 평가
Authors
Choi, Yonghan
Chen, Shu­-Hua
Huang, Chu-­Chun
Earl, Kenneth
Chen, Chih-­Ying
Schwartz, Craig S.
Matsui, Toshihisa
Subject
Meteorology & Atmospheric Sciences
Keywords
ENSEMBLE KALMAN FILTERVARIATIONAL DATA ASSIMILATIONPARTICULATE MATTERDAILY MORTALITYBIAS CORRECTIONICE NUCLEATIONSATELLITE DATASAHARAN DUSTDESERT DUSTPART I
Issue Date
2020-04
Citation
Choi, Yonghan, et al. 2020. "Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods". JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 12(4): 1-30.
Abstract
This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three­dimensional variational (3D­Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48 hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only),AOD assimilation only improves AOD forecasts for up to 30 hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D­Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter.
URI
https://repository.kopri.re.kr/handle/201206/13030
DOI
http://dx.doi.org/10.1029/2019MS001890
Type
Article
Station
해당사항없음
Indexed
SCIE
Appears in Collections  
2020-2020, Earth System Model-based Korea Polar Prediction System (KPOPS-Earth) Development and Its Application to the High-impact Weather Events originated from the Changing Arctic Ocean and Sea Ice (20-20) / Kim, Joo-Hong (PE20090)
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