Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods. (4th April 2020)

• Record Type: Journal Article
• Authors: Choi, Yonghan; Chen, Shu‐Hua; Huang, Chu‐Chun; Earl, Kenneth; Chen, Chih‐Ying; Schwartz, Craig S.; Matsui, Toshihisa
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-04
• Subjects: aerosol assimilation -- dust model -- GSI -- aerosol optical depth (AOD) -- deep blue AOD -- Sahara Desert; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS001890
• Languages: English
• ISSNs: 1942-2466

Abstract

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 three‐dimensional variational (3D‐Var), 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 3D‐Var 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. Key Points: Assimilating MODIS AOD data improves AOD forecasts up to 48 hr, but the improvement can last for only 30 hr when deep blue data are excluded The hybrid and EnSRF methods can produce better AOD analyses and forecasts than the 3D‐Var method does Assimilation of MODIS AOD data has very little impact on analyses and/or forecasts of aerosol backscatter profile