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

Tuesday, 14 January 2020: 2:00 PM
254B (Boston Convention and Exhibition Center)
Yonghan choi, Univ. of California, Davis, Davis, CA; and S. H. Chen, C. C. Huang, K. Earl, C. Y. Chen, C. S. Schwartz, and T. Matsui

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 this, seven experiments are conducted using the Weather Research and Forecasting (WRF) dust model and the Gridpoint Statistical Interpolation (GSI) 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. All experiments use DA cycling to produce analyses at 6-h intervals throughout June 2015 that are then used to initialize 72-h forecasts. Analyses and forecasts are verified against MODIS AOD, Aerosol Robotic Network (AERONET) AOD, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter profiles.

The assimilation of MODIS AOD data clearly improves AOD analyses as well as forecasts up to 48-h 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-h, an 18-h reduction compared to assimilating both types of AOD data together. Of the three DA methods examined in this study, the hybrid and EnSRF methods, which benefit from the use of flow-dependent background error covariances, 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. Nevertheless, the 3D-Var and hybrid methods produce slightly better backscatter analyses and forecasts than EnSRF does, which is partially due to the use of an adjoint AOD operator in both the 3D-Var and hybrid methods.

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