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.
Highlights
Aerosols have received considerable attention globally due to their detrimental effects on air quality and public health (García‐Pando et al, 2014; Mallone et al, 2011; Morman & Plumlee, 2013; Pandolfi et al, 2014)
Schwartz et al (2014) evaluated the effects of assimilating aerosol observations on aerosol forecasts over the United States, while this study focuses on dust forecasts over North Africa and the East Atlantic during the summer, where dust transport is dominated by strong flow features (e.g., African easterly jet and African easterly waves)
Similar to the analysis evaluation procedure above, dust forecasts are verified by comparing model results against moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD), Aerosol Robotic Network (AERONET) AOD, and Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter observations
Summary
Aerosols have received considerable attention globally due to their detrimental effects on air quality and public health (García‐Pando et al, 2014; Mallone et al, 2011; Morman & Plumlee, 2013; Pandolfi et al, 2014). Schwartz et al (2014) assimilated MODIS AOD, surface PM2.5, and conventional meteorological observations over the United States using 3D‐Var, EnSRF, and hybrid variational‐ensemble methods within the NCEP GSI DA and found that aerosol forecasts initialized from hybrid analyses best matched AIRNow and Aerosol Robotic Network (AERONET) observations in terms of bias, RMSE, correlation, and Equitable Threat Score. Following the approach of Schwartz et al (2014), this study assesses the impact of assimilating AOD and other meteorological observations on dust forecasts over North Africa and the East Atlantic using the 3D‐Var, EnSRF, and hybrid DA methods within the GSI system and the Weather Research and Forecasting (WRF) dust model
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