Open AccessThree‐dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia
Author(s) -
Liu Zhiquan,
Liu Quanhua,
Lin HuiChuan,
Schwartz Craig S.,
Lee YenHuei,
Wang Tijian
Publication year - 2011
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2011jd016159
Subject(s) - aerosol , dust storm , aeronet , environmental science , data assimilation , moderate resolution imaging spectroradiometer , meteorology , weather research and forecasting model , storm , remote sensing , radiative transfer , atmospheric sciences , climatology , geology , satellite , physics , quantum mechanics , astronomy
Assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) retrieval products (at 550 nm wavelength) from both Terra and Aqua satellites have been developed within the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three‐dimensional variational (3DVAR) data assimilation system. This newly developed algorithm allows, in a one‐step procedure, the analysis of 3‐D mass concentration of 14 aerosol variables from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. The Community Radiative Transfer Model (CRTM) was extended to calculate AOD using GOCART aerosol variables as input. Both the AOD forward model and corresponding Jacobian model were developed within the CRTM and used in the 3DVAR minimization algorithm to compute the AOD cost function and its gradient with respect to 3‐D aerosol mass concentration. The impact of MODIS AOD data assimilation was demonstrated by application to a dust storm from 17 to 24 March 2010 over East Asia. The aerosol analyses initialized Weather Research and Forecasting/Chemistry (WRF/Chem) model forecasts. Results indicate that assimilating MODIS AOD substantially improves aerosol analyses and subsequent forecasts when compared to MODIS AOD, independent AOD observations from the Aerosol Robotic Network (AERONET) and Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, and surface PM 10 (particulate matter with diameters less than 10 μ m) observations. The newly developed AOD data assimilation system can serve as a tool to improve simulations of dust storms and general air quality analyses and forecasts.