Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

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Abstract
Three types of observations, aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer, surface particulate matter with diameters less than 2.5 (PM2.5) and 10 μm (PM10), and aerosol extinction coefficient (AEXT) profiles from ground‐based lidars, were separately and simultaneously assimilated using the Weather Research and Forecasting Model with the Chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART). Two cases in June and November 2018 were selected over middle and eastern China. Experiments assimilating single‐type and multiple observations were evaluated by cross validating their analysis and forecast against the three observation types. Compared to the experiment without data assimilation (DA), DA of single‐type observations is always closer to the type of observations assimilated. However, DA of aerosol optical depth or AEXT sometimes significantly degraded the error performance for PM2.5. This problem is caused by the inconsistency of bias tendencies when modeling aerosol optical properties and surface aerosol mass. It is found that WRF‐Chem tends to predict dryer air within the boundary layer over eastern China, which may have played a role in the underestimation of AEXT even when PM2.5 was overestimated. After applying a simple bias correction (BC), the problem was alleviated. DA of multiple observations with BC gives the best overall error performance when validated against all types of observations and even performs better than any DA of single‐type observations experiments in reproducing AEXT profiles. The results illustrate that BC is important in DA of multiple observations and that the simultaneous DA of aerosol observations with different vertical information can work synergistically to improve aerosol forecasts.
Funding Information
  • National Natural Science Foundation of China (91544230, 41575145, 41621005, 41601193)
  • Natural Science Foundation of Jiangsu Province (BK20160622)
  • Natural Science Foundation of Jiangsu Province (BK20160622)
  • National Natural Science Foundation of China (41621005, 41575145, 41601193, 91544230)

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