The impacts of background error covariance on particulate matter assimilation and forecast: An ideal case study with a modal aerosol model over China

Abstract

Accurate estimation of background error covariance (BEC) is the key to successful data assimilation (DA). In aerosol three-dimensional variational (3DVAR) DA, the National Meteorological Center (NMC) method is typically applied to estimate BEC, which uses the difference between forecasts of dissimilar lengths valid at the common time. The difference will be considerably small when the underestimation of the aerosol is caused by the lack of emissions or the missing of chemical progress, which makes the aerosol concentration field too difficult to constrain. In this study, a modified module for adjusting the BEC of individual aerosol species was developed in the Gridpoint Statistical Interpolation (GSI) 3DVAR system. This module was mainly utilized to expand the standard deviations and the horizontal length scales of BEC for the specified aerosol components by multiplying an adjustment factor. The results of the impacts of BEC on PM10 24-hour forecast indicated that the horizontal length scales take a relatively more important role than the standard deviations. The horizontal length scales affect the influence sphere more significantly, which might be crucial for the longer length forecast. Moreover, the larger and the wider differences of the aerosol initial conditions produced by DA, the longer duration of DA benefits. Using the original BEC, the 24-hour forecast of PM10 reduced fractional error by 13%, while using the modified BEC in DA can decline fractional error by 29%. More work needs to be conducted to investigate how to modify the aerosol BEC in 3DVAR, or how to generate a suitable BEC, which is crucial for aerosol forecast and analysis, especially during the aerosol-polluted period.