Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe

We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimere model in order to improve the modeling of fine particulate matter (PM2.5) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM2.5 concentrations and the AOD field could be reduced from -34 to -15% and from -45 to -27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD(550) over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimere model and can be further developed to improve modeled PM2.5 and ozone concentrations.

Automated summary

This study assimilated MODIS AOD data into the Chimere model in Europe to improve modeling of PM2.5 concentrations and AOD field. Evaluation showed that data assimilation technique can push the model results closer to surface observations, but the difficulty in distributing AOD over different particle sizes may lead to false alarms.