A Neural Network preprocessing model for OMI Aerosol Optical Depth data assimilation

A regional chemical transport model assimilated with daily mean satellite and ground based Aerosol Optical Depth (AOD) observations is used to produce three dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for pre-processing AOD based on Neural Network (NN) is proposed. The aerosol forecasts involve a two-phase process assimilation, and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The model state is then adjusted to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.