Development of a chemical data assimilation system for air quality reanalysis

Abstract:

Air quality simulations suffer from errors and biases due to errors in initial conditions, uncertainties in input emissions, and poor understanding of some of the air quality processes. These errors and biases can be partially addressed via assimilation of space-borne observations of atmospheric composition available from the National Aeronautics and Space Administration (NASA) satellites. With the objective of improving long-term air quality simulations for use in the trend analysis in unmonitored areas and studies linking air quality to human health, we have developed a chemical data assimilation system to simultaneously assimilate aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), carbon monoxide (CO) retrievals from the Measurement of Pollution in the Troposphere (MOPITT), and tropospheric nitrogen dioxide (NO₂) retrievals from the Ozone Monitoring Instrument (OMI) in the Community Multiscale Air Quality (CMAQ) model. Air quality simulations have been performed over the contiguous United States (CONUS) at 12 x 12 km² for the summer of 2018. This presentation will discuss the design of the chemical data assimilation system and its impact on air quality simulations evaluated through comparison with a variety of meteorological and air quality observations available from the Environmental Protection Agency (EPA) monitoring network. The chemical data assimilation significantly pushes the model AOD, CO profile, and tropospheric NO2 retrievals towards the satellite retrievals. The assimilation of MODIS AOD is found to improve surface PM_2.5 simulations at both the diurnal and daily scales. The impact of assimilation on surface ozone and related species is ongoing and will be shown during the presentation.