A Strategy for Research in the Application of Dynamic Data Assimilation to Air Quality Forecasting

William R. Stockwell

Howard University

Although meteorological data assimilation has been successfully applied to improve forecasts the community is in the early stages of developing a system that couples the assimilation of weather elements (wind, pressure, temperature and water vapor) with chemical species (e.g., nitrogen oxides, volatile organic compounds, ozone and PM).  The strategy includes the testing of the viability of both dynamic/deterministic schemes (e.g., 3DVAR) and stochastic/dynamic schemes (e.g., ensemble Kalman Filter). These approaches will rely on available National Centers for Environmental Prediction (NCEP) prediction models presently available (or soon to be available) such as the Weather Research and Forecast Model (WRF)/CMAQ (Community Multi-scale Air Quality model). We will use the 3DVAR scheme developed at NCEP, the so-called Grid-Point Statistical Interpolation system (GSI). In essence, the assimilation scheme(s) will rely on a prediction model that is faithful to the evolution of the meteorological/chemical state of the atmosphere. With output from this model serving as background, the observations are combined with this output to produce the estimate of the system state. The estimate is found optimally, i.e., the error in the estimate is minimized by properly accounting for the relative goodness of forecast compared to observation – statistics contained in the error covariance matrix (error variances and covariances associated with the meteorology and chemistry). The construction of this matrix for the combined problem is a major component of the proposed research.  In parallel with the work on the operational system, we will perform data assimilation experiments that take advantage of data collected in field programs. We are especially interested in comparing air quality forecasts in geographically different regions – regions that exhibit differences in terrain and ocean-atmosphere-land interactions.