Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas

This work is the first of a two-part study that aims to develop a computationally efficient bias correction framework to improve surface PM(2.5)forecasts in the United States. Here, an ensemble-based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM(2.5)and applied to three (GEOS-Chem, WRF-Chem, and WRF-CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM(2.5)mass concentration by 20-50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least-square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM(2.5)observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM(2.5)at the national scale. Our findings provide the foundation for the second part of this study that uses satellite-based aerosol optical depth (AOD) products to further improve the forecast of surface PM(2.5)in rural areas by performing statistical analysis of model output.