Improving surface PM forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas.

This work serves as the second of a two-part study to improve surface PM forecasts in the continental U.S. through the integrated use of multi-satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multi chemical transport model (CTM) (GEOS-Chem, WRF-Chem and CMAQ) outputs and ground observations. In part I of the study, a multi-model ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM for next day over non-rural areas that have surface PM measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125 - 300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM in rural areas from multiple models through the AOD spatial pattern between these areas and non-rural areas, referred to as "extended ground truth" or EGT, for today. Lastly, we applied the KF technique to update the bias in the forecast for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM from three models for both today and next day show the best performance. Together, the two-part study develops a multi-model and multi-AOD bias correction technique that has the potential to improve PM forecasts in both rural and non-rural areas in near real time, and be readily implemented at state levels.