Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction With Satellite Data for Rural Areas. Issue 1 (30th December 2021) - Zhang, Huanxin; Wang, Jun; García, Lorena Castro; Zhou, Meng; Ge, Cui; Plessel, Todd; Szykman, James; Levy, Robert C.; Murphy, Benjamin; Spero, Tanya L.
Abstract: This work serves as the second of a two‐part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multisatellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS‐Chem, WRF‐Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an 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 PM2.5 for next day over nonrural areas that have surface PM2.5 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 PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and nonrural areas, referred to as "extended ground truth" or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias‐corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two‐part study develops a multimodel and multi‐AOD bias‐correction technique that has the potential to improve PM2.5 forecasts in both rural and nonrural areas in near real time, and be readily implemented at state levels. Plain Language Summary: The U.S. Environmental Protection Agency's AirNow program reports current or forecasted air quality to the general public in the form of Air Quality Index (AQI). The forecasted AQI is made available by local and state air quality agencies across more than 500 cities across the U.S. However, since surface observations of particulate matter (PM) are primarily located in the urban areas, observation‐based AQI in the rural areas is limited, and either the current or the forecasted AQI from AirNow has large uncertainties that are difficult to assess, especially during the fire season. Satellite observation with large spatial coverage provides a promising opportunity to fill in the gaps in areas where observations are spare. Building upon our previous work, here we develop a statistical technique to improve surface PM forecasts in the rural areas of continental U.S. through the use of satellite observations of aerosols, surface observations, and air quality forecasting models. Assessment with the data from Interagency Monitoring of Protected Visual Environments (IMPROVE) network shows the promise of our technique. The technique is designed with the consideration of the forecast in near real time, and is efficient with minimal requirement of computing. Key Points: Ensemble‐based AOD from different sensors and algorithms shows better performance than individual ensemble member A multimodel and multi‐AOD ensemble bias correction via Kalman filter improves PM2.5 forecasts in rural areas lack of ground observations An ensemble framework for producing the single best PM2.5 forecast for next day in near real time via bias correction is established