Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM_2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

Abstract

Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

Toward Improving Short‐Term Predictions of Fine Particulate Matter Over the United States Via Assimilation of Satellite Aerosol Optical Depth Retrievals

“…Data assimilation pushes both the modeled AOD and surface PM 2.5 distributions toward the observed distributions, but CMAQ still underestimated both the MODIS AOD and observed surface PM 2.5 . This behavior is in line with the previous studies assimilating MODIS AOD with the objective of improving surface PM 2.5 mass concentrations (e.g., McHenry et al, 2015;Saide et al, 2013;Schwartz et al, 2012;Tang et al, 2017).…”

“…At both the diurnal and daily scale, CMAQ simulations with and without assimilation significantly underestimate the observed PM 2.5 mass concentrations similar to the AOD. This behavior is in line with the previous studies where models continued to underestimate (e.g., McHenry et al, 2015;Schwartz et al, 2012) or overestimate (e.g., Saide et al, 2013) the PM 2.5 mass concentrations even after assimilating MODIS AOD. However, the assimilation for both the MET_BE and MET + EMIS_BE experiments reduces the model bias with the MET + EMIS_BE experiment yielding larger improvements.…”

“…The overestimation of SO 4 could be related to the use of older anthropogenic emissions from NEI 2011. The degradation of some of the aerosol components after the AOD assimilation has also been reported for California (Saide et al, ) as well as for CONUS (McHenry et al, ).…”

“…The increase in correlation coefficient in both the MET_BE and MET + EMIS_BE experiments is higher than that (0.03-0.06) reported in previous studies (e.g., McHenry et al, 2015;Schwartz et al, 2012) employing a variational data assimilation scheme over a domain similar to the one used here. Previous studies also reported a reduction in the MB of 0.03-0.07 (McHenry et al, 2015;Schwartz et al, 2012;Tang et al, 2017) that is higher than the MET_BE experiment but is comparable for the MET + EMIS_BE experiment. The assimilation of MODIS AOD using a different method called Optimal Interpolation (OI) produces larger increments in CMAQ AOD (e.g., Tang et al, 2017;Chai et al, 2017).…”

“…Thus, assimilation of MODIS AOD into CMAQ reduces the MB in CMAQ surface PM 2.5 from −5.2 μg/m 3 in the BKG experiment to −4.7 μg/m 3 (~10% reduction) in the MET_BE experiment and to −3.2 μg/m 3 (~38% reduction) in the MET + EMIS_BE experiment. In comparison, the MB reduced by~14% over CONUS in McHenry et al (2015).…”