Enhancing Surface PM2.5 Air Quality Estimates in GEOS Using CATS Lidar Data

Abstract

Spaceborne lidar offers unique advantages for improving global estimates of fine particulate matter (${\text{PM}}_{2.5}$), traditionally limited by critical data gaps in the vertical dimension. Here, we present a new method to retrieve ${\text{PM}}_{2.5}$ relying on ensembles on aerosol extinction available within the GEOS Aerosol Data Assimilation. This study uses 1064-nm backscatter lidar data from the NASA Cloud-Aerosol Transport System (CATS) and model priors from the GEOS model. First, we developed a 1-D ensemble-based variational technique (1-D EnsVar) to perform vertically resolved retrievals of speciated aerosol extinction and surface ${\text{PM}}_{2.5}$. Next, we evaluated the performance of 1-D EnsVar retrievals of ${\text{PM}}_{2.5}$ and extinction through an independent validation using measurements from spaceborne, airborne, and ground-based platforms. This approach overcomes traditional limitations by leveraging the strengths of complementary vertical aerosol information from CATS and GEOS to better resolve speciated aerosol optical properties and mass. Assimilating CATS lidar data with the GEOS model reduced bias in surface ${\text{PM}}_{2.5}$ prediction by 1.1 $\mu g/m^3$ over the CONUS in 2016, potentially reducing model errors by up to 20%. Given the unique capability of CATS to process vertical profile data in near real-time, this work demonstrates the powerful utility of spaceborne lidar for improving air quality forecasting. While this pilot study is not yet performed within a cycling data assimilation system, the developed algorithm can easily be integrated in such systems. These results have broader implications for validating aerosol transport models, refining passive satellite retrievals of ${\text{PM}}_{2.5}$, and developing data assimilation techniques for future lidar platforms.