An adjoint sensitivity analysis and 4D-Var data assimilation study of Texas air quality

Abstract

In this paper, we discuss the theory of adjoint sensitivity analysis and the method of 4D-Var data assimilation in the context of the Sulfur Transport Eulerian Model (STEM). The STEM atmospheric Chemical Transport Model (CTM) is used to perform adjoint sensitivity analysis and data assimilation over the State of Texas. We first demonstrate the use of adjoint sensitivity analysis for a receptor located at ground level in the Dallas Fort Worth (DFW) area. Simulations are carried out for three 36-h intervals in July 2004. One set of simulations focuses on a passive tracer, and illustrates the influence of meteorological conditions. The other results show the areas of influence associated with DFW ground level ozone, i.e. the areas where changes in precursors (O 3, NO 2, and HCHO) have the maximum impact on DFW ozone. Next, we employ data assimilation to optimize initial conditions of chemical fields and ground level emissions. We optimize the initial conditions for two episodes on 1 and 16 July 2004. Data assimilation makes use of AirNow ground level observations (for both episodes) and SCHIAMACHY NO 2 and HCHO observations from ENVISAT (for the 16 July episode). The re-analyzed chemical fields show considerable improved agreement with AirNow observations for non-initial conditions. We also perform inverse modeling of ground level emissions using data assimilation under an additional smoothness constraint. The results indicate higher NO 2 emission levels than in the current emission inventory in the DFW area, and lower emission levels in eastern Texas. The formaldehyde emissions are found to be larger than reported in a localized area near the Gulf Coast, and about at the reported level elsewhere. While the results obtained with the current state-of-the-art tools can help guide tuning of emission inventories, better constraints on the inverse problem are needed to obtain more rigorous quantitative estimates.