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A multiphase CMAQ version 5.0 adjoint
Citation:
Zhao, S., A. Hakami, S. Capps, M. Turner, D. Henze, P. Percell, J. Resler, H. Shen, A. Russell, A. Nenes, A. Pappin, S. Napelenok, J. Bash, K. Fahey, J. Baek, G. Carmichael, C. Stanier, A. Sandu, AND T. Chai. A multiphase CMAQ version 5.0 adjoint. Geoscientific Model Development . Copernicus Publications, Katlenburg-Lindau, Germany, 13(7):2925-2944, (2020). https://doi.org/10.5194/gmd-13-2925-2020
Impact/Purpose:
It is often helpful for scientific and policy reasons to track the sources of atmospheric pollution with high degree of specificity. To accomplish this goal, various tools exists for current state-of-the-art atmospheric chemistry and physics models such as CMAQ. Here, we develop one of the most comprehensive and mathematically precise of such tools - the adjoint. We describe the implementation of the adjoint for CMAQ and demonstrate its capability with an application for public health in the U.S.
Description:
We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosols, cloud chemistry and dynamics, diffusion and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with Algorithmic Differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or Finite Difference Method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the Complex Variable Method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM/CVM. In an example application of the full, multiphase adjoint model, we provide the first estimates of how emissions of PM2.5 affect public health across the US .
URLs/Downloads:
DOI: A multiphase CMAQ version 5.0 adjoint
https://gmd.copernicus.org/articles/13/2925/2020/