1. A multiphase CMAQ version 5.0 adjoint
- Author
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Zhao, S. (Shunliu), Russell, M.G. (Matthew G.), Hakami, A. (Amir), Capps, S.L. (Shannon L.), Turner, M.D. (Matthew D.), Henze, D.K. (Daven K.), Percell, P.B. (Peter B.), Resler, J. (Jaroslav), Shen, H. (Huizhong), Russell, A.G. (Armistead G.), Nenes, A. (Athanasios), Pappin, A.J. (Amanda J.), Napelenok, S.L. (Sergey L.), Bash, J.O. (Jesse O.), Fahey, K.M. (Kathleen M.), Carmichael, G.R. (Gregory R.), Stanier, C.O. (Charles O.), Chai, T. (Tianfeng), Zhao, S. (Shunliu), Russell, M.G. (Matthew G.), Hakami, A. (Amir), Capps, S.L. (Shannon L.), Turner, M.D. (Matthew D.), Henze, D.K. (Daven K.), Percell, P.B. (Peter B.), Resler, J. (Jaroslav), Shen, H. (Huizhong), Russell, A.G. (Armistead G.), Nenes, A. (Athanasios), Pappin, A.J. (Amanda J.), Napelenok, S.L. (Sergey L.), Bash, J.O. (Jesse O.), Fahey, K.M. (Kathleen M.), Carmichael, G.R. (Gregory R.), Stanier, C.O. (Charles O.), and Chai, T. (Tianfeng)
- Abstract
We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model 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, aerosol dynamics and thermodynamics, 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 are used 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 columnmodel 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
- Published
- 2020
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