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. Regional atmospheric CO2 inversions commonly use Lagrangianparticle trajectory model simulations to calculate the required influencefunction, which quantifies the sensitivity of a receptor to flux sources. Inthis paper, an adjoint-based four-dimensional variational (4D-Var)assimilation system, WRF-CO2 4D-Var, is developed to provide an alternativeapproach. This system is developed based on the Weather Research andForecasting (WRF) modeling system, including the system coupled to chemistry(WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with dataassimilation (WRFDA), all in version 3.6. In WRF-CO2 4D-Var, CO2 is modeled as a tracer and itsfeedback to meteorology is ignored. This configuration allows most WRFphysical parameterizations to be used in the assimilation system withoutincurring a large amount of code development. WRF-CO2 4D-Var solves for theoptimized CO2 flux scaling factors in a Bayesian framework. Twovariational optimization schemes are implemented for the system: the firstuses the limited memory Broyden–Fletcher–Goldfarb–Shanno (BFGS)minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugategradient (CG) in an incremental approach. WRFPLUS forward, tangent linear,and adjoint models are modified to include the physical and dynamicalprocesses involved in the atmospheric transport of CO2. The system istested by simulations over a domain covering the continental United States at48 km × 48 km grid spacing. The accuracy of the tangent linear andadjoint models is assessed by comparing against finite differencesensitivity. The system's effectiveness for CO2 inverse modeling is testedusing pseudo-observation data. The results of the sensitivity and inversemodeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var forregional CO2 inversions.

Development of the WRF-CO2 4D-Var assimilation system v1.0