Development of the WRF-CO2 4DVar assimilation system
Tao Zheng1, Nancy French2, and Martin Baxter31Department of Geography, Central Michigan University, Mount Pleasant, MI. USA 2Michigan Technological Research Institute, Michigan Technological University, Ann Arbor, MI. USA 3Department of Earth and Atmospheric Sciences, Central Michigan University, Mount Pleasant, MI. USA
Received: 23 Nov 2016 – Accepted: 08 Dec 2016 – Published: 12 Dec 2016
Abstract. Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function. To provide an alternative, we developed an adjoint based four-dimensional variational (4DVar) assimilation system, WRF-CO2 4DVar. This system is developed based on the Weather Research and Forecasting (WRF) model system, including WRF-Chem, WRFPLUS, and WRFDA. In WRF-CO2 4DVR, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4DVar solves for the optimized CO2 emission scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the L-BFGS-B and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. We modified WRFPLUS forward, tangent linear, and adjoint models to include CO2 related processes. The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models are assessed by comparing against finite difference sensitivity. The system's effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4DVar for regional CO2 inversions.
Zheng, T., French, N., and Baxter, M.: Development of the WRF-CO2 4DVar assimilation system, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-289, in review, 2016.