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

Abstract

Abstract. Regional atmospheric CO 2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources. In this paper, an adjoint-based four-dimensional variational (4D-Var) assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach. This system is developed based on the Weather Research and Forecasting (WRF) modeling system, including the system coupled to chemistry (WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with data assimilation (WRFDA), all in version 3.6. In WRF-CO2 4D-Var, CO 2 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 4D-Var solves for the optimized CO 2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden-Fletcher-GoldfarbShanno (BFGS) minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO 2 . 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 is assessed by comparing against finite difference sensitivity. The system's effectiveness for CO 2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO 2 inversions.

Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations

“…Such approaches include establishing a suitable WRF-Chem model configuration for each particular site through validations against meteorological measurements [40], assimilating meteorological observations during simulations [41,42], and using ensemble transport model simulations. In addition, variational assimilation approaches can be used to address the sensitivity of the estimation to the transport model non-linearity [43,44].…”

Fundamentals of data assimilation applied to biogeochemistry

“…Rödenbeck, 2005;Chevallier et al, 2005;Henze et al, 2007;Bergamaschi et al, 2007;Liu et al, 2014;Wilson et al, 2014) and for regional models (e.g. Brioude et al, 2011;Broquet et al, 2011;Guerrette and Henze, 2015;T. Zheng et al, 2018).…”

“…Peters et al, 2010) for Europe or Kim et al (2014) for Asia. Other applications of the EnKF to the estimation of surface sources include Zupanski et al (2007) and Feng et al (2009). One problem common to all these models was a potential time delay between target variables and observations.…”

Fundamentals of Data Assimilation applied to biogeochemistry

“…Analytical methods are prized for 25 exact calculation of posterior covariance and so are widely used in network design (references (e.g. Gloor et al, 2000;Patra et al, 2003;Hungershoefer et al, 2010;Kaminski and Rayner, 2016 (2015); Zheng et al (2018b) for regional models. Most of the effort has centred on improving our knowledge of sources and sinks (e.g.…”