Abstract
Abstract. Data assimilation (DA) approaches, including variational and the ensemble Kalman filter methods, provide a computationally efficient framework for solving the CO2 source–sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection–diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical real CO2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source–sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.
Highlights
Data assimilation (DA) is best known as a tool in numerical weather prediction (NWP; e.g., Swinbank, 2010) and has been applied to analyze complex data sets and estimate parameters in a variety of fields, including atmospheric constituent (e.g., Lahoz and Errera, 2010; Elbern et al, 2010), oceanographic (e.g., Haines, 2010), and land surface (e.g., Reichle, 2008; Houser et al, 2010) assimilation problems
The performance across the full examined time period is summarized in Fig. 4a, where all three approaches show a high CC (∼0.97), low root-mean-square difference (RMSD) (∼0.3 [M L−1 T −1]), and standard deviations (∼1.5 [M L−1 T −1]) that are similar to that of the true fluxes
We present a comparative assessment of two advanced DA approaches with the Batch inverse modeling (BIM) approach for the atmospheric CO2 inversion problem
Summary
Data assimilation (DA) is best known as a tool in numerical weather prediction (NWP; e.g., Swinbank, 2010) and has been applied to analyze complex data sets and estimate parameters in a variety of fields, including atmospheric constituent (e.g., Lahoz and Errera, 2010; Elbern et al, 2010), oceanographic (e.g., Haines, 2010), and land surface (e.g., Reichle, 2008; Houser et al, 2010) assimilation problems In all such applications, a DA system aims to optimally combine the information from available observations with a prior model estimate (or the background derived from a model forecast) based on their respective uncertainty estimates.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
