Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01

The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. While the analytical and variational optimization methods implemented in CIF are operational and have proved to be accurate and efficient, the initial ensemble method was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community, mainly owing to strong performance limitations and absence of localization methods. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments. As a first step, we chose to implement the serial and batch versions of the ensemble square root filter (EnSRF) algorithm because it is widely employed in the inversion community. We provide a comprehensive description of the technical implementation in CIF and the useful features it can provide to users. Finally, we demonstrate the capabilities of the CIF-EnSRF system using a large number of synthetic experiments over Europe with the flexible and scalable high-performance atmospheric transport model ICON-ART, exploring the system’s sensitivity to multiple parameters that can be tuned by users. As expected, the results are sensitive to the ensemble size and localization parameters. Other tested parameters, such as the number of lags, the propagation factors, or the localization function, can also have a substantial influence on the results. We also introduce and provide a way of interpreting a set of metrics that are automatically computed by CIF and that can help assess the success of inversions and compare them. This work complements previous efforts focused on other inversion methods within CIF. While ICON-ART has been used for testing in this work, the integration of these new ensemble algorithms enables any atmospheric transport model to perform inversions, fully leveraging CIF's robust capabilities.