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

Abstract

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 perfor�mance 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) algo�rithm because it is widely employed in the inversion commu�nity. 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 sys�tem’s sensitivity to multiple parameters that can be tuned by users.