Improving the EnSRF in the Community Inversion Framework: a case study with ICON-ART 2024.01

• DOI: 10.5194/egusphere-2024-2197
• Publication Date: 2024-08-30T13:13:20Z
• Authors: Joël Thanwerdas; Antoine Berchet; Lionel Constantin; Aki Tsuruta; Michael Steiner; Friedemann Reum; Stephan Henne; Dominik Brunner
• Keywords: Icon

Abstract. The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. While the analytical and variational optimization implemented in CIF are operational have proved to be accurate efficient, initial ensemble method was found incomplete could hardly compared other employed inversion community, mainly owing strong performance limitations absence of localization methods. In this paper, we present evaluate a more efficient implementation mode. As first step, chose implement serial batch versions Ensemble Square Root Filter (EnSRF) algorithm because it is widely community. We provide comprehensive description technical useful features can users. Finally, demonstrate capabilities CIF-EnSRF system using large number synthetic experiments over Europe, exploring system’s sensitivity multiple parameters that tuned by expected, results sensitive size parameters. Other tested parameters, such as lags, propagation factors, or function also substantial influence on results. introduce way interpreting set metrics automatically computed help assessing success inversions comparing them. This work complements previous efforts focused within CIF. With integration these new algorithms, any chemical transport model (CTM), including models without existing adjoint, now perform CIF, leveraging its robust capabilities.