Enabling And Validating Ensemble Data Assimilation Scenarios In JEDI-GEOS Using The SkyLab Workflow.

The Ensemble Data Assimilation (EDA) approach involves using multiple model simulations, each initialized with slightly different conditions. The goal is to account for uncertainty in the model and the observations and create an ensemble of analyses to represent the probability distribution of the analysis. From the EDA we can generate an ensemble of forecasts, used for decision making purposes and the ensemble contribution in the background error matrix representation ("ensemble B"). This method has been shown to be effective in improving the accuracy and uncertainty estimates of forecasts and has been widely used in weather forecasting applications.

The Joint Center for Satellite Data Assimilation (JCSDA) has developed a comprehensive workflow (SkyLab) to facilitate the execution of diverse EDA scenarios. These scenarios can involve different instruments, algorithm choices such as background error covariance or cost function, and even different weather models. The SkyLab workflow serves as the orchestrator, driving the underlying JEDI code (Joint Effort for Data assimilation Integration) to execute the different EDA experiments.

In this study, we present an overview of our workflow's architecture and an analysis of the results provided by EDA experiments in the context of JEDI-GEOS (Goddard Earth Observing System) assimilation: the primary focus is to compare the performance of the EDA with that of the traditional LETKF (Local Ensemble Transform Kalman Filter) approach used in operations. Key metrics such as ensemble spread and forecast error of the high resolution are analyzed, highlighting the strengths and weaknesses of an EDA approach.

The findings from this study will contribute to the ongoing efforts to optimize the JEDI-GEOS data assimilation system for more reliable atmospheric simulations.