JEDI goes to space: First steps in applying an atmospheric data assimilation framework to space weather

Data assimilation has the potential to improve the accuracy and reliability of space weather models by helping maintain consistency with observational data. It is widely employed in weather forecasting and other atmospheric modeling, but its application in space weather modeling is less mature at present compared with meteorology. Utilizing data assimilation tools developed for atmospheric modeling could help accelerate the application of data assimilation to space weather. One challenge in doing so is that space weather models often use coordinate systems and grid geometries that are substantially different from those used in atmospheric modeling, such as the use of geomagnetic coordinates. In this work we demonstrate the use of the Joint Effort for Data assimilation Integration (JEDI) framework on an ionospheric model, PyIRI (Python International Reference Ionosphere). We demonstrate the use of JEDI's ensemble Kalman filter (EnKF) to update an ensemble of PyIRI states to fit a set of synthesized observations, and discuss our approach to adapting JEDI to work with ionospheric forecasts.