Specification and Forecasting of Thermosphere Neutral Density with a Whole Atmosphere Data Assimilation System

By statistically combining observations with a background numerical model, data assimilation systems can greatly benefit scientific understanding of the middle and upper atmospheres. They are additionally useful for initializing short-term space weather forecasts. Experiments performed using the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX) with data assimilation capability provided by the Data Assimilation Research Testbed (DART) ensemble Kalman filter are used to demonstrate the impact of observations on unobserved model states. In particular, we present results showing that assimilating COSMIC-2 ionosphere observations and GOLD temperatures can improve the specification and short-term forecasting of thermosphere neutral densities. These results highlight the advantages of assimilating a diverse set of observations using a physics-based data assimilation system for near-Earth space weather forecasting. We additionally present results demonstrating that improvements in the data assimilation methodology, such as using empirical localization functions to localize the observations, leads to greater impact of the observations, illustrating the need for continued research and development in whole atmosphere data assimilation systems.