Predictability and Observability of the Upper Atmosphere (Invited Presentation)

The ionosphere-thermosphere-mesosphere (ITM) system is constantly affected by space weather and terrestrial weather forcing. An ensemble-based probabilistic modeling framework facilitates assessment of the impacts of uncertainties in forcing, initial conditions, model dynamics and physics on the predictability of the upper atmosphere as it responds to nearly constant changes in forcing. Some uncertainties are more likely to lead to greater prediction error, through amplification of an originally small uncertainty, through nonlinear coupling processes. Data assimilation can help reduce uncertainties in initial conditions as well as in drivers by systematically integrating satellite and ground-based observations into first-principles models, thereby extending the predictive capability of numerical models of the upper atmosphere. This paper will demonstrate the observation impact of some ITM satellite missions and ground-based facilities on upper-atmosphere predictability using ensemble data assimilation and forecasting simulation experiments.