Numerical Space Weather Forecasting Facilitated by Ensemble Modeling and Data Assimilation

As numerical space weather models become more mature, it is imperative to embrace a paradigm shift from a deterministic to a probabilistic modeling framework that takes various sources of uncertainty into consideration. The predictability of space weather is bounded by the nonlinearity intrinsic to the coupled Sun-Earth dynamical system, as well as by the shortcomings of current modeling and observing capabilities. An ensemble of model simulations can be designed to emulate how model uncertainty grows and evolves over time. Ensemble forecasting reflecting realistic model uncertainties associated with approximated model physics and inadequate initial and driver conditions can facilitate more robust and informative numerical space weather forecasting. Data assimilation, in the context of ensemble forecasting, provides a means to reduce uncertainty in initial and driver conditions by integrating observations into numerical models with rigorous consideration of model and observation uncertainty, and extends the predictive capability of numerical forecast models. Ensemble modeling and data assimilation are envisioned to be integral components of space weather research and operation in the future. This talk will demonstrate this vision using the numerical prediction and data assimilation tools developed at NOAA and NCAR focusing on predictability of the upper atmosphere.