Ensemble Modeling with Data Assimilation Models: A New Strategy for Space Weather Science, Specifications and Forecasts

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 4 February 2014: 9:15 AM
Room C110 (The Georgia World Congress Center )
Robert W. Schunk, Utah State Univ., Logan, UT; and L. Scherliess, V. Eccles, L. C. Gardner, J. J. Sojka, L. Zhu, X. Pi, A. J. Mannucci, B. D. Wilson, A. Komjathy, C. Wang, and G. Rosen

The Earth's Ionosphere-Thermosphere-Electrodynamics (I-T-E) system varies markedly on a range of spatial and temporal scales and these variations can have adverse effects on human operations and systems. Consequently, there is a need to both mitigate and forecast near-Earth space weather. First-principles-based global models are available to address some of the fundamental issues, but different coupled global models simulating the ‘same' geophysical case have been shown to produce different results. These difficulties also occurred in meteorology and oceanography before the advent of data assimilation models. Since their use, a major advance in our understanding of the basic physics underlying multi-scale atmosphere-ocean science has been achieved. Data assimilation models started to be used for I-T-E studies about 15 years ago as more global measurements became available. Since then, several modeling groups have become involved in data assimilation. Currently, our team has 7 first-principles-based data assimilation models for the ionosphere, ionosphere-plasmasphere, thermosphere, high-latitude ionosphere-electrodynamics, and mid-low latitude ionosphere-electrodynamics. These models assimilate a myriad of different ground- and space-based observations, and there is more than one data assimilation model for each near-Earth space domain. These data assimilation models are being used to create a Multimodel Ensemble Prediction System (MEPS), which will allow us to conduct ensemble modeling of the I-T-E system with different data assimilation models that are based on different physical assumptions, assimilation techniques, and initial conditions. The application of ensemble modeling with several different data assimilation models will lead to a paradigm shift in how basic physical processes are studied in near-Earth space, and it should also lead to a significant advance in space weather forecasting.