The Earth’s Ionosphere-Thermosphere-Electrodynamics (I-T-E) system varies markedly on a range of spatial and temporal scales and these variations have adverse effects on human operations and systems, including high-frequency communications, over-the-horizon radars, and survey and navigation systems that use Global Positioning System (GPS) satellites. Consequently, there is a need to elucidate the underlying physical processes that lead to space weather disturbances and to both mitigate and forecast near-Earth space weather. The meteorologists and oceanographers have shown that data assimilation models are superior to global physics-based models for specifications and forecasts, but only during the last 15 years have they been used for near-Earth investigations as more global (space and ground-based) measurements became available. Although data assimilation models produce better specifications and forecasts than global physicsbased models, there is still a spread in results for a given simulation scenario when different data assimilation models are used. This spread occurs because the different data assimilation models use different data types, data amounts, assimilation techniques, and background physics-based models. This data assimilation issue is being addressed with the launching of the “NASA/NSF Space Weather Modeling Collaboration” program. Currently, our team has seven physics-based data assimilation models for the ionosphere, plasmasphere, thermosphere, and 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 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 is expected to lead to a significant advance in space weather specifications and forecasts.
