Advances in Modeling, Specifying, and Predicting Space Weather

Significant progress has been made during the last two decades in modeling, specifying, and predicting space weather. During this period, numerous space weather modeling approaches have been, and are still being, pursued, including those involving empirical, physics-based, and data assimilation models. All of the different approaches have been useful to some degree. However, if there are sufficient measurements, the data assimilation modeling approach is expected to be the most reliable. Therefore, like the meteorology and oceanography communities, we created a Multimodel Ensemble Prediction System (MEPS) for the Ionosphere-Thermosphere-Electrodynamics (I-T-E) system that involves physics-based data assimilation models. The MEPS ensemble is composed of seven data assimilation models for the ionosphere, ionosphere-plasmasphere, thermosphere, high-latitude ionosphere-electrodynamics, and mid-low latitude ionosphere-electrodynamics. For the mid-low latitude ionosphere, ensemble modeling can be conducted using five different data assimilation models. The MEPS models can assimilate a range of ground and space observations, including GPS-TEC; ionosonde/digisonde plasma density profiles; in situ electron densities, plasma drifts, and magnetic perturbations; occultation data; ultraviolet emissions from the limb and disk; and radar line-of sight plasma velocities. The goal of the MEPS program is to improve space weather specification and forecasting with ensemble modeling. Several storm and quiet periods that were reconstructed with more than one data assimilation model will be presented and discussed. The emphasis will be on the similarities and differences in the reconstructions, and the effect of different data types on the reconstructions.