Monday, 23 January 2017: 11:15 AM
4C-2 (Washington State Convention Center )
Specifying, predicting, and mitigating space weather disturbances have become increasingly important because of the effect of space weather on numerous civilian and military systems and operations, and during the last two decades significant progress has been made. However, there are still many challenges with regard to understanding, specifying, and predicting space weather. As far as modeling is concerned, numerous approaches are being pursued, including those involving empirical, physics-based, and data assimilation models, and all of the approaches have been useful to some degree. But, if there are sufficient measurements, the data assimilation modeling approach is expected to be the most reliable. Therefore, we created a Multimodel Ensemble Prediction System (MEPS) that involves seven physics-based data assimilation models covering 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.
In comparing MEPS ionospheric reconstructions, it was anticipated that different data assimilation (DA) models used to describe the same geophysical event could yield different results even if the same data sources are assimilated, because the different DA models are based on “different” background physics-based models, assimilation techniques, data error analyses, and spatial and temporal resolutions. Recent space weather MEPS simulations will be shown that display both similarities and differences in the ensemble member reconstructions, with the focus on identifying the challenges still ahead.
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