Long-Term Space Weather Forecasting: Parameters and Accuracy Needed for the Ionosphere/Thermosphere

Details Long-Term Space Weather Forecasting: Parameters and Accuracy Needed for the Ionosphere/Thermosphere Long-Term Space Weather Forecasting: Parameters and Accuracy Needed for the Ionosphere/Thermosphere

Dec 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufmsm54a..03s&link_type=abstract

American Geophysical Union, Fall Meeting 2010, abstract #SM54A-03

Physics

[2475] Ionosphere / Polar Cap Ionosphere, [2736] Magnetospheric Physics / Magnetosphere/Ionosphere Interactions, [2753] Magnetospheric Physics / Numerical Modeling, [2776] Magnetospheric Physics / Polar Cap Phenomena

Scientific paper

Many of the problems associated with space weather pertain to the ionosphere-thermosphere system. Space weather can affect over-the-horizon (OTH) radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, orbital calculations, power grids, pipelines, and the FAA’s Wide-Area Augmentation System (WAAS). As shown by the meteorologists and oceanographers, the best weather models are physics-based data assimilation models. As the physics-based models improve and more data are included in the assimilation scheme, the data assimilation models have been shown to provide more reliable specifications and forecasts. Therefore, in an effort to mitigate the adverse effects of space weather, we developed and are continuing to develop four data assimilation models, including two for the ionosphere (GAIM-GM and GAIM-FP), one for the high-latitude ionosphere dynamics and electrodynamics (IDED), and one for the thermosphere. The ionosphere models assimilate bottom-side Ne profiles from 100 ionosondes, slant TEC from 1000 ground GPS/TEC stations, in situ Ne from 4 DMSP satellites, occultation data from multiple satellites, and line-of-sight UV emissions measured by satellites. The IDED model assimilates cross-track ion velocities from the 4 DMSP satellites, line-of-sight ion velocities from the SuperDarn radar system, and magnetic perturbations from 100 ground magnetometers and from the Iridium constellation of satellites. These data assimilation models provide reliable specifications and short-term forecasts. However, for long-term forecasts it is useful to have a chain of coupled physics-based models from the Sun’s surface to the ionosphere-thermosphere system. Some of the issues that we will address with regard to long-term forecasting include the role a data assimilation model plays in providing a reliable specification, the resolution needed to describe important space weather features, the parameters needed for applications, and the accuracy needed from the Sun-Earth modeling chains.

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