SA23C-4078:
THE ROLE OF THERMOSPHERIC COMPOSITION IN IONOSPHERIC FORECASTING

Tuesday, 16 December 2014
Frank Morgan II1, Alex Chartier2, Gary S Bust1 and Cathryn N Mitchell3, (1)JHU Applied Physics Lab, Laurel, MD, United States, (2)Applied Physics Laboratory Johns Hopkins, Laurel, MD, United States, (3)University of Bath, Bath, United Kingdom
Abstract:
Thermospheric composition changes play a key role in determining ionospheric plasma densities during magnetic storms. High-latitude heating causes a redistribution of atomic oxygen and molecular nitrogen, the two primary chemical species in the thermosphere. It is difficult to forecast the ionosphere without having good solar and geomagnetic forecasts because those two sources cause most of the ionospheric variability during storms. Current solar and geomagnetic forecasts do not provide the necessary detail or level of accuracy. We show that, in the absence of good solar/geomagnetic forecasts, ionospheric forecasts can be greatly improved with initial knowledge of thermospheric composition. Forecast improvements last for 18 hours in the case of the Halloween 2003 storm and for 15 hours in the November 2003 storm. The results show that O/N2 redistribution occurs during storm onset, so it is useful to know the true O/N2 distribution at the start of an ionospheric storm forecast. We also present assimilation results that show it is possible to determine the O/N2 ratio from measurements of ionospheric Total Electron Content (TEC). The approach uses an ensemble of model runs to estimate the spatiotemporal relationship between TEC and composition. Ensemble approaches are particularly suited to storms, since they allow for dynamic adjustment of the assumed relationships between variables. The O/N2 determination results are produced using the Data Assimilation Research Testbed (DART) and validated using Special Sensor Ultraviolet Spectrographic Imager (SSUSI) and Global Ultra-Violet Imager (GUVI) measurements. The Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) is used in both the simulation and the data assimilation sections of this research.