Ensemble Ionospheric Total Electron Content Forecasting during Storms

Monday, 15 December 2014
Alex Chartier1, Cathryn N Mitchell2, Gang Lu3, Jeffrey L Anderson4, Nancy Collins4, Timothy J Hoar4, Gary S Bust5 and Tomoko Matsuo6, (1)Applied Physics Laboratory Johns Hopkins, Laurel, MD, United States, (2)University of Bath, Bath, United Kingdom, (3)National Center for Atmospheric Research, High Altitude Observatory, Boulder, CO, United States, (4)NCAR, Boulder, CO, United States, (5)JHU Applied Physics Lab, Laurel, MD, United States, (6)University of Colorado, Boulder, CO, United States
Earth's ionosphere presents a threat to human activities such as satellite positioning and timing, radio communications and surveillance. Nowcasts and forecasts of the ionosphere could help mitigate these damaging effects. Recent advances in the field of ionospheric imaging, as well as new storm-time ionospheric forecasting results are presented here. The approach combines globally distributed GPS Total Electron Content (TEC) measurements with an ensemble of coupled thermosphere-ionosphere models in order to produce short-term forecasts during a storm. One-hour forecast accuracy is much better than a climatological model run. Using this ensemble approach, it is possible to infer the neutral O/N2 ratio from TEC measurements so that subsequent TEC forecasts are improved. A review of ionospheric physics and data assimilation will also be given. The term data assimilation refers to a group of techniques designed to estimate atmospheric or oceanic states. In practice, data assimilation techniques seek to improve modeled estimates of the atmospheric state by incorporating observations. The relationship between data assimilation and forecasting is explored with reference to the physics of the thermosphere-ionosphere system. The work presented here uses the Data Assimilation Research Testbed (DART), which is an ensemble Kalman filter data assimilation framework. This is combined with a version of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) that has been modified to accept more detailed solar and geomagnetic driver specifications. Future directions of work include the inference of Solar and geomagnetic drivers from the data assimilation process as well as coupling with lower-atmospheric models.