Data Assimilation Using the Global Ionosphere-Thermosphere Model

We consider a data assimilation technique for coupled ionospheric and thermospheric dynamics. The Global Ionosphere-Thermosphere Model (GITM) is used to simulate the ionospheric and thermospheric dynamics, and evaluate the performance of the data assimilation scheme that estimates the ion densities and flow speeds. This estimation technique is based on the state dependent Riccati equation (SDRE), which uses a frozen linear dynamics matrix for the time update of the error covariance and the evaluation of the Kalman filter gain. We demonstrate the performance of the data assimilation technique on a section of the ionosphere. 1 Data Assimilation for Space Weather Prediction The Sun drives our atmosphere in many ways. The most commonly understood way is through the heating of the lower atmosphere where sunlight heats the atmosphere causing weather. A similar process occurs in the thermosphere (above about 60 miles), where the thin atmosphere absorbs sunlight, and the atmosphere is heated dramatically. Another way in which the Sun affects the upper atmosphere is through the transfer of electromagnetic energy. The Sun’s atmosphere flows away from it at supersonic speeds and encounters the Earth’s magnetic field, transferring a significant amount of electromagnetic energy to our magnetosphere. This energy flows throughout the magnetosphere until it either leaves the system or is deposited into the upper atmosphere. This energy takes the form of strong electromagnetic currents and aurora, that is, the Northern and Southern lights. During quiet times, when the Sun’s atmosphere is calm, the heating of the atmosphere through sunlight is the dominant process. But the Sun sometimes erupts, sending large amounts of plasma and strong magnetic fields toward the Earth in events called coronal mass ejections (CMEs). When CMEs reach the Earth, they cause the Earth’s magnetosphere to shrink significantly, bringing the aurora to much lower latitudes (sometimes over the United States). During these This research was supported by the National Science Foundation under grants CNS0539053 and ATM-0325332. V.N. Alexandrov et al. (Eds.): ICCS 2006, Part III, LNCS 3993, pp. 489–496, 2006. c © Springer-Verlag Berlin Heidelberg 2006