Applied Mathematics Seminar | Chunming Wang, New Mathematical Approaches for Space Weather Forecast

MC 5501

Speaker

Chunming Wang

Department of Mathematics | University of Southern California

Title

New Mathematical Approaches for Space Weather Forecast

Abstract

Space weather including solar coronal ejection, storms in the interplanetary magnetic fields, ionosphere irregularities can have significant impacts on terrestrial social and economic activities. In particular, the Earth’s ionosphere plays an important role in space and ground based wireless communications. Since solar and interplanetary magnetic field are major driving forces for the ionization, transport and diffusion of particles in the ionosphere, monitoring of the entire space weather is crucial for forecasting anomalies in the Earth’s ionosphere.

General principles governing the physics of the ionosphere is well understood since the early 1960s. With the increasing availability of the measurements of ionosphere electron density derived from signals of the Global Positioning Satellites (GPS), it is now possible to perform real-­-time global monitoring of the ionosphere using mathematical techniques for system identification and for solving nonlinear inverse problems. This talk gives an overview of approaches we have developed to perform data assimilation that combines ionosphere measurements and first principle physics models. The resulting Global Assimilative Ionosphere Model (GAIM) has shown substantially improved capability in determining the state of ionosphere.

On the other hand, the ionosphere is a system strongly driven by the solar and interplanetary magnetic field variations. Although the connections between these systems and the ionosphere are known, the linkages are not well quantified. This presents a significant challenge for forecasting of ionosphere anomalies. In a recent effort, we have used data mining techniques to produce empirical forecast of the ionosphere anomalies. We believe a combination of statistical and first principle physics models offers the best hope for producing reliable forecast of ionosphere anomalies.