This project will apply and extend two new approaches to the problem of recasting magnetic storms. In particular it will develop tools for forecasting of extreme events in ground-based magnetic indices. It will also develop tools for accurately predicting local magnetic fluctuations on the ground that are generated by magnetic storms. Local forecasts during and prior to storms are particularly important since coupling of the magnetosphere-ionosphere system produces geospace and ground magnetic variations whose statistical properties vary with both local time and latitude. The project will develop risk estimation methods based on (1) the statistics of the waiting times between storms above (below) a certain threshold, and (2) a symbolic dynamics method which quantifies temporal variations in the magnetic fluctuations and incorporates the solar wind driving fluctuations. Recent years have witnessed the development of tools that allow for the analysis of data that feature sudden transitions and extreme events, like space storms. These advances have been made possible by a combination of approaches based on dynamical systems theory and statistical physics. A growing catalogue of research provides evidence that the magnetosphere behaves like a complex system with sudden transitions and extreme events that have a distinct statistical structure, including heavy-tailed Levy-type behavior. Because of this complex nature it is possible to leverage the data statistics to make probabilistic hazard assessments for storm risk. These are primarily based on the association of small disturbances with future large storms. The project will examine the way in which the nonlinear behavior in the driver and the magnetosphere interact to produce space storms, especially those for which there is no obvious gross trigger.
American society relies upon technologies such as power distribution systems and constellations of communications satellites for its daily functioning. These technologies are all susceptible to the effects of magnetic storms (also called space storms). Increased societal dependence on these technologies implies the need to understand and predict spatiotemporal fluctuations in the magnetosphere. In addition to the improved ability to predict important space weather phenomena the project will include meaningful research performed by undergraduate students.
