DDDAS-SMRP:Targeted Data Assimilation for Disturbance-Driven Systems: Space Weather Forcasting in the Ionosphere and Thermosphere Using a Dynamically Steered Incoherent Scatter Ra

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Scientists and engineers, as well as the general public, are becoming increasingly aware of the threat that solar disturbances pose to humans and technological systems. To improve space weather prediction, this project combines a physics-based model of the ionosphere and thermosphere with targeted measurements, a process known as targeted data assimilation. The proposed research includes extensions of data assimilation algorithms to address nonlinear dynamics, model error, and computational complexity. Dynamically steered experiments involving the Millstone Observatory and EISCAT incoherent scatter radars will demonstrate the ability of targeted data assimilation to improve the quality and value of measurements for space weather prediction. The proposed research is relevant to NOAA. The project's goal is to improve space weather prediction in the ionosphere and thermosphere. The ability to predict the effects of solar storms is needed to protect humans and technological systems. Humans in manned spaced flight, as well as in commercial aircraft, are affected by solar storms, and total radiation dosages are a growing concern. Technological systems are also affected. For example, magnetic field fluctuations induce currents in electric power lines, which can damage components of the power grid causing blackouts and substantial economic losses. The accuracy of GPS,widely used for military and commercial operations, is commonly degraded by these events. The proposed research will greatly benefit the incoherent scatter radar (ISR) community. Since ISRs require high power, they are extremely expensive to run, and thus every radar experiment must be chosen for maximal efficiency. This efficiency is difficult to achieve in practice, however, since radars work in different modes and scan different parts of the ionosphere. This project will improve ISR efficiency by allowing operators to observe what may be happening if the radar mode or look direction were changed. This methodology will fundamentally change the way radars are run, since operators will be able to make informed decisions on what science they can accomplish by changing modes. When an experiment is not yielding useful results, operators will be able examine model results that simulate alternative radar modes, and thus dynamically switch experiments. This ability will dramatically improve the efficiency of the radars.