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Understanding the Sun and Solar System Plasmas: Future Directions in Solar and Space Physics THEORY, COMPUTER MODELING, DATA EXPLORATION, AND DATA MINING Solar and space physics has evolved from a strongly exploratory and discovery-driven discipline to a more mature, explanatory science. Moreover, the societal and economic importance of a capability for forecasting space weather has become increasingly apparent. Its character as a mature science and its role in space weather prediction present solar and space physicists with significant new challenges and opportunities in the areas of theory, computer modeling, and data exploration and mining. CAPTURING COMPLEXITY Solar system plasmas are complex systems, and their behavior is characterized by multiple interactions or “couplings” between different plasma regions and energy regimes, between different populations of particles, between different processes, and across different spatial and temporal scales. For example, the heliosphere contains cosmic rays, solar wind plasma, neutral atoms, and pickup ions, each of which interacts with the other but is described by its own set of equations. Similarly, the ionosphere-thermosphere and magnetosphere are different but interacting regions governed by distinct and different physical processes. The challenge to theoreticians and modelers is to develop the theoretical and computational tools needed to understand and describe solar system plasmas as dynamic, coupled systems. To address this challenge, the Survey Committee proposes two new research initiatives. The Coupling Complexity Research Initiative will address multiprocess coupling, multiscale coupling, and multiregional feedback in solar system plasmas. The program advocates both the development of coupled global models of the different regions of the heliosphere and the synergistic investigation of important unresolved theoretical problems. The Virtual Sun initiative will incorporate a systems-oriented approach to theory, modeling, and simulations that will provide continuous models from the solar interior to the outer heliosphere. The relevant models will be developed in a modular fashion so that future improvements to models of
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Understanding the Sun and Solar System Plasmas: Future Directions in Solar and Space Physics Total electron content (TEC) data derived from GPS navigation signals can be used to map ionospheric weather and will be incorporated in data assimilation models for space weather “nowcasting” and forecasting. specific domains can be easily integrated. Initial efforts would focus on development of the modules pertaining to the solar dynamo (the source of solar magnetism) and magnetic reconnection (the prime mechanism for releasing stored magnetic energy). DATA ASSIMILATION The coming decade will see the availability of enormous quantities of space physics data that will have to be integrated or assimilated into physical models of Earth’s space environment. Meteorologists were the first to use data
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Understanding the Sun and Solar System Plasmas: Future Directions in Solar and Space Physics assimilation to improve terrestrial weather prediction. Data obtained at various times and places are used in combination with physics-based (numerical) models to provide, in real time, an essentially continuous “movie” of the behavior of the lower atmosphere. Data assimilation models can be used for “now-casting”—that is, for describing the current weather conditions at any given time—and can also be run into the future, providing the weather forecasts that are seen on television. During the last 40 years, meteorologists have dramatically improved their ability to predict the weather, both because of the availability of faster computers, and hence better numerical models, and because of a large infusion of satellite and ground-based data. In comparison, the space physics community has been slow to implement data assimilation techniques, primarily because there have been insufficient measurements for a meaningful assimilation. However, this situation is rapidly changing, and within the next decade several million measurements per day will be available for assimilation into specification and forecast models relevant to space physics. The data will be acquired from operational satellites of NOAA and the Department of Defense, the constellation of GPS satellites, and worldwide networks of ground-based instruments, such as the Distributed Array of Small Instruments (DASI; previously called the Small Instrument Distributed Ground-Based Network) recommended by the Survey Committee as a new initiative. Data assimilation models will play a particularly important role in space weather prediction, but will also contribute usefully to purely scientific investigations of Earth’s space environment. DISTRIBUTED ARRAY OF SMALL INSTRUMENTS The Distributed Array of Small Instruments (DASI) is an initiative recommended by the Survey Committee for implementation by the National Science Foundation. Geographically distributed instrument arrays will provide the global coverage and high-resolution observations needed to characterize the dynamic behavior of the ionosphere and thermosphere. Instrumentation will include Global Positioning System (GPS) receivers, all-sky imagers, passive radars, Fabry-Perot interferometers, very low frequency (VLF) receivers, magnetometers, and ionosondes. Data will be available via the Internet in real time and will provide necessary inputs for data assimilation models. Instrument clusters located at universities and high schools will provide students with hands-on training in instrumentation and data analysis.
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