Mathematics – Logic
Scientific paper
May 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agusmsh22a..07s&link_type=abstract
American Geophysical Union, Spring Meeting 2009, abstract #SH22A-07
Mathematics
Logic
2427 Ionosphere/Atmosphere Interactions (0335), 2435 Ionospheric Disturbances, 2447 Modeling And Forecasting, 2753 Numerical Modeling, 7924 Forecasting (2722)
Scientific paper
The Earth's upper atmosphere and ionosphere display highly variable and turbulent densities, temperatures, and winds, and these features are manifestations of space weather. During geomagnetic storms, wind gusts in the upper atmosphere can become supersonic, localized density troughs and peaks form, localized temperature hot spots occur, cyclonic-like and tornado-like winds periodically form, and the radiation levels can dramatically increase. Unfortunately, these space weather features can have detrimental effects on human systems and operations. As society becomes more dependent on sophisticated technological systems, specification and forecasting of space weather disturbances become crucial to our economy, safety, and security. Consequently, a significant effort is being devoted to developing both data assimilation and coupled physics-based models of the space environment. The data assimilation models are useful for specification and the coupled physics-based models are needed for forecasting. Significant progress has been made in the development of physics-based, Kalman filter, data assimilation models for the global ionosphere. The state-of-the-art models can assimilate several data types, including in situ electron densities from satellites, bottomside electron densities from 100 ionosondes, line-of-sight Total Electron Content (TEC) measurements between 1000 ground stations and the GPS satellites, TEC via occultations between low-altitude and high-altitude satellites, and line-of-sight ultraviolet emission data. Likewise, significant progress has been made in developing coupled physics-based models of the space environment from the Sun to the Earth. However, the construction of these models is not always straightforward for several reasons: (1) Although there are a lot of data available via the world wide web, the data quality is generally not adequate for data assimilation models and data errors are not routinely provided; (2) There is a limited number of real-time data sources for specification and forecast models; (3) The application of a rigorous Kalman filter is not feasible and approximations are necessary; (4) The coupled physics-based models have uncertain parameters that need to be determined and/or have missing physics; and (5) Validation of data assimilation and coupled physics-based models requires massive independent data sets so that statistics can be done to determine the accuracy of the models. The status and challenges related to the development of data assimilation and coupled physics-based models will be discussed.
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