Radiation Belt Specification and Situational Awareness using Data Assimilation Based Modeling

Details Radiation Belt Specification and Situational Awareness using Data Assimilation Based Modeling Radiation Belt Specification and Situational Awareness using Data Assimilation Based Modeling

Dec 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006agufmsm42a..06r&link_type=abstract

American Geophysical Union, Fall Meeting 2006, abstract #SM42A-06

Physics

2722 Forecasting (7924, 7964), 2730 Magnetosphere: Inner, 2753 Numerical Modeling, 2774 Radiation Belts, 2788 Magnetic Storms And Substorms (7954)

Scientific paper

For a number of years now the operational limitations of the standard radiation belt models have been widely discussed. Doses from specific parts of the spectrum can be over- or under-estimated. The averaging procedures used do not give statistical distributions or worst case fluences. And, critically, the models are not time-dependent or real time. Here we present a new approach to radiation belt specification that provides fluxes, fluences, or dose rates for any arbitrary orbit and for any arbitrary mission duration up to and including real time. DREAM ( the Dynamic Radiation Environment Assimilation Model) uses data assimilation techniques to combine measurements from geosynchronous and GPS satellites along with a physics-based model to derive optimal state specification of the full radiation belts. The physical equations are solved by evolving phase space density at fixed adiabatic invariants. Once the underlying physical equations are solved and optimized with the current measured state (based on the observations), the phase space density representation is inverted back to physical space and physical fluxes. We show initial results for 6-months in 2002 and compare the orbital dose rates predicted by DREAM with those measured by HEO satellites. We also discuss how this model could be implemented with real time data to provide space situational awareness and short term radiation belt forecasts.

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