Utilizing Data Assimilation and Small Satellites to Better Understand the Thermospheric Density Structure

Details Utilizing Data Assimilation and Small Satellites to Better Understand the Thermospheric Density Structure Utilizing Data Assimilation and Small Satellites to Better Understand the Thermospheric Density Structure

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsa23a..07b&link_type=abstract

American Geophysical Union, Fall Meeting 2008, abstract #SA23A-07

Other

3315 Data Assimilation, 3367 Theoretical Modeling, 3369 Thermospheric Dynamics (0358), 3394 Instruments And Techniques

Scientific paper

Most satellite missions focus on measuring as much physics as possible in single or a very small number of locations. These missions typically have the problem of determining whether the effects that they are measuring are propagated or locally developed features. Further, the extent of the features often can not be determined. Utilizing models and modern data assimilation techniques, it is possible to take a series of simultaneous measurements of a single quantity, such as the mass density, at multiple points to reconstruct the time-dependent structure of the thermosphere. Within the model, other quantities can be determined self-consistently, such as the wind and temperature structure. We will present initial results from a Kalman Filter data assimilation technique within the Global Ionosphere Thermosphere Model (GITM), showing quantitative improvement in the global thermospheric mass density utilizing only a few point measurements. Further, we will discuss an idea for multi-satellite mission to investigate the dynamical global thermospheric density and wind response to strong high-latitude inputs utilizing these techniques.

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