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Abstract Details
Activity Number:
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456
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract - #304220 |
Title:
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Emulating and Calibrating the Lyon-Fedder-Mobarry Magnetosphere-Ionosphere Coupled Computer Model Using Predictive Processes
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Author(s):
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Matthew Heaton*+ and Stephan Sain and William Kleiber and Michael Wiltberger
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Companies:
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National Center for Atmospheric Research and Geophysical Statistics Project and National Center for Atmospheric Research and National Center for Atmospheric Research
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Address:
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PO Box 3000, Boulder, CO, 80307-3000, United States
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Keywords:
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Gaussian Process ;
Dimension Reduction ;
Ionosphere ;
Magentosphere
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Abstract:
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The Lyon-Fedder-Mobarry global magnetosphere-ionosphere coupled (LFM-MIX) model is a multiple fidelity computer model used to study sun-earth interactions by simulating aspects of geomagnetic storms. Higher fidelities of the LFM-MIX model require more computation but, ultimately, more accurately capture the governing physical processes under study. This work focuses on relating the output from the LFM-MIX model run at different fidelities to field observations of ionospheric energy. Of particular interest here are the input settings (and their uncertainties) for the LFM-MIX model such that output most closely matches the field observations. To estimate these input settings, a low rank spatio-temporal statistical LFM-MIX model emulator is constructed using predictive processes. The statistical emulator leverages the multiple fidelities of the LFM-MIX model to achieve calibration of the input variables to field observations. In this way, the less computationally demanding yet lower fidelity LFM-MIX modl is leveraged to provide estimates of the higher fidelity LFM- MIX model output. The higher fidelity LFM-MIX output is then, in turn, used for calibration of the input parameters.
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