Atmosphere Model Inversion with Artificial Neural Networks
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Back to Chuck
Anderson's Home Page
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Philip Gabriel of the CSU
Department of Atmospheric Sciences has written a fortran model of
the atmosphere. Given parameters representing the properties of
clouds in various layers of the atmosphere, Philip's model produces
an expected power spectrum of the light received by a satellite. To
determine the atmosphere properties from an observed power spectrum,
this model must be effectively inverted. We are experimenting with an
application of artificial neural networks to learn this inverse mapping.
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Andrew's final report is available here
as a PDF file.
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Here are some slides from a talk
that Andrew presented at the Colorado State University
Undergraduate Research Symposium, April 24, 2001.
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Atmosphere Model Inversion with Neural Networks, Research in CS at CSU, Charles
W. Anderson /
anderson@cs.colostate.edu