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Chris Ruf, AOSS
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Chris Ruf, AOSS
"Inversion of Satellite Remote Sensing Measurements by Bayesian Estimation" Abstract: Satellite remote sensing instruments for Earth science estimate geophysical properties of the atmosphere and surface indirectly from radiance and backscatter measurements. The estimation problem can often be expressed as an inversion of a forward model which, when given the exact geophysical state, can predict the measurements. In all practical situations, the inverse problem is severely underconstrained since the number of unknown geophysical parameters far exceeds the number of measurements. Various methods are commonly employed to constrain the inversion. One class of methods weighs the past statistical behavior of the geophysical parameters against the behavior implied by the present remote sensing measurements and makes a decision based on both characteristics. One means of implementing this decision algorithmically is Bayesian estimation. The idea of Bayesian estimation will be reviewed and then applied to the problem of estimating rainfall with a satellite microwave radiometer. March 12, 2008 3:30 pm - 4:30 pm 2355 GG Brown |
