Some adaptive filtering techniques applied to the passive remote sensing problemA review is made of recursive statistical regression techniques incorporating past or past and future observations through smoothing and Kalman filtering, respectively; with results for the cases of the Tiros-N/MSU and Nimbus-6/Scams remote sensing satellite experiments. In response to the lack of a satisfactory model for the medium sounded, which is presently a major limitation on retrieval technique performance, a novel, global approach is proposed which casts the retrieval problem into the framework of adaptive filtering. A numerical implementation of such an adaptive system is presented, with a multilayer, semi-spectral general circulation model for the atmosphere being used to fine-tune the sensor as well as the dynamical equations of a Kalman filter. It is shown that the assimilation of radiometric data becomes a straightforward subproblem.
Document ID
19810061459
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Toldalagi, P. M. (MIT Cambridge, MA, United States)
Date Acquired
August 11, 2013
Publication Date
January 1, 1980
Subject Category
Meteorology And Climatology
Meeting Information
Meeting: Interactive Workshop on Interpretation of Remotely Sensed Data