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Proceedings Paper

Retrieving atmospheric temperature profiles from simulated DMSP sounder data with a neural network
Author(s): Charles T. Butler; R. V.Z. Meredith
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Paper Abstract

We have used neural networks to separately retrieve atmospheric temperature and moisture vertical profiles from simulated microwave sounder data. Backpropagation networks, implemented on a PC or Sun SPARCstation 2, were trained on data that simulated the multichannel output of either the SSM/T-1 temperature- or SSM/T-2 moisture-sensing radiometers on board current Air Force DMSP weather satellites. Ground-truth information was obtained from the Phillips data, a collection of approximately 1600 validated radio- and rocketsonde measurements. Radiometer outputs were simulated from the ground-truth data using the Air Force program RADTRAN. For each temperature or moisture profile in the Phillips data, RADTRAN generated a corresponding simulated sounder output. These simulated radiometer outputs were combined with the ground truth to produce training and testing sets for the neural networks. For both temperature and moisture, the neural method produces atmospheric profiles from unfamiliar data that are comparable to or better than those obtained with current operational methods. Networks, however, were able to retrieve profiles from a much broader range of geographic areas and seasons than standard methods, and to do this using less externally supplied geographic or season information.

Paper Details

Date Published: 16 September 1992
PDF: 9 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140062
Show Author Affiliations
Charles T. Butler, Physical Sciences Inc. (United States)
R. V.Z. Meredith, Research Support Instruments, Inc. (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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