22 March 2001 Neural networks for sensor fusion of meteorological measurements
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Abstract
The collection and management of vast quantities of meteorological data, including satellite-based as well as ground- based measurements, is presenting great challenges in the optimal usage of this information. To address these issues, the Army Laboratory has developed neural networks for combining for combining multi-sensor meteorological data for Army battlefield weather forecasting models. As a demonstration of this data fusion methodology, multi-sensor data was taken from the Meteorological Measurement Set Profiler (MMSP-POC) system and from satellites with orbits coinciding with the geographical locations of interest. The MMS Profiler-POC comprises a suite of remote sensing instrumentation and surface measuring devices. Neural network techniques were used to retrieve temperature and wind information from a combination of polar orbiter and/ or geostationary satellite observations and ground-based measurements. Back-propagation neural networks were constructed which use satellite radiances, simulated microwave radiometer measurements, and other ground-based measurements as inputs and produced temperature and wind profiles as outputs. The network was trained with Rawinsonde measurements used as truth-values. The final outcome will be an integrated, merged temperature/wind profile from the surface up to the upper troposphere.
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Young P. Yee, Young P. Yee, Edward M. Measure, Edward M. Measure, James L. Cogan, James L. Cogan, Max Bleiweis, Max Bleiweis, } "Neural networks for sensor fusion of meteorological measurements", Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421095; https://doi.org/10.1117/12.421095
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