ALBERT

Description: This paper describes a method for determining global atmospheric-temperature anomalies by means of satellite microwave radiometry. It is shown that microwave measurements of molecular oxygen thermal emission by the Microwave Sounding Units (MSUs) flying aboard the NOAA-6 and NOAA-7 can be used to monitor tropospheric temperature anomalies on global basis to a high level of precision. Comparisons between monthly MSU-derived hemispheric temperature anomalies with those computed from surface thermometer data show a very good agreement over the United States, although not for the hemispheres, especially the Southern Hemisphere. In this latter case, the poor agreement is ascribed to weaker thermal coupling between the ocean and the deep troposphere than that over the U.S. Annual anomalies for the hemispheres exhibit better correlations than do monthly anomalies.

Description: A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).