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Information Technology and Systems Center The University of Alabama in Huntsville
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Rain Detection Using SSM/I

A study was conducted to determine whether generic pattern recognition techniques could be applied to SSM/I data to detect rain over land. Three pattern recognition techniques ( Minimum Distance Classifier, Backpropagation Network and Discrete Bayes Classifier) were compared against a science algorithm (WetNet PIP SSM/I Algorithm) for this study.

The US Composite Rainfall product from the Global Hydrology Resource Center (GHRC) was used to obtain an independent estimate of rainfall over the area of interest (ground truth).

This was used as the ground truth. SSM/I and US rain data over the southeastern United States for the period January and July of 1995 were compared in this study. The recognition accuracy for the different algorithms for this study are given in the figure below.


Collaboration with Domain Expert:
Dr. Steve Goodman (NASA/MSFC GHCC)