An Automated Neural Network Cloud Classifier for Use over Land and Ocean Surfaces

Abstract

An automated neural network cloud classifier that functions over both land and ocean backgrounds is presented. Motivated by the development of a combined visible, infrared, and microwave rain-rate retrieval algorithm for use with data from the 1997 Tropical Rainfall Measuring Mission (TRMM), an automated cloud classification technique is sought to discern different types of clouds and, hence, different types of precipitating systems from Advanced Very High Resolution Radiometer (AVHRR) type imagery. When this technique is applied to TRMM visible?infrared imagery, it will allow the choice of a passive microwave rain-rate algorithm, which performs well for the observed precipitation type, theoretically increasing accuracy at the instantaneous level when compared with the use of any single microwave algorithm. A neural network classifier, selected because of the strengths of neural networks with respect to within-class variability and nonnormal cluster distributions, is developed, trained, and tested on AVHRR data received from three different polar-orbiting satellites and spanning the continental United States and adjacent waters, as well as portions of the Tropics from the Tropical Ocean and Global Atmosphere Coupled Ocean?Atmosphere Response Experiment (TOGA COARE). The results are analyzed and suggestions are made for future work on this technique. The network selected the correct class for 96% of the training samples and 82% of the test samples, indicating that this type of approach to automated cloud classification holds considerable promise and is worthy of additional research and refinement.