Pattern recognition techniques for the identification of cloud and cloud systems

A wealth of often under‐used information is present in visible and infrared meteorological satellite imagery, in the form of cloud size, shape, texture and context. In an effort to provide an objective system that can identify meteorological objects over a range of scales, a study of currently available pattern recognition techniques has been undertaken. These techniques, which include supervised and unsupervised classification, image segmentation and scale context, are reviewed, together with a method for selecting the optimal image characteristics for the application of interest. This paper also considers a number of meteorological applications where these techniques can be used, such as cloud classification for operational or climatological purposes and the improvement of numerical weather prediction models. The result of the study has been the design of a synoptic‐scale object recognition system based on three layers of artificial neural networks, each operating at a different scale in a bottom‐up approach.