Cloud pattern recognition

Abstract

Cloud cover photographs transmitted from meteorological satellites must be processed and interpreted before weather maps can be issued. Most of the routine processing can be handled by present day digital computer techniques; however, the recognition and interpretation of cloud patterns such as vortices indicating hurricanes, must still be performed by humans due to the lack of suitable recognition mechanisms. This paper investigates the feasibility of using a perceptron-type computer for the recognition of vortex patterns. A formula is derived which enables the prediction of machine performance as a function of problem complexity and perceptron size (number of logic units). It is shown that the problem complexity can be estimated through optical correlation measurements on cloud cover negatives. These measurements are described and a computer routine is developed which mechanizes the prediction equations and examines the experimental data gained from 10,000 measurements. The results of the computer program are presented and their meaning is discussed.