The use of the texture and motion of clouds from geostationary satellite images in rain rate estimation and prediction
This thesis addresses the problem of estimating rainfall rates from satellite imagery. The potential for using cloud motion and texture to estimate rain rates has been examined. The main types of textural information, i.e. statistical, structural, frequency and spatio-temporal, have been used to derive features from the satellite measurements and then used to determine a relationship to the radar-observed rain rates. These features were ranked by two scoring functions that were devised to assess their relationship to rain rates. The first scoring function selected a feature set for classifying samples into three rain rate classes. The selected features successfully classify rain rates of a mid-latitude cyclone seen on Meteosat7 with 84.8-99.3 % accuracy with a significant Hanssen-Kuipers discriminant score when a probabilistic neural network was used. A similar accuracy was found when a support vector machine (SVM) was used. Another scoring function was used for the selection of the features for estimating rain rates of each class. A Gaussian-kernel SVM that has been trained by these features produced visually agreeable rain estimates, which were much better than those produced by other methods that used only spectral information. Using the same types features at different time also achieved the similar accuracy. The method was robust and continuous rain estimates were obtained. Unlike other techniques in which additional information has always been required, the results showed that textural information alone can be used for rain estimation. This is preferable when only satellite measurements are available. Frequent updating of the observed rain rates can be done to improve the accuracy of the estimation. The potential for using cloud motion to predict rain rates was also examined. It was found that a combination of the maximum cross correlation and optical flow techniques provided the best estimate of the velocity of clouds. A cloud’s displacement derived by the maximum cross correlation technique was used for the approximation of the future location of its corresponding rain and the final velocity derived by the optical flow technique predicts how the rain rates would change. The rain rates predicted by this novel method provided good correlation to the observed rain rates at an hour later.