Research Of Cloud Classification And Short-time Cloud Movement Forecast Based On Multi-spectrum Stationery Meteorological Satellite Pictures

To compete with hardness of military meteorological service , it is forced to produce lots of advanced theories and practical technologies which can serve the purpose of weather forecast. A satellite cloud image contains a lot of weather information. It is hard and important task to distinguish the characteristics and short-time movement forecast of satellite cloud image timely,correctly,objectively and automatically. To meet the challenge of particularity and complexity of cloud classification and short-time movement forecast of satellite cloud image, it is studied to improve used theory and find a new arithmetic which can be easily put into use based on idea of crossing of subjects and methods in this paper.The paper contains two parts. In part one, it is mainly aimed at distinguishing and classification of satellite cloud image. There are five subjects:(1) After standard conversion of gray and recognizing ratio and as well as rectifying to the height angle of the Sun from satellite cloud image of GMS-5 through four bands of IRK IR2,VS and WV , it is built of a sample base which collects numbers of samples of land and water and seven types of typical clouds as well, which is used for study of cloud classification;(2) Distinguishing and classification of cloud characteristics inner season is basic part of ordinary weather services. On the basis of above works, the samples of cloud classification are reflected to two dimension gray space of IR and VS by the means of Characteristic Space Projection (i.e. CSP). It is found the criteria of classification of cloud pixel with determination of falling area of the samples. The test results show that the method of CSP is simple and easy, especially suited for uneven division of unregulated clustering data, the result is better than the regular criterion method .(3) Due to complexity and variety of clouds presentation, it is necessary to distinguish and classify clouds' characteristics within a year or inter-seasons. On the basis of each advantage of SOMF (i.e. Self-Organized Mapping Reflection) and PNN (i.e .Probability Neural Network), an improving method of two times classification of clouds is introduced in combination of SOMF and PNN.. The method makes process of cloud classification into two stages: first, it is clustered to the base of samples characteristics without supervision by the mean of SOMF, then, models of clouds classification with their own PNN are build after kinds of classified samples have been trained under supervision and objects' rectification. The method can both consider differentiae of clouds in different seasons and simplify process of cloud classification .Therefore, it can effectively reduce the sampling errors, its classification results conform to reality .(4) It is hard to distinguish attribute of clouds in transitional area. On the basis of IR-VS two dimension gray space projection of the samples of cloud classification , the characteristic area of the samples is rectified and optimized by the means of Fuzzy C Mean clustering method(i.e. FCM). An improving method that characteristic mean values of the samples replace with random initial centre values in FCM can both avoid defection of that FCM is too sensitive to the initial values and correct the clustering results' distortion to characteristic structure of the samples. Therefore, it can effectively reduce the sampling error and keep basic characteristic structure of cloud samples .(5) In actual atmosphere, there are some of "fuzzy" clouds which are hard to tell what kinds of clouds are. FCM is an advantage non-supervised clustering arithmetic. It can fairly good realize non-linear distinguishing of both high dimension complicated data and non-determinative cloud patterns with calculating and comparing what the subordination is each pixel of cloud image apart from every clustering center. Nevertheless, the normal FCM has three native defections : less capability of global optimization; clustering results dependent on the initial clustering center which produces in random way; the clustering numbers must be given manually. To counter the defections of FCM, a new comprehensive method that Genetic Arithmetic is used for global optimization and FCM is used for local optimization and FSC (Fuzzy Subtract Clustering) is used for objectively estimating clustering numbers as well is introduced on distinguishing satellite cloud patterns. The test results show that the comprehensive method of cloud classification is obvious advantage over any one of three methods and effectively remedy the defection of FCM and GA on cloud classification, and can be used to practice.In part two, it is researched on short time forecasting of cloud motion of satellite cloud image. The major content includes:(1) An arithmetic of cloud motion vectors—local wavelet quadrature is selected to objectively and effectively reflect characteristics of cloud motion . Firstly, the basic thought of computing the cloud motion vectors was given and the four important match methods of Fourier Phase, Cross Correlation, Local Entropy were introduced, analyzed . Then, the four schemes are designed according to the unsteady property of could motion during process of heavily changes and rotations . By evaluation of advantage and disadvantage of each scheme, and comparison and simulation tests, it is found that the local wavelet quadrature is more reasonable to reflect variation and characteristics of clouds . Moreover, an improved method of quality control and cloud motion vectors using for corrections scheme is presented.(2) A model of the short-time forecast is established based on linear extrapolation of cloud motion vectors. Then, quality control is implemented to computed cloud motion vectors with multi-smoothing successive corrections scheme (i.e. Cressman). After smoothing process, 1-3 hours forecast of cloud motion is tested with Backward Trajectory method. The test results show that the method can effectively decrease deviation of distinguishing cloud motion by range estimation and present unsteady characteristics of could rotating to some extent, and as well as provide forecasting of cloud motion in a more objective and quantity way.(3) A nonlinear idea of cloud prediction combined EOF decomposition with dynamical system restructure was brought up. Series of the samples of the satellite cloud image were made space-time decomposition by EOF. On basis of relative stability of space in EOF decomposition, Genetic algorithms were introduced to go on dynamical system restructure and parametric inversion of model by EOF time factors, and a nonlinear dynamical model forecasting cloud evolution was established. By means of prediction of EOF time factors mingled with restructure of space-structure modalities, a middle and long-time prediction on unsteady characteristics of cloud motion has been realized. An innovative idea and useful way was created for cloud motion prediction.