| Hailstone is a main disaster in the Meteorological area. The hailstorm arises suddenly, moves fast, and disappears very quickly. Now, weather operators monitor it and make hailstone forecast mainly by reflectivity images of Doppler radar. The development of the hailstone is of regularity that the hailstone's reflectivity intensity and its morphological features make them difference, which help weather operators make forecast. These features are gist of the research on the hailstone automatic detection based on hailstone's radar image, which is done in this dissertation.Recently, the research on hailstone's detection is mainly throw its light on mechanics analysis, forecasting factor and subjective model. There is no similar method to the one described in this dissertation reported yet.The automatic analysis of the images'morphological character is mainly used in medical field with objects to be the rigid body which have rigid fixing shape and little movement among parts of it, but the radar reflectivity image of hail clouds is different. In the hail cloud, movement among parts of which is violent and the interfaces of these parts are not very clear, which makes traditional morphological character extraction and the moving object tracing methods inefficient with the hail cloud.In this dissertation, some new ideas and methods are brought forward.1. With the pattern analysis of hail cloud in radar reflectivity, a model is constructed in which the image of cloud is separated into three layers: core, body and the transition band around of the body according to typical hail cloud reflectivity image. As to the hail clouds, they have some visual features that the reflectivity of the core is very high, the body is hook-like and the transition band is thin.2. To extraction these features of the model constructed, another model named"probe"is introduced. A method for feature extraction, including the gradient information of the transition band and the attribute about its morphological character especially those about the hook-like body, is proposed based on the"probe"model for hail auto-forecasting. Furthermore, a Support Vector Machine classifier is trained with features extracted from the probe model. The classifier is shown that its accuracy is satisfactory, which testify the efficiency of the method in this dissertation.3. For predicting the hailstone as early as most, the process of the hailstone clouds development is researched using the time series method and a corresponding algorithm model is constructed. In the algorithm model the velocity and direction of cloud's movement, the separation and the combination in hail clouds developing process are taken into account.4. A method is put forward to filter the super refraction echo in reflectivity image by using both of reflectivity image and radial speed image.All algorithms are coded with C++ language. It is stable and effective, which is testified by test. |
