Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Yet, because satellite image data are in increasingly higher resolution, both spatially and temporally, meteorologists cannot fully leverage the data in their forecasts. Automatic satellite image analysis methods that can find storm-related cloud patterns are thus in demand. We propose a machine learning and pattern recognition-based approach to detect “comma-shaped” clouds in satellite images, which are specific cloud distribution patterns strongly associated with cyclone formulation. In order to detect regions with the targeted movement patterns, we use manually annotated cloud examples represented by both shape and motion-sensitive features to train the computer to analyze satellite images. Sliding windows in different scales ensure the capture of dense clouds, and we implement effective selection rules to shrink the region of interest among these sliding windows. Finally, we evaluate the method on a hold-out annotated comma-shaped cloud data set and cross match the results with recorded storm events in the severe weather database. The validated utility and accuracy of our method suggest a high potential for assisting meteorologists in weather forecasting.
Cloud segmentation is an important method for detecting storm cells. Lakshmanan @cite_42 proposed a hierarchical cloud-texture segmentation method for satellite image. Later, they improved the method by applying watershed transform to the segmentation and using pixel intensity thresholding to identify storms @cite_31 . However, brightness temperature in a single satellite image is easily affected by lighting conditions, geographical location, and satellite imager quality, which is not fully considered in the thresholding-based methods. Therefore, we consider these spatial and temporal factors and segment the high cloud part based on the Gaussian Mixture Model (GMM).
@cite_31 @cite_42
1985621603 2074056052
Abstract Existing techniques for identifying, associating, and tracking storms rely on heuristics and are not transferrable between different types of geospatial images. Yet, with the multitude of remote sensing instruments and the number of channels and data types increasing, it is necessary to develop a principled and generally applicable technique. In this paper, an efficient, sequential, morphological technique called the watershed transform is adapted and extended so that it can be used for identifying storms. The parameters available in the technique and the effects of these parameters are also explained. The method is demonstrated on different types of geospatial radar and satellite images. Pointers are provided on the effective choice of parameters to handle the resolutions, data quality constraints, and dynamic ranges found in observational datasets. Abstract Extensive observations were made of a winter storm that crossed eastern Canada on 10–12 March 1986 during the Canadian Atlantic Storms Program (CASP) field project. A kinematic description of the storm is established by using isentropic analyses and this description is compared with storm observations in other regions as well as with other CASP storms. The occurrence and role of snow and freezing precipitation in this storm are highlighted. It is suggested that the evolution and organization of the storm was greatly influenced by mesoscale circulations driven by melting snow.