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 motion vectors derived from sequences of remotely sensed data are widely used by numerical weather prediction models and other meteorological and climatic applications. One approach to computing cloud motion vectors is the correlation-relaxation labeling technique, in which a set of candidate vectors for each template is refined using relaxation labeling to provide a local smoothness constraint. In this letter, an extension of the correlation-relaxation labeling framework to tracking clouds in multichannel imagery is presented. As this multichannel approach takes advantage of the diversity between channels, it has the potential for producing motion vectors with a superior quality and coverage than can be achieved by any individual channel. Results for visible and infrared images from Meteostat Second Generation confirm the benefits of the multichannel approach Abstract A simple, fully automated, and efficient method to determine the structural properties and evolution (tracking) of cloud shields of convective systems (CS) is described. The method, which is based on the maximum spatial correlation tracking technique (MASCOTTE), is a new alternative to the existent techniques available for studies that monitor the evolution of CS using satellite images. MASCOTTE provides as CS structural properties the following parameters: mean and variance of brightness temperature, horizontal area, perimeter, minimum brightness temperature, fractional convective area, center of gravity, and fragmentation. The fragmentation parameter has the potential to monitor the evolution of the CS. A new way of estimating the orientation and eccentricity of CS is proposed and is based on the empirical orthogonal function analysis of CS pixel coordinates. The method includes an accurate detection of splitting and merging of convective systems, which is a critical step in the automated satel... Abstract Cloud motion can be determined from sequential pictures obtained from the geosynchronous Applications Technology Satellites, ATS-I and ATS-III. Experiments have been conducted using two automated techniques for computing the cloud motions over the time interval between two digitized pictures. One technique is a direct application of cross-correlation using the fast Fourier Transform (fFT). In the other, a relatively simple matching technique is used in the computer to recognize binary images of cloud fields on each member of a picture pair. Results obtained from the two techniques are similar. The computed directions are in good agreement with those determined by manual methods with indications that computed speeds are more accurately resolved than those determined manually. The measurement of winds to an accuracy of at least 2 knots (1 m s) is a major requirement of the global atmospheric research program (GARP) [1]. Such accurate wind measurements will be needed over large regions presently lacking ground or rawinsonde observations. A man-computer system called WINDCO has been developed to measure cloud motion from ATS pictures. This system is capable of providing cloud motion measurements that are repeatable to within 6 knots over two independent time intervals in the majority of cases.