Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape and Motion
This work addresses the need for automatic detection of storm-related cloud patterns in high-resolution satellite data to aid meteorologists in severe weather forecasting, representing a domain-specific incremental improvement.
The paper tackled the problem of detecting comma-shaped clouds in satellite imagery for severe weather forecasting by proposing a machine learning and pattern recognition approach, achieving validated utility and accuracy that suggests high potential for assisting meteorologists.
Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Satellite imaginary data are in increasingly higher resolution, both spatially and temporally, making it impossible for humans to fully leverage the data in their forecast. Automatic satellite imagery analysis methods that can find storm-related cloud patterns as soon as they are detectable are 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 the cyclone formulation. In order to detect regions with the targeted movement patterns, our method is trained on manually annotated cloud examples represented by both shape and motion-sensitive features. Sliding windows in different scales are used to ensure that dense clouds will be captured, 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 dataset 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.