Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape and Motion

TL;DR

This paper presents a machine learning approach to automatically detect comma-shaped clouds in satellite images, aiding severe weather forecasting by identifying storm-related cloud patterns with high accuracy.

Contribution

The study introduces a novel shape and motion-based detection method for comma-shaped clouds, improving automation and accuracy in severe weather prediction.

Findings

High detection accuracy on annotated datasets

Effective identification of storm-related cloud patterns

Potential to assist meteorologists in forecasting

Abstract

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.