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

Xinye Zheng (1), Jianbo Ye (1), Yukun Chen (1), Stephen Wistar (2),
Jia Li (1), Jose A. Piedra-Fernandez (3), Michael A. Steinberg (2), James Z. Wang (1)
(1) The Pennsylvania State University, USA
(2) Accuweather Inc., USA
(3) University of Almeria, Spain
Abstract:

Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Yet, because satellite imaginary data are in increasingly higher resolution, both spatially and temporally, meteorologists cannot fully leverage the data in their forecasts. Automatic satellite imagery analysis methods that can find storm-related cloud patterns as soon as they are detectable 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 dataset and crossmatch 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.


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Citation: Xinye Zheng, Jianbo Ye, Yukun Chen, Stephen Wistar, Jia Li, Jose A. Piedra-Fernandez, Michael A. Steinberg and James Z. Wang, ``Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape and Motion,'' IEEE Transactions on Geoscience and Remote Sensing, 14 pages, 2018, under review for minor revisions.

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Last Modified: May 4, 2018
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