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 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 commashaped 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.
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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, vol. 57, no. 6, pp. 3788-3801, 2019.
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Last Modified:
May 28, 2019
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