Locating Visual Storm Signatures from Satellite Images
Yu Zhang (1),
Stephen Wistar (2),
Jose A. Piedra-Fernandez (3),
Jia Li (1),
Michael A. Steinberg (2),
James Z. Wang (1)
(1) The Pennsylvania State University, USA
(2) Accuweather Inc., USA
(3) University of Almeria, Spain
Abstract:
Weather forecasting is a problem where an enormous
amount of data must be processed. Severe storms cause a
significant amount of damages and loss every year in part due to
the insufficiency of the current techniques in producing reliable
forecasts. We propose an algorithm that analyzes satellite images
from the vast historical archives to predict severe storms. Conventional
weather forecasting involves solving numerical models
based on sensory data. It has been challenging for computers to
make forecasts based on the visual patterns from satellite images.
In our system we extract and summarize important visual storm
evidence from satellite image sequences in a way similar to how
meteorologists interpret these images. Particularly, the algorithm
extracts and fits local cloud motions from image sequences to
model the storm-related cloud patches. Image data of an entire
year are adopted to train the model. The historical storm reports
since the year 2000 are used as the ground-truth and statistical
priors in the modeling process. Experiments demonstrate the
usefulness and potential of the algorithm for producing improved
storm forecasts.
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Citation:
Yu Zhang, Stephen Wistar, Jose A. Piedra-Fernandez, Jia Li, Michael
A. Steinberg and James Z. Wang, ``Locating Visual Storm Signatures
from Satellite Images,'' Proceedings of the IEEE Big Data Conference,
pp. 711-720, Washington, D.C., October 2014.
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Last Modified:
October 13, 2014
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