TY - JOUR
T1 - Severe Thunderstorm Detection by Visual Learning Using Satellite Images
AU - Zhang, Yu
AU - Wistar, Stephen
AU - Li, Jia
AU - Steinberg, Michael A.
AU - Wang, James Z.
N1 - Funding Information:
This work was supported by the National Science Foundation under Grant 1027854 and Grant 0821527 (Shared computational infrastructure).
Publisher Copyright:
© 1980-2012 IEEE.
PY - 2017/2
Y1 - 2017/2
N2 - Computers are widely utilized in today's weather forecasting as a powerful tool to leverage an enormous amount of data. Yet, despite the availability of such data, current techniques often fall short of producing reliable detailed storm forecasts. Each year severe thunderstorms cause significant damage and loss of life, some of which could be avoided if better forecasts were available. We propose a computer algorithm that analyzes satellite images from historical archives to locate visual signatures of severe thunderstorms for short-term predictions. While computers are involved in weather forecasts to solve numerical models based on sensory data, they are less competent in forecasting based on visual patterns from both current and past satellite images. In our system, we extract and summarize important visual storm evidence from satellite image sequences in the way that meteorologists interpret the images. In particular, the algorithm extracts and fits local cloud motion from image sequences to model the storm-related cloud patches. Image data from the year 2008 have been adopted to train the model, and historical severe thunderstorm reports in continental U.S. from 2000 to 2013 have been used as the ground truth and priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing more accurate severe thunderstorm forecasts.
AB - Computers are widely utilized in today's weather forecasting as a powerful tool to leverage an enormous amount of data. Yet, despite the availability of such data, current techniques often fall short of producing reliable detailed storm forecasts. Each year severe thunderstorms cause significant damage and loss of life, some of which could be avoided if better forecasts were available. We propose a computer algorithm that analyzes satellite images from historical archives to locate visual signatures of severe thunderstorms for short-term predictions. While computers are involved in weather forecasts to solve numerical models based on sensory data, they are less competent in forecasting based on visual patterns from both current and past satellite images. In our system, we extract and summarize important visual storm evidence from satellite image sequences in the way that meteorologists interpret the images. In particular, the algorithm extracts and fits local cloud motion from image sequences to model the storm-related cloud patches. Image data from the year 2008 have been adopted to train the model, and historical severe thunderstorm reports in continental U.S. from 2000 to 2013 have been used as the ground truth and priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing more accurate severe thunderstorm forecasts.
UR - http://www.scopus.com/inward/record.url?scp=84995484497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84995484497&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2016.2618929
DO - 10.1109/TGRS.2016.2618929
M3 - Article
AN - SCOPUS:84995484497
SN - 0196-2892
VL - 55
SP - 1039
EP - 1052
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 2
M1 - 7733124
ER -