  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 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 thunderstorm reports in continental
US from 2000 through 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 thunderstorm forecasts.
