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Developing an Objective
Identification Algorithm for Tropical
Cloud Clusters from Geostationary
Satellite Data
By Chip Helms
Faculty Advisor: Dr. Chris Hennon
What is a cloud cluster?
An organized grouping
of clouds in the
tropics with the
potential for forming
a tropical cyclone
Cloud Cluster Requirements
Clusters must be...
– Independent of other systems
– 2 degrees in diameter
– Located in a favorable area of the ocean
– Persistent for at least 24 hours
– Located over water
The Problem Objective testing against somewhat subjective
requirements
Data Source
Provided by the National Climatic Data Center
(NCDC)
HURSAT-Basin dataset, courtesy of Ken
Knapp
Created from geostationary satellite data
Data as used in the algorithm
Infrared (IR) satellite data
– Measurement of cloud temperature Known as the brightness temperature
– Colder temperatures correspond to darker colors Clouds appear black
• Program focuses on Atlantic Basin region
Interactive Data Language (IDL)
Optimized to work with arrays of data
Most languages require an explicit for loop to
copy the contents of an array to another array
IDL can do this implicitly
How does it work?
How does it work?
Example: Atlantic Tropical Wave
IR image of wave on 8/8/2000 at 18Z
June 1st – August 31st Cluster Tracks
Results for 2000 Atlantic Season
Results for 2000 Atlantic Season Jun-Aug 2000 Run Statistics
Cluster Candidates: 322
Clusters Found: 44
Best Tracks Found: ~3
Jun-Aug 2000 Statistics
Systems Tracked: 7
Hurricanes: 2
Tropical Storms: 2
Tropical Depressions: 3
Results for 2000 Atlantic Season Sept-Nov 2000 Run Statistics
Cluster Candidates:
Clusters Found:
Best Tracks Found:
Sept-Nov 2000 Statistics
Systems Tracked: 11
Hurricanes: 6
Tropical Storms: 4
Tropical Depressions: 1
Is it accurate?
A tentative yes, but more analysis is still needed.
Applications
Climatology
Areas of preferred development
Impacts of climate change on development
Impacts of cycles such as El Nino
Case Studies for Cyclogenesis
Modeling
Applications: Preferred
DevelopmentExamples using only data from 2000
Source: http://hurricanes.noaa.gov/prepare/season_zones.htm
Applications: Preferred
DevelopmentExamples using only data from 2000
Source: http://hurricanes.noaa.gov/prepare/season_zones.htm
Applications: Preferred
DevelopmentExamples using only data from 2000
Source: http://hurricanes.noaa.gov/prepare/season_zones.htm
Future Work
Run additional years
Adapt algorithm for other basins
Improve runtime
Bibliography
• Goldenberg, S.B., C.W. Landsea, A.M. Mestas-Nuñez, and W.M. Gray, 2001:
The recent increase in Atlantic hurricane activity: Causes and implications.
Science, 293, 474-479.
• Hennon, C.C., and J.S. Hobgood, 2003: Forecasting tropical cyclogenesis over
the Atlantic Basin using large-scale data. Monthly Weather Review, 131, 2927-
2940.
• Hennon, C.C., C. Marzban, and J.S. Hobgood, 2005: Improving tropical
cyclogenesis statistical model forecasts through the application of a neural
network classifier. Weather and Forecasting, 20, 1073-1083.
• Lee, C.S., 1989: Observational analysis of tropical cyclogenesis in the Western
North Pacific. Part I: Structural evolution of cloud clusters. Journal of the
Atmospheric Sciences, 46, 2580-2598.
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