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.