Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.

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Presentation transcript:

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 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

Data Source Provided by the National Climatic Data Center (NCDC)‏ HURSAT-Basin dataset, courtesy of Ken Knapp  Part of the HURSAT data project Created from geostationary satellite data

How does it work?

Cloud Clusters from 1999 Season

Results for 1999 Atlantic Season 1999 Run Statistics  Cluster Candidates: 1080  Clusters Found: Statistics  Systems Tracked: 16 Hurricanes: 8 Tropical Storms: 4 Tropical Depressions: 4

Cloud Clusters from 2000 Season

Results for 2000 Atlantic Season 2000 Run Statistics  Cluster Candidates: 1077  Clusters Found: Statistics  Systems Tracked: 18 Hurricanes: 8 Tropical Storms: 6 Tropical Depressions: 4

Cloud Clusters from 2001 Season

Results for 2001 Atlantic Season 2001 Run Statistics  Cluster Candidates: 1013  Clusters Found: Statistics  Systems Tracked: 17 Hurricanes: 9 Tropical Storms: 6 Tropical Depressions: 2

Applications:Preferred Development Examples using data from Source: June

Applications:Preferred Development Examples using data from Source: July

Applications:Preferred Development Examples using data from Source: August

Applications:Preferred Development Examples using data from Source: September

Applications:Preferred Development Examples using data from Source: October

Applications:Preferred Development Examples using data from Source: November

Is it accurate? A tentative yes, but more analysis is still needed.

Output Statistics Text file output  Information about modifications to points (Smoothed, Interpolated)  Track number  Latitude  Longitude  Statistics on the cluster

Applications Climatology  Areas of preferred development  Impacts of climate change on development  Impacts of cycles such as El Nino Case Studies for Cyclogenesis Modeling

Future Work Run additional years Adapt algorithm for other basins Filter out developed systems (Hurricanes, Tropical Storms) Verification

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, Hennon, C.C., and J.S. Hobgood, 2003: Forecasting tropical cyclogenesis over the Atlantic Basin using large-scale data. Monthly Weather Review, 131, 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, 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,

Questions? Hurricane Epsilon (2005) taken from the International Space Station