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

2. What is a cloud cluster? An organized grouping of clouds in the tropics with the potential for forming a tropical cyclone

3. 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

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

5. 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

6. 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

7. How does it work?

9. Example: Atlantic Tropical Wave IR image of wave on 8/8/2000 at 18Z

10. June 1 st – August 31 st Cluster Tracks Results for 2000 Atlantic Season

11. 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

12. 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

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

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

15. Applications: Preferred Development Examples using only data from 2000 Source: http://hurricanes.noaa.gov/prepare/season_zones.htm

16. Applications: Preferred Development Examples using only data from 2000 Source: http://hurricanes.noaa.gov/prepare/season_zones.htm

17. Applications: Preferred Development Examples using only data from 2000 Source: http://hurricanes.noaa.gov/prepare/season_zones.htm

18. Future Work Run additional years Adapt algorithm for other basins Improve runtime

19. 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.