Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary...
-
Upload
katherine-alexander -
Category
Documents
-
view
213 -
download
0
Transcript of Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary...
![Page 1: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/1.jpg)
Developing an Objective
Identification Algorithm for Tropical
Cloud Clusters from Geostationary
Satellite Data
By Chip Helms
Faculty Advisor: Dr. Chris Hennon
![Page 2: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/2.jpg)
What is a cloud cluster?
An organized grouping
of clouds in the
tropics with the
potential for forming
a tropical cyclone
![Page 3: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/3.jpg)
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
![Page 4: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/4.jpg)
Data Source
Provided by the National Climatic Data Center
(NCDC)
HURSAT-Basin dataset, courtesy of Ken
Knapp
Created from geostationary satellite data
![Page 5: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/5.jpg)
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
![Page 6: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/6.jpg)
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
![Page 7: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/7.jpg)
How does it work?
![Page 8: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/8.jpg)
How does it work?
![Page 9: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/9.jpg)
Example: Atlantic Tropical Wave
IR image of wave on 8/8/2000 at 18Z
![Page 10: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/10.jpg)
June 1st – August 31st Cluster Tracks
Results for 2000 Atlantic Season
![Page 11: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/11.jpg)
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
![Page 12: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/12.jpg)
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
![Page 13: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/13.jpg)
Is it accurate?
A tentative yes, but more analysis is still needed.
![Page 14: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/14.jpg)
Applications
Climatology
Areas of preferred development
Impacts of climate change on development
Impacts of cycles such as El Nino
Case Studies for Cyclogenesis
Modeling
![Page 15: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/15.jpg)
Applications: Preferred
DevelopmentExamples using only data from 2000
Source: http://hurricanes.noaa.gov/prepare/season_zones.htm
![Page 16: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/16.jpg)
Applications: Preferred
DevelopmentExamples using only data from 2000
Source: http://hurricanes.noaa.gov/prepare/season_zones.htm
![Page 17: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/17.jpg)
Applications: Preferred
DevelopmentExamples using only data from 2000
Source: http://hurricanes.noaa.gov/prepare/season_zones.htm
![Page 18: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/18.jpg)
Future Work
Run additional years
Adapt algorithm for other basins
Improve runtime
![Page 19: Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.](https://reader030.fdocuments.in/reader030/viewer/2022032705/56649dd15503460f94ac7722/html5/thumbnails/19.jpg)
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.