An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery. Why?
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1 An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery By Chip Helms Faculty Advisor: Dr. Chris Hennon Why? Create a database for the tropical research community Based off widely accepted definitions of a cloud cluster Provides information regularly used in tropical cyclogenesis studies Maximize data coverage over both time and area 1
2 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 2
3 Data as used in the algorithm Infrared (IR) satellite data Measurement of cloud brightness temperature Geostationary data available for all tropical latitudes 30 years of data available 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 3
4 How does it work? 1 How does it work?
5 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 5
6 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 6
7 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 7
8 Applications:Preferred Development Examples using data from June Source: Applications:Preferred Development Examples using data from July Source: 8
9 Applications:Preferred Development Examples using data from August Source: Applications:Preferred Development Examples using data from September Source: 9
10 Applications:Preferred Development Examples using data from October Source: Applications:Preferred Development Examples using data from November Source: 10
11 Is it accurate? A tentative yes, but more analysis is still needed. Text file output Output Statistics Information about modifications to points (Smoothed, Interpolated) Track number Latitude Longitude Statistics on the cluster 11
12 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 12
13 4/20/2009 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 13
An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery
University of North Carolina Asheville Journal of Undergraduate Research Asheville, North Carolina, 2010 An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary
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P1.4 TOWARD A GLOBAL CLIMATOLOGY OF TROPICAL CLOUD CLUSTERS Christopher C. Hennon * University of North Carolina Asheville, Asheville NC Charles N. Helms University of North Carolina Asheville, Asheville,
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