Cluster Analysis of Tropical Cyclone Tracks and ENSO Suzana J. Camargo, Andrew W. Robertson, International Research Institute for Climate Prediction, Columbia Earth Institute, Palisades, NY Scott J. Gaffney and Padhraic Smyth Department of Information and Computer Science, University of California, Irvine, CA
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Cluster Analysis of Tropical Cyclone Tracks and ENSO Suzana J. Camargo, Andrew W. Robertson, International Research Institute for Climate Prediction, Columbia.
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Cluster Analysis of Tropical Cyclone Tracks and ENSO
Suzana J. Camargo, Andrew W. Robertson,International Research Institute for Climate Prediction,
Columbia Earth Institute, Palisades, NY
Scott J. Gaffney and Padhraic SmythDepartment of Information and Computer Science,
University of California, Irvine, CA
Outline• Introduction• Clustering Technique• Previous works on cluster analysis and tropical cyclones• Western North Pacific Results
– Mean Regression Trajectories– Tracks– Properties of main clusters– ENSO Relationship: tracks, tracks density, NTC, ACE– Composites: SST, SLP, winds, wind shear
• North Atlantic Results– Mean Regression Trajectories and Tracks– ENSO relationship– Atlantic multi-decadal signal
• Eastern North Pacific Results– Mean Regression Trajectories and Tracks– ENSO relationship
• Summary
Introduction• Identify different track types, their seasonality and
relation with large-scale circulation and ENSO.• Importance: different track types have higher
incidence on some years and make landfall in different regions.
• New clustering technique used.• Best track datasets:
– Western North Pacific – JTWC 1950-2002.– North Atlantic – NHC 1851-2003.– Eastern North Pacific – NHC 1949-2003.
• Only tropical cyclones (TCs) with tropical storm or hurricane (typhoon) intensity (no tropical depressions).
Clustering Technique
• Developed by S.J. Gaffney and P. Smyth:- S.J. Gaffney (2004), Ph.D. thesis, University of California, Irvine.
• Mixture of polynomial regression models (curves) to fit the geographical “shape” of the trajectories.
• Extension of the standard multivariate finite mixture model to allow quadratic functions.
• Enable highly non-Gaussian density functions to be expressed as a mixture of a few PDFs.
• Fitting by maximizing the likelihood of the parameters.• Rigorous probabilistic context for clustering• Accommodate easily tropical cyclone tracks of different
lengths.
Previous works on Cluster Analysis and Tropical Cyclones
• Western North Pacific:– P.A. Harr and R.L. Elsberry, Mon. Wea. Rev. 123,
1225-1246 (1985).– J.B. Elsner and K.B. Liu, Climate Research 25, 43-54
Eastern North PacificTropical CyclonesCluster Analysis
Results
Mean Regression Trajectories and Tracks
ENSO Relationship
Tracks El Niño years Tracks La Niña years
Cluster 1
Cluster 2
Cluster 3
Summary
• New clustering technique applied to Northern Hemisphere TC tracks.
• Clusters with different properties: genesis and track regions, intensity, timing.
• In all basins clusters strongly related to ENSO are identified.
• Composites of large scale fields with different characteristics for each cluster identify the factors influencing the formation and movement of TCs in each cluster.