Presentation is loading. Please wait.

Presentation is loading. Please wait.

Cluster Analysis of Tropical Cyclone Tracks and ENSO Suzana J. Camargo, Andrew W. Robertson, International Research Institute for Climate Prediction, Columbia.

Similar presentations


Presentation on theme: "Cluster Analysis of Tropical Cyclone Tracks and ENSO Suzana J. Camargo, Andrew W. Robertson, International Research Institute for Climate Prediction, Columbia."— Presentation transcript:

1 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

2 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

3 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).

4 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.

5 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 (2003); North Atlantic: –J.B. Elsner, Bull. Amer. Meteor. Soc. 84, 353-356 (2003); J.B. Elsner et al., J. Climate 13, 2293-2305 (2000). Eastern North Pacific (TC precursors): –J.B. Mozer and J.A. Zehnder, J. Geophys. Res. – Atmos. 99, 8085-8093 (1994).

6 Western North Pacific Tropical Cyclones Cluster Analysis Results

7 Mean Regression Trajectories Appropriate number of clusters appears to be seven. Quantitative (out of sample likelihood) and subjective analysis. Two main trajectory-types: “straight-movers” and “recurvers”. Additional clusters: detailed differences in shape among these types.

8 MEAN REGRESSION TRAJECTORIES TRACKS TRACKS TROPICAL CYCLONES Western North Pacific 1983-2002

9 Number of TCs per Cluster

10 Cluster A Landfall 63% Regression Trajectory 67% reach typhoon intensity FIRST POSITION DENSITY NTC ANNUAL CYCLE TRACK DENSITY

11 Cluster B Landfall 61% Regression Trajectory 50% only reach TS intensity. FIRST POSITION DENSITY NTC ANNUAL CYCLE TRACK DENSITY

12 Cluster C 70% reach typhoon intensity Landfall 7% Regression Trajectory FIRST POSITION DENSITY NTC ANNUAL CYCLE TRACK DENSITY

13 ENSO Relationship NTC- Number of Tropical Cyclones ACE – Accumulated Cyclone Energy Total ACE has a well known relationship with ENSO (Camargo & Sobel, 2004). Total NTC per year is not significantly correlated with ENSO (e.g. Wang & Chan, 2002).

14 Tracks El Niño years Tracks La Niña years Cluster A Cluster E Cluster G

15 Track Density per year: Difference El Niño and La Niña years Full basinCluster A Cluster G Cluster E

16 Mean NTC and ACE per cluster and ENSO A A E E G G

17 SST Anomalies Composites TCs first positions Regression trajectory SST and TC data for SST composites:11/81 – 12/02

18 Sea Level Pressure Anomalies Composites NCEP Reanalysis and TC data for composites: 1950-2002

19 Anomalous Low Level Wind Composites

20 Wind Shear Composites Magnitude of the total wind shear between 200hPa and 850hPa

21 North Atlantic Tropical Cyclones Cluster Analysis Results

22 Tracks and Regression Trajectories Tracks Atlantic named Tropical Cyclones 1970-2003. TRACKS Mean Regression Trajectory

23 Number of TCs per cluster

24 ENSO Relationship NTC correlations ACE correlations

25 Tracks El Niño yearsTracks La Niña years Cluster 1 Cluster 2 Cluster 3 Named Tropical Cyclones in warm/cold ENSO years 1950-2003

26 Named Tropical Cyclones: 1950-2003

27 SST Anomalies Composites TCs First Positions Main Development Region SST and TC data for SST composites: 11/81 – 12/2003

28 Wind shear composites Magnitude of the total wind shear between 200hPa and 850hPa. NCEP Reanalysis and TC data for composites: 1950-2003.

29 Atlantic Multi-Decadal Signal S.B. Goldenberg, C.W. Landsea, A.M. Mesta-Nuñez and W.M. Gray, Science 293, 474-478 (2001).

30 Number of Major Hurricanes per cluster

31 SST anomalies composite SST composites: 11/1981-12/2003

32 Eastern North Pacific Tropical Cyclones Cluster Analysis Results

33 Mean Regression Trajectories and Tracks

34 ENSO Relationship

35 Tracks El Niño yearsTracks La Niña years Cluster 1 Cluster 2 Cluster 3

36 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.


Download ppt "Cluster Analysis of Tropical Cyclone Tracks and ENSO Suzana J. Camargo, Andrew W. Robertson, International Research Institute for Climate Prediction, Columbia."

Similar presentations


Ads by Google