NWC SEMINAR SERIES

SoM DISSERTATION DEFENSE
Northwest Australian Tropical Cyclones: Variability and Seasonal Prediction

Kevin Goebbert

27 July 2009, 9:30 AM
National Weather Center, Room 5820
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK
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Global teleconnections, involving geopotential height, air temperature, and sea surface temperature, are found for the interannual variability of tropical cyclone (TC) activity in Northwest- Australian (NWAUS) basin of the Southeast Indian Ocean (105–135°E). The NWAUS basin averages 5.5 TCs per year, 42 TC days, and 3 TC landfalls. Additionally, a wavelet analysis yields wavelet power maximum in the 4–6 year and the decadal time periods for both yearly TC frequency and TC days. To identify significant correlates, the global atmospheric and oceanic parameters mentioned above were correlated with the TC frequency and TC days from the Woodside Petroleum Ltd. TC data set. Large correlations were obtained between the NWAUS TC frequency and NCEP–NCAR reanalysis geopotential heights and air temperature. Additionally, large correlations were obtained between the NWAUS TC days and the geopotential height and the v- component of the wind. These variables can be utilized as seasonal predictors for the upcoming TC season in terms of frequency and days with a lead-time of at least three months for TC frequency and two months for TC days. This set of seasonal predictors includes, intra- basin, inter-basin, and cross-hemispheric regions, unlike previous Australian TC activity studies, which stress the primacy of ENSO. Here it is noted that the traditional Niño 3.4 and Niño 4 regions were not highly correlated with the NWAUS TC activity (| r |< 0.5). No local predictors based on SST, geopotential height, or air temperature resulted from the correlation analysis. The predictors are used in a multiple linear regression model for forecasting the coming seasons number of TCs and TC days. Finally, both prediction schemes are then compared to forecasts made using persistence, climatology, and random forecasts to determine if they perform better than the reference forecasts.


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