Publication Date


Document Type


First Advisor

Changnon, David

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Geography


Atmospheric sciences; Meteorology; Geography; Cyclones--North Atlantic Ocean; Cyclones--Tropics; Ocean-atmosphere interaction--Indian Ocean; Cyclone forecasting


Improving early tropical cyclone forecasts would assist reinsurance decision makers as they seek information that can minimize risks. Early lead forecasts are based on model variables before December 1 (Year 0) that predict Atlantic tropical cyclone activity (Year +1). The autumn Indian Ocean Dipole (IOD) has an 8 to 14 month antecedent correlation with the El Nino - Southern Oscillation (ENSO). ENSO is traditionally the best non-lead and overall predictor of Atlantic tropical cyclone activity. Analyses were performed over a 30-year period from 1984/85-2013/14, with some time variation depending on the test. Correlation, spatial, and wavelet analyses were utilized to find associations between the IOD, west and east components of the IOD, and four other variables related to the following season's ENSO state and tropical cyclone activity. The prior western pole of the October IOD (WIOD) was demonstrated to have statistically significant r-squared values (i.e. 99% confidence interval) to upcoming tropical storm activity (i.e. explained 25% of the variance), named storm counts (28%), and ENSO (21%). The WIOD has no connection with U.S. hurricane landfalls. Wavelet analysis between October IOD variables and following August-October ENSO data was observed to have the best time-frequency relationship. Dynamic reasoning for these relationships reside within the idealized biennial IOD-ENSO cycle, Walker circulation process, and the impact of ENSO on the state of the Atlantic Basin. The WIOD's integration into early-lead forecast models could be an advantage for those in the reinsurance industry and other decision makers impacted by Atlantic tropical cyclonesn.


Advisors: David Changnon.||Committee members: Walker Ashley; Isaac Hankes; Jie Song.


87 pages




Northern Illinois University

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