Statistical modeling of Atlantic tropical cyclone counts

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Sunday, 23 January 2011
Statistical modeling of Atlantic tropical cyclone counts
Michael E. Kozar, Pennsylvania State University, University Park, PA; and M. E. Mann, S. J. Camargo, and J. P. Kossin

This study examines the empirical relationships between climate state variables and Atlantic tropical cyclone (TC) counts. State variables considered as predictors include indices of the El Niño/Southern Oscillation (ENSO) and Northern Atlantic Oscillation (NAO), and both “local” and “relative” measures of main development region (MDR) sea surface temperature (SST). We consider in addition indices measuring the so-called “Atlantic Meridional Mode” (AMM) and the West African monsoon. Forward stepwise Poisson regression is used to assess the relationships between TC counts and various combinations of these predictors. As a further extension on past studies, both basin-wide named storm counts and cluster analysis time series representing distinct flavors of TCs, are modeled. Relative skill of competing models is measured by a variety of cross-validation metrics. These analyses suggest that total TC counts may be more skillfully modeled than particular storm “cluster” series. The most skillful models shared three commonly used predictors: the MDR SST index, an index of ENSO, and the NAO index.