18th Conference on Probability and Statistics in the Atmospheric Sciences


Forecasting U.S. hurricanes 6 months in advance

James B. Elsner, Florida State University, Tallahassee, FL; and T. H. Jagger and R. J. Murnane

Hurricanes are a serious social and economic threat to the United States. Recent advances allow us to forecast a measure of this threat with some skill at the beginning of August, the start of the most active part of the hurricane season for the Atlantic basin. Skillful forecasts of hurricane landfalls at longer lead times (forecast horizons) for the complete hurricane season would greatly benefit risk managers and others interested in acting on these forecasts. Here we demonstrate a Bayesian regression model that provides a 6-month forecast horizon for annual hurricane counts along the U.S.~coastline during the June through November hurricane season. Forecast skill exceeds that of climatology. The long-lead skill is linked to the persistence of Atlantic sea-surface temperatures and to teleconnections between North Atlantic sea-level pressures and precipitation variability over North America and Europe. The model incorporates the full set of Atlantic hurricane data extending back to 1851. .

Session 3, Bayesian Probability Forecasting
Monday, 30 January 2006, 4:00 PM-5:00 PM, A304

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