A New Method for Estimating Tropical Cyclone Wind Speed Probabilities

Author:

DeMaria Mark1,Knaff John A.1,Knabb Richard2,Lauer Chris3,Sampson Charles R.4,DeMaria Robert T.5

Affiliation:

1. NOAA/NESDIS, Fort Collins, Colorado

2. NOAA/NWS/Central Pacific Hurricane Center, Honolulu, Hawaii

3. NOAA/NCEP/National Hurricane Center, Miami, Florida

4. Naval Research Laboratory, Monterey, California

5. CIRA/Colorado State University, Fort Collins, Colorado

Abstract

Abstract The National Hurricane Center (NHC) Hurricane Probability Program (HPP) was implemented in 1983 to estimate the probability that the center of a tropical cyclone would pass within 60 n mi of a set of specified points out to 72 h. Other than periodic updates of the probability distributions, the HPP remained unchanged through 2005. Beginning in 2006, the HPP products were replaced by those from a new program that estimates probabilities of winds of at least 34, 50, and 64 kt, and incorporates uncertainties in the track, intensity, and wind structure forecasts. This paper describes the new probability model and a verification of the operational forecasts from the 2006–07 seasons. The new probabilities extend to 120 h for all tropical cyclones in the Atlantic and eastern, central, and western North Pacific to 100°E. Because of the interdependence of the track, intensity, and structure forecasts, a Monte Carlo method is used to generate 1000 realizations by randomly sampling from the operational forecast center track and intensity forecast error distributions from the past 5 yr. The extents of the 34-, 50-, and 64-kt winds for the realizations are obtained from a simple wind radii model and its underlying error distributions. Verification results show that the new probability model is relatively unbiased and skillful as measured by the Brier skill score, where the skill baseline is the deterministic forecast from the operational centers converted to a binary probabilistic forecast. The model probabilities are also well calibrated and have high confidence based on reliability diagrams.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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