2.2 Recent Advancements in the Tropical Cyclone Wind Speed Probability Program

Tuesday, 8 January 2013: 3:45 PM
Room 4ABC (Austin Convention Center)
Andrea B. Schumacher, CIRA/Colorado State Univ., Fort Collins, CO; and M. DeMaria, R. Berg, E. Gibney, and R. D. Knabb

Under the Hurricane Forecast Improvement Project (HFIP), a new hybrid statistical-dynamical wind speed probability algorithm has been developed. The current wind speed probability product, which replaced the Strike Probability program at the National Hurricane Center in 2006, is a purely statistical algorithm that uses a Monte Carlo method to estimate the probability of 34, 50 and 64-kt winds out to 5 days. This new hybrid algorithm uses a similar methodology but also incorporates global numerical model ensemble tropical cyclone track data. Some advantages of this new hybrid algorithm are the ability to represent track bifurcations and the reflection of global model spread in the wind speed probabilities. The forecast performance of the hybrid wind speed probabilities will be compared to those of the current operational product for the Atlantic 2012 hurricane season.

Collaborations between product developers, operational forecasters, and social scientists have led to the development of several socioeconomic applications of wind speed probabilities. Many of these applications utilize the ensemble-based methodology of the wind speed probability algorithm. As result, improvements made to the wind speed probability ensembles may have a direct impact on the accuracy of the impact-related applications. Some of the more recent socioeconomic applications, such as hurricane warning guidance and landfall timing estimates, will be presented and the potential impacts that the new hybrid algorithm will have on these types of applications will be discussed.

Disclaimer: The views, opinions, and findings contained in this article are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration (NOAA) or U.S. Government position, policy, or decision.

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