Applications of a Simple Monte Carlo Model to Tropical Cyclone Wind Speed Probability Forecasts

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Monday, 3 February 2014
Hall C3 (The Georgia World Congress Center )
Michael Splitt, Florida Institute of Technology, Melbourne, FL; and S. M. Lazarus and S. Collins

Probabilistic wind speed forecasts for tropical cyclones from Monte Carlo type simulations are assessed using a simple unbiased Gaussian system based on feature size and location error. Wind speed probability distributions are shown to be well approximated by bounded power-law distributions when the feature size is less than the location error and tends toward a U-shaped distribution as location error becomes small. Forecast skill (i.e., True and Heidke skill scores) is dependent on the probability forecast data distribution. Forecasts from the National Hurricane Center (NHC) Wind Speed Probability Forecasts Text Product are used to test applicability of the simple system to an operational scenario.