Probability Distributions and Threshold Selection for Monte Carlo–Type Tropical Cyclone Wind Speed Forecasts

Abstract

Abstract Probabilistic wind speed forecasts for tropical cyclones from Monte Carlo–type simulations are assessed within a theoretical framework for a simple unbiased Gaussian system that is based on feature size and location error that mimic tropical cyclone wind fields. Aspects of the wind speed probability data distribution, including maximum expected probability and forecast skill, are assessed. Wind speed probability distributions are shown to be well approximated by a bounded power-law distribution when the feature size is smaller than the location error and tends toward a U-shaped distribution as the location error becomes small. Forecast skill (i.e., true and Heidke skill scores) is shown to be highly dependent on the probability forecast data distribution. Forecasts from the National Hurricane Center (NHC) Wind Speed Probability Forecast Product are used to assess the applicability of the simple system in the interpretation and evaluation of a more advanced system.