[[File:Ernesto2006modelspread.png|thumb|right|250px|Significant track errors still occur on occasion, as seen in this [[Hurricane Ernesto (2006)|Ernesto (2006)]] early forecast. The [[National Hurricane Center|NHC]] official forecast is light blue, while the storm's actual track is the white line over [[Florida]].]]<!--image stacked below trend-map (same width) -->
A '''tropical cyclone forecast model''' is a computer program that uses [[meteorology|meteorological]] data to [[weather forecasting|forecast]] aspects of the future state of [[tropical cyclone]]s. There are three types of models: statistical, dynamical, or combined statistical-dynamic.<ref name="NHCmodel"/> DynamicalDdual modelsforecast utilizemodel. powerful[[supercomputer]]s with sophisticated [[mathematical model]]ingBoth softwareconsensus and meteorologicalsuperensemble dataforecasts tocan [[numerical weather prediction|calculate future weather conditions]]. [[Statistical model]]s forecastuse the evolutionguidance of a tropical cyclone in a simpler manner, by extrapolating from historical datasets,global and thusregional canmodels beruns runto quicklyimprove onthe platformsperformance suchmore asthan [[personal computer]]s. Statistical-dynamical models use aspectsany of boththeir typesrespective of forecastingcomponents. Techniques Fourused primary types of forecasts exist for tropical cyclones:at the [[tropicalJoint cycloneTyphoon trackWarning forecasting|trackCenter]],intensity,indicate [[stormthat surge]],superensemble andforecasts [[tropicalare cyclonea rainfallvery climatology|rainfall]].powerful tool Dynamicalfor modelstrack were not developed until the 1970s and the 1980s, with earlier efforts focused on the storm surge problemforecasting.
Track models did not show [[forecast skill]] when compared to statistical models until the 1980s. Statistical-dynamical models were used from the 1970s into the 1990s. Early models use data from previous model runs while late models produce output after the official hurricane forecast has been sent. The use of consensus, ensemble, and superensemble forecasts lowers errors more than any individual forecast model. Both consensus and superensemble forecasts can use the guidance of global and regional models runs to improve the performance more than any of their respective components. Techniques used at the [[Joint Typhoon Warning Center]] indicate that superensemble forecasts are a very powerful tool for track forecasting.
