Tropical cyclone prediction model

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Average trends in forecast errors by the National Hurricane Center

A tropical cyclone prediction model is a computer program that uses meteorological data to predict the motion and intensity of tropical cyclones. Such models utilize powerful supercomputers with sophisticated mathematical modeling software and meteorological data to calculate paths and intensities. There are two general types, statistical and dynamical, and two primary types of forecasts, track and intensity.

1 Track models
2 Intensity models
3 See also
4 References
5 External links

[edit] Track models

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Some of the track models used by the National Hurricane Center follows: ^[1] ^[2] ^[3]

CLIPER (CLImatology and PERsistence) is a 3-day statistical model (CLIPER5 is a 5-day version of the same model). It uses the current path of a tropical cyclone and an average of historical paths of similar cyclones to come up with a track, and is considered a "no-skill" model. Curiously, until the late 1980s, this was actually the most accurate model. Forecast skill is determined by comparing forecasts against this model. ^[3]
NHC90 and NHC98
BAM (Beta and Advection) uses horizontal winds. There are three forms, BAMD (BAM Deep), BAMM (BAM Medium) and BAMS (BAM Shallow) that use different altitudes of wind.
VICBAR and LBAR
NHCP Aviation primarily predicts wind direction and speed and provides input for other models.
GFDL (Geophysical Fluid Dynamics Laboratory) and an interpolated version, GFDI
UKMET (United Kingdom Meteorological Office)
NOGAPS (United States Navy Global Atmospheric Prediction System)
GFS (National Weather Service Global Forecast System)

Some models do not produce output quickly enough to be used for the forecast cycle immediately after the model starts running. Most of the above track models (except CLIPER) require data from global weather models, which produce output about four hours after synoptic time. However, the NHC issues forecasts only three hours after that time, so some "early" models - NHC90, BAM, and LBAR - are run using a 12-hour-old forecast for the current time. "Late" models, such as the GFS and GFDL, finish after the advisory has already been issued. These models are interpolated to the current storm position for use in the following forecast cycle - for example, GFDI. ^[2] ^[3]

No model is ever perfectly accurate because it is impossible to learn exactly everything about the atmosphere in a timely enough manner, and atmospheric measurements that are taken are not always totally accurate. Thus, the models are used as a tool that an experienced forecaster will use to assemble an official track forecast.

Like all weather forecasts, track predictions are more accurate in the short term than in the long term. Average errors for the United States National Hurricane Center are around 100, 200, and 300 nautical miles in 1, 2, and 3 days, respectively - the source of the 1-2-3 rule - although the errors have been decreasing.

[edit] Intensity models

Some of the intensity models used by the National Hurricane Center follows:^[1] ^[3]

SHIFOR (Statistical Hurricane Intensity FORecast) and SHIFOR5 uses the average of past storms with similar behaviour as well as an extrapolation of recent behaviour by a storm to arrive at a prediction, similar to CLIPER and CLIPER5 above. Until recently, this was the most consistently accurate. ^[3]
SHIPS (Statistical Hurricane Intensity Prediction Scheme) uses current conditions and past storms to arrive at a prediction.
GFDL is the same model used in track prediction.
RI Scheme (Rapid Intensification) uses output from SHIPS to determine the probability of rapid intensification.

As with track models, intensity models are not perfect. They increase in accuracy as the future time of the prediction approaches the present. That is, a model is more accurate in the short term than in the long term. Intensity models are considered to be inferior to track models in that it is much more difficult to predict intensity changes of a tropical cyclone than it is to predict its course.