An Operational Statistical Typhoon Intensity Prediction Scheme for the Western North Pacific

Abstract The current version of the Statistical Typhoon Intensity Prediction Scheme (STIPS) used operationally at the Joint Typhoon Warning Center (JTWC) to provide 12-hourly tropical cyclone intensity guidance through day 5 is documented. STIPS is a multiple linear regression model. It was developed using a “perfect prog” assumption and has a statistical–dynamical framework, which utilizes environmental information obtained from Navy Operational Global Analysis and Prediction System (NOGAPS) analyses and the JTWC historical best track for development. NOGAPS forecast fields are used in real time. A separate version of the model (decay-STIPS) is produced that accounts for the effects of landfall by using an empirical inland decay model. Despite their simplicity, STIPS and decay-STIPS produce skillful intensity forecasts through 4 days, based on a 48-storm verification (July 2003–October 2004). Details of this model’s development and operational performance are presented.

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Understanding and Forecasting Tropical Cyclone Intensity Change with the Typhoon Intensity Prediction Scheme (TIPS)

Weather and Forecasting ◽

10.1175/1520-0434(1997)012<0826:uaftci>2.0.co;2 ◽

1997 ◽

Vol 12 (4) ◽

pp. 826-846 ◽

Cited By ~ 41

Author(s):

Patrick J. Fitzpatrick

Keyword(s):

Tropical Cyclone ◽

Intensity Change ◽

Tropical Cyclone Intensity ◽

Cyclone Intensity ◽

Intensity Prediction ◽

Prediction Scheme ◽

Typhoon Intensity

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A western North Pacific tropical cyclone intensity prediction scheme

Acta Meteorologica Sinica ◽

10.1007/s13351-011-0506-9 ◽

2011 ◽

Vol 25 (5) ◽

pp. 611-624 ◽

Cited By ~ 6

Author(s):

Peiyan Chen ◽

Hui Yu ◽

Johnny C. L. Chan

Keyword(s):

Tropical Cyclone ◽

North Pacific ◽

Western North Pacific ◽

Tropical Cyclone Intensity ◽

Cyclone Intensity ◽

Intensity Prediction ◽

Prediction Scheme

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Passive-Microwave-Enhanced Statistical Hurricane Intensity Prediction Scheme

Weather and Forecasting ◽

10.1175/waf941.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 613-635 ◽

Cited By ~ 19

Author(s):

Thomas A. Jones ◽

Daniel Cecil ◽

Mark DeMaria

Keyword(s):

Tropical Cyclone ◽

Linear Regression ◽

Tropical Cyclones ◽

North Pacific ◽

Passive Microwave ◽

Tropical Cyclone Intensity ◽

Hurricane Intensity ◽

Cyclone Intensity ◽

Intensity Prediction ◽

Prediction Scheme

Abstract The formulation and testing of an enhanced Statistical Hurricane Intensity Prediction Scheme (SHIPS) using new predictors derived from passive microwave imagery is presented. Passive microwave imagery is acquired for tropical cyclones in the Atlantic and eastern North Pacific basins between 1995 and 2003. Predictors relating to the inner-core (within 100 km of center) precipitation and convective characteristics of tropical cyclones are derived. These predictors are combined with the climatological and environmental predictors used by SHIPS in a simple linear regression model with change in tropical cyclone intensity as the predictand. Separate linear regression models are produced for forecast intervals of 12, 24, 36, 48, 60, and 72 h from the time of a microwave sensor overpass. Analysis of the resulting models indicates that microwave predictors, which provide an intensification signal to the model when above-average precipitation and convective signatures are present, have comparable importance to vertical wind shear and SST-related predictors. The addition of the microwave predictors produces a 2%–8% improvement in performance for the Atlantic and eastern North Pacific tropical cyclone intensity forecasts out to 72 h when compared with an environmental-only model trained from the same sample. Improvement is also observed when compared against the current version of SHIPS. The improvement in both basins is greatest for substantially intensifying or weakening tropical cyclones. Improvement statistics are based on calculating the forecast error for each tropical cyclone while it is held out of the training sample to approximate the use of independent data.

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