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Improving Tropical Cyclone Intensity Guidance in the Eastern North Pacific

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  • 1 National Center for Atmospheric Research, Boulder, Colorado
  • | 2 Atmospheric Sciences Program, The Ohio State University, Columbus, Ohio
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Abstract

The primary objective of this research is the development of a statistical model that will provide tropical cyclone (TC) intensity guidance for the eastern North Pacific (ENP) superior to that provided by climatology and persistence models. Toward this goal, the authors investigate the use of shear and thermal variables in statistical forecasts of TC intensity change. European Centre for Medium-Range Weather Forecasts (ECMWF) global analyses are used to develop an Eastern Pacific Intensity Change (EPIC) model, forecasting tropical cyclone intensity changes at 12-h intervals, out to 72 h. The dataset consists of ENP tropical cyclones during the years 1989–96 along with ECMWF analyses for those years.

The synoptic predictors examined in this study consist of shear and thermal variables, including zonal and meridional components of shear between 200 and 850 mb, the difference in temperature between 200 and 850 mb, and equivalent potential temperature at 700 mb. The time tendency of these variables is also explored, and multiple linear regression analysis is used to detect which variables best explain the variance in tropical cyclone intensity change in the ENP.

The EPIC model forecasts are compared to those from a climatology–persistence model developed from the 1989–96 dataset, and to the current operational statistical models, SHIFOR (a model based on climatology and persistence) and SHIPS (Statistical Hurricane Intensity Prediction Scheme—a model based on climatology, persistence, and synoptic variables), in the ENP basin for the 1997 and 1998 hurricane seasons. Results for these seasons reveal that EPIC may provide better intensity guidance than statistical models based on climatology and persistence, and confirm that the inclusion of synoptic predictors in a statistical intensity prediction scheme improves intensity change forecasts and that, overall, EPIC may better forecast intensity change in the ENP.

Corresponding author address: Kevin R. Petty, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307.

Email: kpetty@ucar.edu

Abstract

The primary objective of this research is the development of a statistical model that will provide tropical cyclone (TC) intensity guidance for the eastern North Pacific (ENP) superior to that provided by climatology and persistence models. Toward this goal, the authors investigate the use of shear and thermal variables in statistical forecasts of TC intensity change. European Centre for Medium-Range Weather Forecasts (ECMWF) global analyses are used to develop an Eastern Pacific Intensity Change (EPIC) model, forecasting tropical cyclone intensity changes at 12-h intervals, out to 72 h. The dataset consists of ENP tropical cyclones during the years 1989–96 along with ECMWF analyses for those years.

The synoptic predictors examined in this study consist of shear and thermal variables, including zonal and meridional components of shear between 200 and 850 mb, the difference in temperature between 200 and 850 mb, and equivalent potential temperature at 700 mb. The time tendency of these variables is also explored, and multiple linear regression analysis is used to detect which variables best explain the variance in tropical cyclone intensity change in the ENP.

The EPIC model forecasts are compared to those from a climatology–persistence model developed from the 1989–96 dataset, and to the current operational statistical models, SHIFOR (a model based on climatology and persistence) and SHIPS (Statistical Hurricane Intensity Prediction Scheme—a model based on climatology, persistence, and synoptic variables), in the ENP basin for the 1997 and 1998 hurricane seasons. Results for these seasons reveal that EPIC may provide better intensity guidance than statistical models based on climatology and persistence, and confirm that the inclusion of synoptic predictors in a statistical intensity prediction scheme improves intensity change forecasts and that, overall, EPIC may better forecast intensity change in the ENP.

Corresponding author address: Kevin R. Petty, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307.

Email: kpetty@ucar.edu

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