An Integrated Approach for Assessing Tropical Cyclone Track and Intensity Forecasts

Wenqing Zhang Physical Oceanography Laboratory, Ocean University of China, Qingdao, Shandong, China, and Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

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Lian Xie Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

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Bin Liu NOAA/NCEP/EMC/I. M. Systems Group, College Park, Maryland

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Changlong Guan Physical Oceanography Laboratory, Ocean University of China, Qingdao, Shandong, China

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Abstract

Track, intensity, and, in some cases, size are usually used as separate evaluation parameters to assess numerical model performance on tropical cyclone (TC) forecasts. Such an individual-parameter evaluation approach often encounters contradictory skill assessments for different parameters, for instance, small track error with large intensity error and vice versa. In this study, an intensity-weighted hurricane track density function (IW-HTDF) is designed as a new approach to the integrated evaluation of TC track, intensity, and size forecasts. The sensitivity of the TC track density to TC wind radius was investigated by calculating the IW-HTDF with density functions defined by 1) asymmetric, 2) symmetric, and 3) constant wind radii. Using the best-track data as the benchmark, IW-HTDF provides a specific score value for a TC forecast validated for a specific date and time or duration. This new TC forecast evaluation approach provides a relatively concise, integrated skill score compared with multiple skill scores when track, intensity and size are evaluated separately. It should be noted that actual observations of TC size data are very limited and so are the estimations of TC size forecasts. Therefore, including TC size as a forecast evaluation parameter is exploratory at the present. The proposed integrated evaluation method for TC track, intensity, and size forecasts can be used for evaluating the track forecast alone or in combination with intensity and size parameters. As observations and forecasts of TC size become routine in the future, including TC size as a forecast skill assessment parameter will become more imperative.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Lian Xie, xie@ncsu.edu, lianxie3@gmail.com

Abstract

Track, intensity, and, in some cases, size are usually used as separate evaluation parameters to assess numerical model performance on tropical cyclone (TC) forecasts. Such an individual-parameter evaluation approach often encounters contradictory skill assessments for different parameters, for instance, small track error with large intensity error and vice versa. In this study, an intensity-weighted hurricane track density function (IW-HTDF) is designed as a new approach to the integrated evaluation of TC track, intensity, and size forecasts. The sensitivity of the TC track density to TC wind radius was investigated by calculating the IW-HTDF with density functions defined by 1) asymmetric, 2) symmetric, and 3) constant wind radii. Using the best-track data as the benchmark, IW-HTDF provides a specific score value for a TC forecast validated for a specific date and time or duration. This new TC forecast evaluation approach provides a relatively concise, integrated skill score compared with multiple skill scores when track, intensity and size are evaluated separately. It should be noted that actual observations of TC size data are very limited and so are the estimations of TC size forecasts. Therefore, including TC size as a forecast evaluation parameter is exploratory at the present. The proposed integrated evaluation method for TC track, intensity, and size forecasts can be used for evaluating the track forecast alone or in combination with intensity and size parameters. As observations and forecasts of TC size become routine in the future, including TC size as a forecast skill assessment parameter will become more imperative.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Lian Xie, xie@ncsu.edu, lianxie3@gmail.com
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