Understanding Biases in Tropical Cyclone Intensity Forecast Error

Author:

Na Wei1,McBride John L.2,Zhang Xing-Hai3,Duan Yi-Hong1

Affiliation:

1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

2. School of Earth Science, University of Melbourne, and Research and Development Division, Bureau of Meteorology, Melbourne, Victoria, Australia

3. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, and Glarun Technology, Fourteenth Research Institute, China Electronic Technology Group Corporation, Nanjing, China

Abstract

Abstract The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.

Funder

the National Key Basic Research Program of China

the Natural Science Foundation of China

the Basic Research Foundation of Chinese Academy of Meteorological Sciences

Publisher

American Meteorological Society

Subject

Atmospheric Science

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