Development of Interpretable Probability Ellipse in Tropical Cyclone Track Forecasts Using Multiple Operational Ensemble Prediction Systems

Author:

Yoo Seungwoo1ORCID,Ho Chang‐Hoi12ORCID

Affiliation:

1. School of Earth and Environmental Sciences Seoul National University Seoul Korea

2. Department of Climate and Energy Systems Engineering Ewha Womans University Seoul Korea

Abstract

AbstractMost tropical cyclone (TC) forecasting centers have implemented a probabilistic circle to represent track uncertainty at a specified lead time. Recent studies suggest that probability ellipses constructed from ensemble prediction systems can convey the anisotropy of track predictability. In this study, a new probability ellipse model is developed to interpret the extent of forward speed and heading uncertainties in ensemble forecasts by selecting an equal proportion of members in the along‐ and cross‐track directions. This method is validated using the 2019–2021 western North Pacific (WNP) TC track forecasts from the ensemble predictions of the European Centre for Medium‐Range Weather Forecasts, the United States National Centers for Environmental Prediction, and the Korea Meteorological Administration. When the proportion of ensemble members in the ellipse is set to 70%, more than one‐half (50.0%–73.6%) of the forecasts, depending on the lead time, indicate reduced area compared with that of the circle. The mean areas of the probability ellipses are 4.9%, 7.0%, 10.0%, and 11.5% smaller than those of the circle in 48‐, 72‐, 96‐, and 120‐hr forecasts, respectively. The forward speed shows greater uncertainty than the heading, as evidenced by the along‐track radii being larger than the cross‐track counterpart in ∼60% of the samples, regardless of the lead time. In addition, the regional distribution of the along‐track/cross‐track ratio in the probability ellipses can explain the dominant direction of the track error in a particular location. The proposed probability ellipse shows potential for application in operational TC track predictions.

Publisher

American Geophysical Union (AGU)

Reference64 articles.

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2. Berg R.(2023).Tropical Storm Karl (AL142022). InNational Hurricane Center Tropical Cyclone Report. Retrieved fromhttps://www.nhc.noaa.gov/data/tcr/AL142022_Karl.pdf

3. Cangialosi J. P.(2022).2021 Hurricane season. InNational Hurricane Center Forecast Verification Report. Retrieved fromhttps://www.nhc.noaa.gov/verification/pdfs/Verification_2021.pdf

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