Dynamical Seasonal Prediction of Tropical Cyclone Activity: Robust Assessment of Prediction Skill and Predictability

Gan Zhang, Hiroyuki Murakami, Rich Gudgel, Xiaosong Yang

Research output: Contribution to journalArticlepeer-review

Abstract

Improving the seasonal prediction of tropical cyclone (TC) activity demands a robust analysis of the prediction skill and the inherent predictability of TC activity. Using the resampling technique, this study analyzes a state-of-the-art prediction system and offers a robust assessment of when and where the seasonal prediction of TC activity is skillful. We found that uncertainties of initial conditions affect the predictions and the skill evaluation significantly. The sensitivity of predictions to initial conditions also suggests that landfall and high-latitude activity are inherently harder to predict. The lower predictability is consistent with the relatively low prediction skill in these regions. Additionally, the lower predictability is largely related to the atmospheric environment rather than the sea surface temperature, at least for the predictions initialized shortly before the hurricane season. These findings suggest the potential for improving the seasonal TC prediction and will help the development of the next-generation prediction systems.

Original languageEnglish (US)
Pages (from-to)5506-5515
Number of pages10
JournalGeophysical Research Letters
Volume46
Issue number10
DOIs
StatePublished - May 28 2019
Externally publishedYes

Keywords

  • decision making
  • predictability
  • seasonal prediction
  • tropical cyclone

ASJC Scopus subject areas

  • Geophysics
  • General Earth and Planetary Sciences

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