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Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning
Author(s) -
Su Hui,
Wu Longtao,
Jiang Jonathan H.,
Pai Raksha,
Liu Alex,
Zhai Albert J.,
Tavallali Peyman,
DeMaria Mark
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl089102
Subject(s) - tropical cyclone , intensity (physics) , precipitation , environmental science , outflow , probabilistic logic , satellite , climatology , meteorology , storm , atmospheric sciences , computer science , geology , physics , artificial intelligence , quantum mechanics , astronomy
Tropical cyclone (TC) intensity change is controlled by both environmental conditions and internal storm processes. We show that TC 24‐hr subsequent intensity change (DV24) is linearly correlated with the departures in satellite observations of inner‐core precipitation, ice water content, and outflow temperature from respective threshold values corresponding to neutral TCs of nearly constant intensity. The threshold values vary linearly with TC intensity. Using machine learning with the inner‐core precipitation and the predictors currently employed at the National Hurricane Center (NHC) for probabilistic rapid intensification (RI) forecast guidance, our model outperforms the NHC operational RI consensus in terms of the Peirce Skill Score for RI in the Atlantic basin during 2009–2014 by 37%, 12%, and 138% for DV24 ≥ 25, 30, and 35 kt, respectively. Our probability of detection is 40%, 60%, and 200% higher than the operational RI consensus, while the false alarm ratio is only 4%, 7%, and 6% higher.