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Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning
  • +8
  • Ryan Eusebi,
  • Hui Su,
  • Longtao Wu,
  • Pingping Rong,
  • Karthik Balaguru,
  • L. Ruby Leung,
  • Yong-Sang Choi,
  • Pak Wai Chan,
  • Jianping Gan,
  • Mark Demaria,
  • Galina Chirokova
Ryan Eusebi
Caltech

Corresponding Author:[email protected]

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Hui Su
Hong Kong University of Science and Technology

Corresponding Author:[email protected]

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Longtao Wu
Jet Propulsion Laboratory
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Pingping Rong
Hong Kong University of Science and Technology
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Karthik Balaguru
Pacific Northwest National Laboratory (DOE)
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L. Ruby Leung
PNNL
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Yong-Sang Choi
Ewha Womans University
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Pak Wai Chan
Hong Kong Observatory
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Jianping Gan
The Hong Kong University of Science and Technology
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Mark Demaria
National Oceanic and Atmospheric Administration (NOAA)
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Galina Chirokova
Colorado State University

Corresponding Author:[email protected]

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Abstract

Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is sea surface salinity (SSS), which is an important proxy for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the effects of SSS on TC RI, we use a previously built statistical model consisting of a variety of machine learning (ML) methods. A calibrator was trained on top of the ML models to correct probability forecasts. The ML model performance is improved with the addition of SSS in the Eastern North Pacific and the Caribbean subregion of the North Atlantic. Limited improvement is found in the Western North Pacific. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.
09 Jul 2024Submitted to ESS Open Archive
11 Jul 2024Published in ESS Open Archive