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Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning

Su, Hui and Wu, Longtao and Jiang, Jonathan H. and Pai, Raksha and Liu, Alex and Zhai, Albert J. and Tavallali, Peyman and DeMaria, Mark (2020) Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning. Geophysical Research Letters, 47 (17). Art. No. e2020GL089102. ISSN 0094-8276. doi:10.1029/2020gl089102.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription ItemBest Track tropical cyclone data ItemBest Track tropical cyclone data ItemTRMM precipitation ItemMERRA‐2 reanalysis products ItemCloudSat tropical cyclone overpass data
http://mls.jpl.nasa.govRelated ItemMLS temperature data ItemSHIPS developmental and forecast datasets
Su, Hui0000-0003-1265-9702
Wu, Longtao0000-0001-8447-8180
Jiang, Jonathan H.0000-0002-5929-8951
Pai, Raksha0000-0003-3088-3994
Zhai, Albert J.0000-0002-0647-1730
Tavallali, Peyman0000-0001-7166-5489
DeMaria, Mark0000-0003-4746-4462
Additional Information:© 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Issue Online: 25 August 2020; Version of Record online: 25 August 2020; Manuscript accepted: 06 August 2020; Manuscript revised: 01 August 2020; Manuscript received: 08 June 2020. We thank Kerry Emanuel and Todd Julian for useful discussions, and two anonymous reviewers for constructive comments. H. S., L. W., J. H. J., and P. T. performed the work at Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. R. P. was supported by IBM Global Business Services and IBM Watson Analytics. A. L. was supported by RMDS Lab. This study is supported by Aura MLS and CloudSat flight projects, and JPL Data Science Pilot Project. All data used in this study are publicly available. The Best Track tropical cyclone data are available at and websites. The TRMM precipitation can be downloaded from website. The MERRA‐2 reanalysis products are available at website. The CloudSat tropical cyclone overpass data set is available at website. The MLS temperature data can be downloaded from website. The SHIPS developmental and forecast datasets are available at website. Please contact the corresponding author Hui Su at for any questions.
Funding AgencyGrant Number
National Oceanic and Atmospheric Administration (NOAA)UNSPECIFIED
Subject Keywords:tropical cyclone; rapid intensification; convective heating; internal storm processes; machine learning; operational forecast
Issue or Number:17
Record Number:CaltechAUTHORS:20200826-101828112
Persistent URL:
Official Citation:Su, H., Wu, L., Jiang, J.H., Pai, R., Liu, A., Zhai, A.J., Tavallali, P. and DeMaria, M. (2020), Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning. Geophys. Res. Lett., 47: e2020GL089102. doi:10.1029/2020GL089102
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105116
Deposited By: Tony Diaz
Deposited On:26 Aug 2020 19:22
Last Modified:16 Nov 2021 18:39

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