Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning
Abstract
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.
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 https://www.nhc.noaa.gov/data/#hurdat and https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks websites. The TRMM precipitation can be downloaded from https://pmm.nasa.gov/data-access/downloads/trmm website. The MERRA‐2 reanalysis products are available at https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/ website. The CloudSat tropical cyclone overpass data set is available at https://adelaide.cira.colostate.edu/tc/ website. The MLS temperature data can be downloaded from http://mls.jpl.nasa.gov website. The SHIPS developmental and forecast datasets are available at http://rammb.cira.colostate.edu/research/tropical_cyclones/ships/developmental_data.asp website. Please contact the corresponding author Hui Su at hui.su@jpl.nasa.gov for any questions.Attached Files
Published - 2020GL089102.pdf
Supplemental Material - grl61100-sup-0001-2020gl089102-si.docx
Files
| Name | Size | Download all |
|---|---|---|
|
md5:427158bc66a5ee9185415eee17e3be15
|
4.8 MB | Download |
|
md5:a6e9b2d379bbe7ef7f9cc0180fd6cd8c
|
1.0 MB | Preview Download |
Additional details
- Eprint ID
- 105116
- Resolver ID
- CaltechAUTHORS:20200826-101828112
- NASA/JPL/Caltech
- IBM
- RMDS Lab
- National Oceanic and Atmospheric Administration (NOAA)
- Created
-
2020-08-26Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field