Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 22, 2025
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 6, pp. 25-39

Methods for retrieval of tropical cyclone intensity from remote sensing data: Current status and prospects

A.N. Yakusheva 1 , D.M. Ermakov 1, 2 , E.A. Sharkov 1 
1 Space Research Institute RAS, Moscow, Russia
2 Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow Region, Russia
Accepted: 25.11.2025
DOI: 10.21046/2070-7401-2025-22-6-25-39
The paper presents an analysis of the most well-known modern methods for remote sensing diagnostics of tropical cyclone (TC) intensity and their historical predecessors based on satellite data. The evolution of the algorithms from the classical subjective Dvorak technique (1975) to modern automated Satellite Consensus (SATCON) systems and machine learning methods is examined. Four classical approaches are described: the Dvorak technique and its objective modification, the Advanced Dvorak Technique, the SATCON, integrating infrared and microwave satellite observation data, as well as the latest methods based on artificial neural networks. For each method, the material is systematized with regard to analysis of operating principles, satellite data used, quality characteristics, and existing limitations. It is shown that modern neural network approaches demonstrate the best accuracy in TC intensity estimation with a root mean square error of 8–11 knots, with the capability to efficiently process unprecedentedly large data arrays, being particularly effective at early stages of TC development, unlike traditional methods that work better with the most intense TCs.
Keywords: tropical cyclone intensity, remote sensing methods, Dvorak method, artificial intelligence, comparative analysis
Full text

References:

  1. Bondur V. G., Krapivin V. F., Kosmicheskii monitoring tropicheskikh tsiklonov (Space monitoring of tropical cyclones), Moscow: NII “Aehrokosmos”, Nauchnyi mir, 2014, 507 p. (in Russian).
  2. Palmén E., Newton C. W., Atmospheric circulation systems: Their structure and physical interpretation, New York: Academic Press, 1969, 603 p.
  3. Sharkov E. A., Atmospheric disasters: evolution of scientific views and the role of remote sensing, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2005, Iss. 2, V. 1, pp. 55–62 (in Russian).
  4. Sharkov E. A., Remote sensing of tropical cyclogenesis: Features and scientific advances in the present state of the art, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, V. 7, No. 1, pp. 29–48 (in Russian).
  5. Yakusheva A. N., Ermakov D. M., Development of a new automatic method for reconstructing the intensity of tropical cyclones from multispectral satellite Earth observations using artificial neural networks, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, V. 21, No. 2, pp. 336–349 (in Russian), DOI: 10.21046/2070-7401-2024-21-2-336-349.
  6. Ahmed R., Mohapatra M., Dwivedi S. et al., An overview of the Satellite Consensus (SATCON) algorithm to estimate tropical cyclone intensity over the North Indian Ocean, J. Earth System Science, 2022, V. 131, Article 169, DOI: 10.1007/s12040-022-01901-5.
  7. Brueske K. F., Velden C., Satellite-based tropical cyclone intensity estimation using the NOAA-KLM series Advanced Microwave Sounding Unit (AMSU), Monthly Weather Review, 2003, V. 131, No. 4, pp. 687–697, DOI: 10.1175/1520-0493(2003)131<0687:SBTCIE>2.0.CO;2.
  8. Chen R., Zhang W., Wang X., Machine learning in tropical cyclone forecast modeling: A review, Atmosphere, 2020, V. 11, No. 7, Article 676, DOI: 10.3390/atmos11070676.
  9. Combinido J. S., Mendoza J. R., Aborot J., A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images, Proc. 24 th Intern. Conf. Pattern Recognition (ICPR), 2018, pp. 1474–1480, DOI: 10.1109/ICPR.2018.8545593.
  10. Dvorak V. F., A technique for the analysis and forecasting of tropical cyclone intensities from satellite pictures, NOAA Tech. Memo. NESS 36, Washington, DC: NOAA/NESDIS, 1972, 15 p.
  11. Dvorak V. F., Tropical cyclone intensity analysis and forecasting from satellite imagery, Monthly Weather Review, 1975, V. 103, No. 5, pp. 420–430, DOI: 10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.
  12. Dvorak V., Tropical cyclone intensity analysis using satellite data: technical report, Washington, DC, 1984, 47 p.
  13. Emanuel K., Tropical cyclones, Annual Review of Earth and Planetary Sciences, 2003, V. 31, pp. 75–104, DOI: 10.1146/annurev.earth.31.100901.141259.
  14. Emanuel K., Increasing destructiveness of tropical cyclones over the past 30 years, Nature, 2005, V. 436, No. 7051, pp. 686–688, DOI: 10.1038/nature03906.
  15. Herndon D., Velden C., Upgrades to the UW–CIMSS AMSU-based TC intensity algorithm, Proc. 26th Conf. on Hurricanes and Tropical Meteorology, Boston, MA: American Meteorological Soc., 2004, pp. 118–119.
  16. Herndon D. C., Velden C. S., Estimating TC intensity using the SSMIS and ATMS sounders, American Meteorological Soc., 2012, Article P1.21, 5 p. https://ams.confex.com/ams/30Hurricane/webprogram/Paper205422.html.
  17. Klotz B. W., Uhlhorn E. W., Improved stepped frequency microwave radiometer tropical cyclone surface winds in heavy precipitation, J. Atmospheric and Oceanic Technology, 2014, V. 31, No. 11, pp. 2392–2408, DOI: 10.1175/JTECH-D-14-00028.1.
  18. Knaff J. A., Brown D. P., Courtney J. et al., An evaluation of Dvorak technique-based tropical cyclone intensity estimates, Weather and Forecasting, 2010, V. 25, No. 5, pp. 1362–1379, DOI: 10.1175/2010WAF2222375.1.
  19. Lee R. S.T., Lin J. N. K., An elastic contour matching model for tropical cyclone pattern recognition, IEEE Trans. Systems, Man, and Cybernetics. Pt. B (Cybernetics), 2001, V. 31, No. 3, pp. 413–417, DOI: 10.1109/3477.931532.
  20. Lee J., Im J., Cha D.-H. et al., Tropical cyclone intensity estimation using multi-dimensional convolutional neural networks from geostationary satellite data, Remote Sensing, 2020, V. 12, No. 1, Article 108, DOI: 10.3390/rs12010108.
  21. Lee Y.-J., Hall D., Liu Q. et al., Interpretable tropical cyclone intensity estimation using Dvorak-inspired machine learning techniques, Engineering Applications of Artificial Intelligence, 2021, V. 101, Article 104233, DOI: 10.1016/j.engappai.2021.104233.
  22. Olander T. L., Velden C. S., The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery, Weather and Forecasting, 2007, V. 22, No. 2, pp. 287–298, DOI: 10.1175/WAF975.1.
  23. Olander T. L., Velden C. S., ADT —Advanced Dvorak Technique: Users’ Guide, Madison, WI: Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, 2015, 76 p.
  24. Olander T. L., Velden C. S., The Advanced Dvorak Technique (ADT) for estimating tropical cyclone intensity: update and new capabilities, Weather and Forecasting, 2019, V. 34, No. 4, pp. 905–922, DOI: 10.1175/WAF-D-19-0007.1.
  25. Pielke R. A., Jr., Pielke R. A., Sr., Hurricanes: their nature and impacts on society, Chichester, New York: John Wiley and Sons, 1997, 279 p.
  26. Reul N., Chapron B., Zabolotskikh E. et al., A new generation of tropical cyclone size measurements from space, Bull. American Meteorological Soc., 2017, V. 98, No. 11, pp. 2367–2385, DOI: 10.1175/BAMS-D-15-00291.1.
  27. Velden C. S., Herndon D. C., Update on the SATellite CONsensus (SATCON) algorithm for estimating TC intensity, 31st Conf. on Hurricanes and Tropical Meteorology, San Diego, CA: Amer. Meteor. Soc., 2014, https://tropic.ssec.wisc.edu/misc/satcon/hurrconf_2014_satcon_poster.pdf.
  28. Velden C. S., Herndon D., A consensus approach for estimating tropical cyclone intensity from meteorological satellites: SATCON, Weather and Forecasting, 2020, V. 35, No. 4, pp. 1645–1662, DOI: 10.1175/WAF-D-20-0015.1.
  29. Velden C. S., Olander T. L., Zehr R. M., Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery, Weather and Forecasting, 1998, V. 13, No. 1, pp. 172–186, DOI: 10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2.
  30. Wimmers A., Velden C., Cossuth J. H., Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery, Monthly Weather Revtew, 2019, V. 147, No. 6, pp. 2261–2282, DOI: 10.1175/MWR-D-18-0391.1.
  31. Xiang K., Yang X., Zhang M. et al., Objective estimation of tropical cyclone intensity from active and passive microwave remote sensing observations in the northwestern Pacific Ocean, Remote Sensing, 2019, V. 11, No. 6, Article 627, DOI: 10.3390/rs11060627.
  32. Zhao Y., Zhao C., Sun R., Wang Z., A multiple linear regression model for tropical cyclone intensity estimation from satellite infrared images, Atmosphere, 2016, V. 7, No. 3, Article 40, DOI: 10.3390/atmos7030040.