Abstract
A group of algorithms for estimating the current intensity (CI) of typhoons, which use infrared and microwave sensor-based images as the input of the algorithm because it is more skilled than each algorithm separately, are used to create a technique to estimate the typhoon intensity which is known as SATCON. In the current study, an effort was undertaken to assess how well the SATCON approach performed for estimating typhoon intensity throughout the West Pacific basin from year 2017 to 2021. To do this, 26 typhoons over the West Pacific basin were analysed using the SATCON-based technique, and the estimates were compared to the best track parameters provided by the Regional Specialized Meteorological Centre (RSMC), Tokyo. The maximum sustained surface winds (Vmax) and estimated central pressures (ECP) for various ‘T’ numbers and types of storm throughout the entire year, as well as during the pre-monsoon (March–July) and post-monsoon (July–February) seasons, have been compared. When compared to weaker and very strong typhoons, the ability of the SATCON algorithm to estimate intensity is determined to be rather excellent for mid-range typhoons. We demonstrate that SATCON is more effective in the post-monsoon across the West Pacific basin than in the pre-monsoon by comparing the algorithm results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmed R, Mohapatra M, Dwivedi S, Giri R K and Kant S 2022 An overview of the Satellite Consensus (SATCON) algorithm to estimate tropical cyclone intensity over the North Indian Ocean; J. Earth Syst. Sci. 131(3) 169.
Bankert et al. 2016 Tropical cyclone intensity estimation via passive microwave data features; In: 32nd Conf. on Hurricanes and Tropical Meteorology, San Juan, Puerto Rico; Am. Meteor. Soc. (AMS supported meetings (confex.com)).
Brueske K F and Velden C S 2003 Satellite-based tropical cyclone intensity estimation using the NOAA-KLM series Advanced Microwave Sounding Unit (AMSU); Mon. Weather Rev. 131(4) 687–697.
Demuth et al. 2004 Evaluation of advanced microwave sounding unit tropical-cyclone intensity and size estimation algorithms; J. Appl. Meteorol. Clim. 43(2) 282–296.
Dvorak V F 1975 Tropical cyclone intensity analysis and forecasting from satellite imagery; Mon. Weather Rev. 103(5) 420–430.
Dvorak V F 1984 Tropical cyclone intensity analysis using satellite data; US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service 11.
Hennon et al. 2015 Cyclone center: Can citizen scientists improve tropical cyclone intensity records?; Bull. Am. Meteorol. Soc. 96(4) 591–607.
Herndon D and Velden C S 2018 An update on the CIMSS SATellite CONsensus (SATCON) tropical cyclone intensity algorithm; 33rd Conf. on Hurricanes and Tropical Meteorology, Ponte Vedra, FL, Am. Meteor. Soc. 284, https://ams.confex.com/ams/33HURRICANE/webprogram/Paper340235.html.
Jiang et al. 2019 Estimation of tropical cyclone intensity in the North Atlantic and northeastern Pacific basins using TRMM satellite passive microwave observations; J. Appl. Meteorol. Clim. 58(2) 185–197.
Knaff et al. 2010 An evaluation of Dvorak technique-based tropical cyclone intensity estimates; Weather Forecast. 25(5) 1362–1379.
Kossin et al. 2007 Estimating hurricane wind structure in the absence of aircraft reconnaissance; Weather Forecast. 22(1) 89–101.
Olander T L and Velden C S 2019 The advanced Dvorak technique (ADT) for estimating tropical cyclone intensity: Update and new capabilities; Am. Meteorol. Soc. 34(4) 905–922.
Sampson C R and Schrader A J 2000 The automated tropical cyclone forecasting system (version 3.2); Bull. Am. Meteorol. Soc. 81(6) 1231–1240.
Schwerdt et al. 1979 Meteorological criteria for standard project hurricane and probable maximum hurricane windfields, Gulf and East Coasts of the United States, NOAA Technical Report NWS 23.
Tokyo R 2017 Annual Report on activities of the RSMC Tokyo-Typhoon Center 2017; RSMC Tokyo.
Tokyo R 2018 Annual Report on activities of the RSMC Tokyo-Typhoon Center 2018; RSMC Tokyo.
Tokyo R 2019 Annual Report on activities of the RSMC Tokyo-Typhoon Center 2019; RSMC Tokyo.
Tokyo R 2020 Annual Report on activities of the RSMC Tokyo-Typhoon Center 2020; RSMC Tokyo.
Tokyo R 2021 Annual Report on activities of the RSMC Tokyo-Typhoon Center 2021; RSMC Tokyo.
Velden C S and Herndon D 2020 A consensus approach for estimating tropical cyclone intensity from meteorological satellites: SATCON; Weather Forecast. 35(4) 1645–1662.
Velden et al. 2006 The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years; Bull. Am. Meteorol. Soc. 87(9) 1195–1210.
Acknowledgements
The RSMC, Tokyo and CIMSS-SATCON are thanked by the authors for providing the information used in this article. The authors appreciate the anonymous peer reviewers’ insightful criticism, which helped the paper’s quality.
Author information
Authors and Affiliations
Contributions
Monu Yadav: Conceptualization, investigation, data curation, methodology, validation, preparation of tables/figures. Laxminarayan Das: Supervision, reviewing and editing.
Corresponding author
Additional information
Communicated by Vijayakumar S Nair
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yadav, M., Das, L. Analyze the SATCON algorithm’s capability to estimate tropical storm intensity across the West Pacific basin. J Earth Syst Sci 133, 79 (2024). https://doi.org/10.1007/s12040-024-02276-5
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12040-024-02276-5