Abstract
Tropical cyclones (TCs), including tropical depressions and different categories of typhoons, hurricanes, and cyclonic storms, mostly originate over the oceans in the absence of direct observations. Thus, detailed monitoring and analysis of TCs has always been an unsolved problem. In the recent 20 years, great changes have taken place in domestic and foreign TC monitoring techniques, imposing a significant impact on TC operations and research. Some new technologies and products gradually emerge to support operations, with improved monitoring accuracy. In this paper, the progress on TC monitoring and analysis via meteorological satellites, radars, and airplanes in China and the world is reviewed, compared, and summarized, with special focuses on multisatellite fusion observations, in situ aircraft measurements, and some unconventional observation equipment such as rockets, saildrones, and underwater gliders. On this basis, the paper points out future directions for improving TC monitoring and analysis in aid of better TC forecast and early warning.
This is a preview of subscription content, log in via an institution to check access.
References
Aberson, S. D., K. J. Sellwood, and P. A. Leighton, 2017: Calculating dropwindsonde location and time from TEMP-DROP messages for accurate assimilation and analysis. J. Atmos. Oceanic Technol., 34, 1673–1678, doi: https://doi.org/10.1175/JTECH-D-17-0023.1.
Bessho, K., K. Date, M. Hayashi, et al., 2016: An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteor. Soc. Japan, 94, 151–183, doi: https://doi.org/10.2151/jmsj.2016-009.
Blackwell, W. J., S. Braun, R. Bennartz, et al., 2018: An overview of the TROPICS NASA Earth Venture Mission. Quart. J. Roy. Meteor. Soc., 144, 16–26, doi: https://doi.org/10.1002/qj.3290.
Blackwell, W. J., A. Cunningham, S. Donnelly, et al., 2022: The NASA tropics mission as a pathfinder for future LEO microwave sounders. IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, IEEE, 4873–4874, doi: https://doi.org/10.1109/IGARSS46834.2022.9884090.
Brown, S., S. Misra, A. Kitiyakara, et al., 2021: Compact Ocean Wind Vector Radiometer on ISS Mission Status. IOVWST Virtual, Pasadena, California, JPL Open Repository. Available online at https://hdl.handle.net/2014/54531. Accessed on 15 April 2024.
Cardone, V. J., J. D. Young, W. J. Pierson, et al., 1976: The Measurement of the Winds near the Ocean Surface with a Radiometer-Scatterometer on Skylab. NASA-JSC Contract NAS 9-13642, Final Report E-PN550, prepared jointly by the City University of New York and the University of Kansas CRES, Houston, Texas, 652 pp.
Chang, K.-F., C.-C. Wu, and K. Ito, 2023: On the rapid weakening of Typhoon Trami (2018): Strong sea surface temperature cooling associated with slow translation speed. Mon. Wea. Rev., 151, 227–251, doi: https://doi.org/10.1175/MWR-D-22-0039.1.
Chen, H. B., J. Li, W. Y. He, et al., 2021: IAP’s solar-powered unmanned surface vehicle actively passes through the center of Typhoon Sinlaku (2020). Adv. Atmos. Sci., 38, 538–545, doi: https://doi.org/10.1007/s00376-021-1006-1.
Chen, K. Y., and P. G. Guan, 2023: The impacts of assimilating Fengyun-4A atmospheric motion vectors on typhoon forecasts. Atmosphere, 14, 375, doi: https://doi.org/10.3390/atmos14020375.
Chen, Y. D., J. Shen, S. Y. Fan, et al., 2021: A study of the observational error statistics and assimilation applications of the FY-4A satellite atmospheric motion vector. Trans. Atmos. Sci., 44, 418–427, doi: https://doi.org/10.13878/j.cnki.dqkxxb.20190921001. (in Chinese)
Chou, K.-H., C.-C. Wu, P.-H. Lin, et al., 2011: The impact of dropwindsonde observations on typhoon track forecasts in DOTSTAR and T-PARC. Mon. Wea. Rev., 139, 1728–1743, doi: https://doi.org/10.1175/2010MWR3582.1.
Christophersen, H., J. Sippel, A. Aksoy, et al., 2022: Recent advancements for tropical cyclone data assimilation. Ann. N. Y. Acad. Sci., 1517, 25–43, doi: https://doi.org/10.1111/nyas.14873.
Cione, J. J., E. A. Kalina, E. W. Uhlhorn, et al., 2016: Coyote unmanned aircraft system observations in Hurricane Edouard (2014). Earth Space Sci., 3, 370–380, doi: https://doi.org/10.1002/2016EA000187.
DeMaria, M., R. T. DeMaria, J. A. Knaff, et al., 2012: Tropical cyclone lightning and rapid intensity change. Mon. Wea. Rev., 140, 1828–1842, doi: https://doi.org/10.1175/MWR-D-11-00236.1.
Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420–430, doi: https://doi.org/10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.
Emanuel, K., 2018: 100 years of progress in tropical cyclone research. Meteor. Monogr., 59, 15.1–15.68, doi: https://doi.org/10.1175/AMS-MONOGRAPHS-D-18-0016.1.
Eyre, J. R., S. J. English, and M. Forsythe, 2020: Assimilation of satellite data in numerical weather prediction. Part I: The early years. Quart. J. Roy. Meteor. Soc., 146, 49–68, doi: https://doi.org/10.1002/qj.3654.
Eyre, J. R., W. Bell, J. Cotton, et al., 2022: Assimilation of satellite data in numerical weather prediction. Part II: Recent years. Quart. J. Roy. Meteor. Soc., 148, 521–556, doi: https://doi.org/10.1002/qj.4228.
Foltz, G. R., C. D. Zhang, C. Meinig, et al., 2022: An unprecedented view inside a hurricane. Eos, 103, doi: https://doi.org/10.1029/2022EO220228.
Goodman, S. J., 2020: GOES-R Series introduction. The GOES-R Series: A New Generation of Geostationary Environmental Satellites, J. S. Goodman, T. J. Schmit, J. Daniels, et al., Eds., Elsevier, 1–3, doi: https://doi.org/10.1016/B978-0-12-814327-8.00001-9.
Hirano, S., K. Ito, H. Yamada, et al., 2022: Deep eye clouds in Tropical Cyclone Trami (2018) during T-PARCII dropsonde observations. J. Atmos. Sci., 79, 683–703, doi: https://doi.org/10.1175/JAS-D-21-0192.1.
Holbach, H., and O. Bousquet, 2022: Advancements in aircraft and in situ observations of tropical cyclones. Tenth International Workshop on Tropical Cyclone (IWTC-10), Bali, Indonesia, 5–9 December, WMO, Topic 1.3. Available online at https://community.wmo.int/iwtc-10-reports. Accessed on 15 April 2024.
Holbach, H. M., O. Bousquet, L. Bucci, et al., 2023: Recent advancements in aircraft and in situ observations of tropical cyclones. Trop. Cyclone Res. Rev., 12, 81–99, doi: https://doi.org/10.1016/j.tcrr.2023.06.001.
Horstmann, J., S. Falchetti, C. Wackerman, et al., 2015: Tropical cyclone winds retrieved from C-band cross-polarized synthetic aperture radar. IEEE Trans. Geosci. Remote Sens., 53, 2887–2898, doi: https://doi.org/10.1109/TGRS.2014.2366433.
Hwang, P. A., and F. Fois, 2015: Surface roughness and breaking wave properties retrieved from polarimetric microwave radar backscattering. J. Geophys. Res. Oceans, 120, 3640–3657, doi: https://doi.org/10.1002/2015JC010782.
IWTC, 2018: Final Report of Ninth International Workshop on Tropical Cyclone (IWTC-9). Honolulu, Hawaii, USA, WMO, 597 pp. Available online at https://library.wmo.int/doc_num.php?explnum_id=11006. Accessed on 8 April 2024.
IWTC, 2022: Tenth International Workshop on Tropical Cyclone (IWTC-10). Bali, Indonesia, WMO, 675 pp. Available online at https://community.wmo.int/iwtc-10-reports. Accessed on 8 April 2024.
Jackson, C. R., T. W. Ruff, J. A. Knaff, et al., 2021: Chasing cyclones from space. Eos, 102, doi: https://doi.org/10.1029/2021EO159148.
Knaff, J. A., M. DeMaria, D. A. Molenar, et al., 2011: An automated, objective, multiple-satellite-platform tropical cyclone surface wind analysis. J. Appl. Meteor. Climatol., 50, 2149–2166, doi: https://doi.org/10.1175/2011JAMC2673.1.
Lei, X. T., M. Lei, B. K. Zhao, et al., 2017: New technology and experiment of rocket dropsondes for typhoon observation. Chinese Sci. Bull., 62, 3789–3796, doi: https://doi.org/10.1360/N972017-00160. (in Chinese)
Lu, N. M., and S. Y. Gu, 2016: Review and prospect on the development of meteorological satellites. J. Remote Sens., 20, 832–841, doi: https://doi.org/10.11834/jrs.20166194. (in Chinese)
Lungu, T., and P. S. Callahan, 2006: QuikSCAT Science Data Product User’s Manual: Overview and Geophysical Data Products. Version 3.0, JPL Tech. Rep. D-18053-Rev A, Jet Propulsion Laboratory, 91 pp.
Meissner, T., L. Ricciardulli, and F. J. Wentz, 2017: Capability of the SMAP mission to measure ocean surface winds in storms. Bull. Amer. Meteor. Soc., 98, 1660–1677, doi: https://doi.org/10.1175/BAMS-D-16-0052.1.
Miles, T. N., D. X. Zhang, G. R. Foltz, et al., 2021: Uncrewed ocean gliders and saildrones support hurricane forecasting and research. Frontiers in Ocean Observing: Documenting Ecosystems, Understanding Environmental Changes, Forecasting Hazards, E. S. Kappel, S. K. Juniper, S. Seeyave, et al., Eds., 78–81, doi: https://doi.org/10.5670/oceanog.2021.supplement.02-28.
Mouche, A., B. Chapron, J. Knaff, et al., 2019: Copolarized and cross-polarized SAR measurements for high-resolution description of major hurricane wind structures: Application to Irma category 5 hurricane. J. Geophys. Res. Oceans, 124, 3905–3922, doi: https://doi.org/10.1029/2019JC015056.
Neumann, C. J., 1952: Wind Estimations from Aerial Observations of Sea Conditions. Weather Squadron Two (VJ-2), NAS Jacksonville, Jacksonville, Florida, 29 pp.
Nonaka, K., S. Nishimura, and Y. Igarashi, 2019: Utilization of Estimated Sea Surface Wind Data Based on Himawari-8/9 Low-Level AMVs for Tropical Cyclone Analysis. Technical Review No. 21, RSMC Tokyo-Typhoon Center, Tokyo, 16 pp.
Olander, T. L., and C. S. Velden, 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287–298, doi: https://doi.org/10.1175/WAF975.1.
Olander, T. L., and C. S. Velden, 2019: The advanced Dvorak technique (ADT) for estimating tropical cyclone intensity: Update and new capabilities. Wea. Forecasting, 34, 905–922, doi: https://doi.org/10.1175/WAF-D-19-0007.1.
Olander, T. L., A. Wimmers, C. Velden, et al., 2021: Investigation of machine learning using satellite-based advanced Dvorak technique analysis parameters to estimate tropical cyclone intensity. Wea. Forecasting, 36, 2161–2186, doi: https://doi.org/10.1175/WAF-D-20-0234.1.
Oyama, R., M. Sawada, and K. Shimoji, 2018: Diagnosis of tropical cyclone intensity and structure using upper tropospheric atmospheric motion vectors. J. Meteor. Soc. Japan, 96B, 3–26, doi: https://doi.org/10.2151/jmsj.2017-024.
Portabella, M., A. Stoffelen, W. M. Lin, et al., 2012: Rain effects on ASCAT-retrieved winds: Toward an improved quality control. IEEE Trans. Geosci. Remote Sens., 50, 2495–2506, doi: https://doi.org/10.1109/TGRS.2012.2185933.
Qian, C. H., Z. C. Li, F. Q. Zhang, et al., 2012: Review on international aircraft reconnaissance of tropical cyclones. Adv. Meteor. Sci. Technol., 2, 6–16. (in Chinese)
Qin, X. H., W. S. Duan, P.-W. Chan, et al., 2023: Effects of dropsonde data in field campaigns on forecasts of tropical cyclones over the western North Pacific in 2020 and the role of CNOP sensitivity. Adv. Atmos. Sci., 40, 791–803, doi: https://doi.org/10.1007/s00376-022-2136-9.
Reul, N., B. Chapron, E. Zabolotskikh, et al., 2016: A revised L-band radio-brightness sensitivity to extreme winds under Tropical Cyclones: the five year SMOS-storm database. Remote Sens. Environ., 180, 274–291, doi: https://doi.org/10.1016/j.rse.2016.03.011.
Reul, N., B. Chapron, E. Zabolotskikh, et al., 2017: A new generation of tropical cyclone size measurements from space. Bull. Amer. Meteor. Soc., 98, 2367–2385, doi: https://doi.org/10.1175/BAMS-D-15-00291.1.
Ricciardulli, L., and F. J. Wentz, 2015: A scatterometer geophysical model function for climate-quality winds: QuikSCAT Ku-2011. J. Atmos. Oceanic Technol., 32, 1829–1846, doi: https://doi.org/10.1175/JTECH-D-15-0008.1.
Ricciardulli, L., and B. Howell, 2022: Remote sensing and TC analysis: Current and emerging satellite sensors. Tenth International Workshop on Tropical Cyclone (IWTC-10), Bali, Indonesia, 5–9 December, WMO, Topic 1.1. Available online at https://community.wmo.int/iwtc-10-reports. Accessed on 15 April 2024.
Ricciardulli, L., G. R. Foltz, A. Manaster, et al., 2022: Assessment of Saildrone extreme wind measurements in Hurricane Sam using MW satellite sensors. Remote Sens., 14, 2726, doi: https://doi.org/10.3390/rs14122726.
Sampson, C. R., E. M. Fukada, J. A. Knaff, et al., 2017: Tropical cyclone gale wind radii estimates for the western North Pacific. Wea. Forecasting, 32, 1029–1040, doi: https://doi.org/10.1175/WAF-D-16-0196.1.
Sampson, C. R., J. S. Goerss, J. A. Knaff, et al., 2018: Tropical cyclone gale wind radii estimates, forecasts, and error forecasts for the western North Pacific. Wea. Forecasting, 33, 1081–1092, doi: https://doi.org/10.1175/WAF-D-17-0153.1.
Schmetz, J., P. Pili, S. Tjemkes, et al., 2002: An introduction to Meteosat Second Generation (MSG). Bull. Amer. Meteor. Soc., 83, 977–992, doi: https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2.
Shimada, U., 2016: Tropical Cyclone Central Pressure Estimation Using Doppler Radar Observations at JMA. Technical Review No. 18, RSMC Tokyo-Typhoon Center, Tokyo, 25 pp.
Shoemaker, D. N., W. M. Gray, and J. D. Sheaffer, 1990: Influence of synoptic track aircraft reconnaissance on JTWC tropical cyclone track forecast errors. Wea. Forecasting, 5, 503–507, doi: https://doi.org/10.1175/1520-0434(1990)005<0503:IOSTAR>2.0.CO;2.
Sippel, J. A., X. R. Wu, S. D. Ditchek, et al., 2022: Impacts of assimilating additional reconnaissance data on operational GFS tropical cyclone forecasts. Wea. Forecasting, 37, 1615–1639, doi: https://doi.org/10.1175/WAF-D-22-0058.1.
Smith, M., and R. Toumi, 2021: Using video recognition to identify tropical cyclone positions. Geophys. Res. Lett., 48, e2020GL091912, doi: https://doi.org/10.1029/2020GL091912.
Stevenson, S. N., K. L. Corbosiero, M. DeMaria, et al., 2018: A 10-year survey of tropical cyclone inner-core lightning bursts and their relationship to intensity change. Wea. Forecasting, 33, 23–36, doi: https://doi.org/10.1175/WAF-D-17-0096.1.
Stiles, B. W., and S. H. Yueh, 2002: Impact of rain on spaceborne Ku-band wind scatterometer data. IEEE Trans. Geosci. Remote Sens., 40, 1973–1983, doi: https://doi.org/10.1109/TGRS.2002.803846.
Stoffelen, A., J. A. Verspeek, J. Vogelzang, et al., 2017: The CMOD7 geophysical model function for ASCAT and ERS wind retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 2123–2134, doi: https://doi.org/10.1109/JSTARS.2017.2681806.
Sun, Z. Y., L. N. Bai, X. S. Zhu, et al., 2023: The extraordinarily large vortex structure of Typhoon In-fa (2021), observed by spaceborne microwave radiometer and synthetic aperture radar. Atmos. Res., 292, 106837, doi: https://doi.org/10.1016/j.atmosres.2023.106837.
Tang, J., J. A. Zhang, P. Chan, et al., 2021: A direct aircraft observation of helical rolls in the tropical cyclone boundary layer. Sci. Rep., 11, 18771, doi: https://doi.org/10.1038/s41598-021-97766-7.
Velden, C. S., 2020: GOES-R series applications to hurricane monitoring. The GOES-R Series: A New Generation of Geostationary Environmental Satellites, S. J. Goodman, T. J. Schmit, J. Daniels, et al., Eds., Elsevier, Amsterdam, 95–102, doi: https://doi.org/10.1016/B978-0-12-814327-8.00009-3.
Velden, C. S., and D. Herndon, 2020: A consensus approach for estimating tropical cyclone intensity from meteorological satellites: SATCON. Wea. Forecasting, 35, 1645–1662, doi: https://doi.org/10.1175/WAF-D-20-0015.1.
Velden, C. S., T. L. Olander, and R. M. Zehr, 1998: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. Forecasting, 13, 172–186, doi: https://doi.org/10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2.
Wadler, J. B., J. A. Zhang, R. F. Rogers, et al., 2021: The rapid intensification of Hurricane Michael (2018): Storm structure and the relationship to environmental and air–sea interactions. Mon. Wea. Rev., 149, 245–267, doi: https://doi.org/10.1175/MWR-D-20-0145.1.
Wang, H., J. H. Zhu, M. S. Lin, et al., 2020: Evaluating Chinese HY-2B HSCAT ocean wind products using buoys and other scatterometers. IEEE Geosci. Remote Sens. Lett., 17, 923–927, doi: https://doi.org/10.1109/LGRS.2019.2940384.
Wang, X., S. H. Tang, and C. Y. Wei, 2021: Review and vision of Fengyun meteorological satellites weather application. Sci. Technol. Rev., 39, 23–30, doi: https://doi.org/10.3981/j.issn.1000-7857.2021.15.002. (in Chinese)
Wang, Z. X., J. H. Zou, A. Stoffelen, et al., 2021: Scatterometer sea surface wind product validation for HY-2C. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14, 6156–6164, doi: https://doi.org/10.1109/JSTARS.2021.3087742.
Weissman, D. E., and M. A. Bourassa, 2008: Measurements of the effect of rain-induced sea surface roughness on the QuikS-CAT scatterometer radar cross section. IEEE Trans. Geosci. Remote Sens., 46, 2882–2894, doi: https://doi.org/10.1109/TGRS.2008.2001032.
Wentz, F. J., and D. K. Smith, 1999: A model function for the ocean-normalized radar cross section at 14 GHz derived from NSCAT observations. J. Geophys. Res. Oceans, 104, 11,499–11,514, doi: https://doi.org/10.1029/98JC02148.
Wimmers, A., and Q.-P. Duong, 2022: Objective satellite methods including AI algorithms. Tenth International Workshop on Tropical Cyclone (IWTC-10), Bali, Indonesia, 5–9 December, WMO, Topic 1.2. Available online at https://community.wmo.int/iwtc-10-reports. Accessed on 15 April 2024.
Wu, C.-C., P.-H. Lin, S. Aberson, et al., 2005: Dropwindsonde observations for typhoon surveillance near the Taiwan region (DOTSTAR): An overview. Bull. Amer. Meteor. Soc., 86, 787–790.
Yin, R. Y., W. Han, Z. Q. Gao, et al., 2021: Impact of high temporal resolution FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) radiance measurements on typhoon forecasts: Maria (2018) case with GRAPES global 4D-Var assimilation system. Geophys. Res. Lett., 48, e2021GL093672, doi: https://doi.org/10.1029/2021GL093672.
Zawislak, J., R. F. Rogers, S. D. Aberson, et al., 2022: Accomplishments of NOAA’s airborne hurricane field program and a broader future approach to forecast improvement. Bull. Amer. Meteor. Soc., 103, E311–E338, doi: https://doi.org/10.1175/BAMS-D-20-0174.1.
Zhang, B., and W. Perrie, 2012: Cross-polarized synthetic aperture radar: A new potential measurement technique for hurricanes. Bull. Amer. Meteor. Soc., 93, 531–541, doi: https://doi.org/10.1175/BAMS-D-11-00001.1.
Zhang, F. Q., and Y. H. Weng, 2015: Predicting hurricane intensity and associated hazards: A five-year real-time forecast experiment with assimilation of airborne Doppler radar observations. Bull. Amer. Meteor. Soc., 96, 25–33, doi: https://doi.org/10.1175/BAMS-D-13-00231.1.
Zhang, F. Q., Y. H. Weng, J. F. Gamache, et al., 2011: Performance of convection-permitting hurricane initialization and prediction during 2008–2010 with ensemble data assimilation of inner-core airborne Doppler radar observations. Geophys. Res. Lett., 38, L15810, doi: https://doi.org/10.1029/2011GL048469.
Zhang, W. J., W. Hui, W. T. Lyu, et al., 2020: FY-4A LMI observed lightning activity in Super Typhoon Mangkhut (2018) in comparison with WWLLN data. J. Meteor. Res., 34, 336–352, doi: https://doi.org/10.1007/s13351-020-9500-4.
Zhang, Z.-Q., F. Lu, X. Fang, et al., 2017: Application and development of FY-4 meteorological satellite. Aerosp. Shanghai, 34, 8–19, doi: https://doi.org/10.19328/j.cnki.1006-1630.2017.04.002. (in Chinese)
Zhao, B. K., J. Tang, X. T. Lei, et al., 2022: Progress on the experiment of a multi-platform collaborative field campaign on offshore typhoon. Adv. Earth Sci., 37, 771–785, doi: https://doi.org/10.11867/j.issn.1001-8166.2022.053. (in Chinese)
Zhao, Y. L., A. A. Mouche, B. Chapron, et al., 2018: Direct comparison between active C-band radar and passive L-band radiometer measurements: Extreme event cases. IEEE Geosci. Remote Sens. Lett., 15, 897–901, doi: https://doi.org/10.1109/LGRS.2018.2811712.
Zhou, G. B., X. Fang, Q. F. Qian, et al., 2022: Application of artificial intelligence technology in typhoon monitoring and forecasting. Front. Earth Sci., 10, 974497, doi: https://doi.org/10.3389/feart.2022.974497.
Zhuo, J.-Y., and Z.-M. Tan, 2021: Physics-augmented deep learning to improve tropical cyclone intensity and size estimation from satellite imagery. Mon. Wea. Rev., 149, 2097–2113, doi: https://doi.org/10.1175/MWR-D-20-0333.1.
Acknowledgments
The authors thank Xiang Chunyi, a senior typhoon forecaster in National Meteorological Centre, CMA, for preparing Fig. 1 of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Natural Science Foundation of China (41930972).
Supporting information
Rights and permissions
About this article
Cite this article
Qian, C., Li, Y., Xu, Y. et al. Tropical Cyclone Monitoring and Analysis Techniques: A Review. J Meteorol Res 38, 351–367 (2024). https://doi.org/10.1007/s13351-024-3135-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-024-3135-9