Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2793-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2793-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ASM-SS: the first quasi-global high-spatial-resolution coastal storm surge dataset reconstructed from tide gauge records
Lianjun Yang
MOE Key Laboratory of Geospace Environment and Geodesy, School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Taoyong Jin
CORRESPONDING AUTHOR
MOE Key Laboratory of Geospace Environment and Geodesy, School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Hubei Luojia Laboratory, Wuhan 430079, China
Weiping Jiang
MOE Key Laboratory of Geospace Environment and Geodesy, School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Hubei Luojia Laboratory, Wuhan 430079, China
Related authors
No articles found.
Wenxuan Liu, Ruibo Lei, Taoyong Jin, Heyang Sun, Michel Tsamados, Isolde A. Glissenaar, Jack Christopher Landy, and Yi Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2026-766, https://doi.org/10.5194/egusphere-2026-766, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The thickness and surface shape of Arctic sea ice are difficult to measure accurately, especially in summer when melting occurs. We developed a new method that uses ICESat-2 photon data to map sea ice height throughout the year. By carefully filtering noise and using satellite images to help identify open water, we obtained more detailed and reliable ice heights and freeboards than existing products. The results better capture rough ice features and improve accuracy.
Guodong Chen, Weiping Jiang, Zhijie Zhang, Taoyong Jin, and Dawei Li
EGUsphere, https://doi.org/10.5194/egusphere-2023-3030, https://doi.org/10.5194/egusphere-2023-3030, 2024
Preprint archived
Short summary
Short summary
This paper attempts to determine Arctic mean sea surface and sea level change by combining ICESat-2 and CryoSat-2 data. Our results show that the SSH flag identified in ATL07 may be too strict, resulting in a small number of lead identifications. Combining the two missions can obtain sea surface height with higher accuracy and coverage in the Arctic. The results are helpful for the study of sea level, sea ice, and the accuracy of ICESat-2 and CryoSat-2 data in the Arctic.
Jiasheng Shi, Taoyong Jin, Mao Zhou, Xiangcheng Wan, and Weiping Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2022-1018, https://doi.org/10.5194/egusphere-2022-1018, 2022
Preprint withdrawn
Short summary
Short summary
SWOT has significant potential for detecting mesoscale eddies, but the detecting method, which is used for nadir altimeters, may be not optimal. We propose to improve the method based on the spatial and temporal features of SWOT, to reduce the long-wavelength errors and enhance the high spatial features. The accuracy of gridded results are improved especially when the number of observations is limited. The reconstruction and detected temporal scales of mesoscale eddy variations is also enhanced.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
Short summary
Short summary
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Cited articles
Ayyad, M., Hajj, M. R., and Marsooli, R.: Machine learning-based assessment of storm surge in the New York metropolitan area, Sci. Rep.-UK, 12, 19215, https://doi.org/10.1038/s41598-022-23627-6, 2022.
Bloemendaal, N., Muis, S., Haarsma, R. J., Verlaan, M., Irazoqui Apecechea, M., De Moel, H., Ward, P. J., and Aerts, J. C. J. H.: Global modeling of tropical cyclone storm surges using high-resolution forecasts, Clim. Dynam., 52, 5031–5044, https://doi.org/10.1007/s00382-018-4430-x, 2019.
Bruneau, N., Polton, J., Williams, J., and Holt, J.: Estimation of global coastal sea level extremes using neural networks, Environ. Res. Lett., 15, 074030, https://doi.org/10.1088/1748-9326/ab89d6, 2020.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August 2016, San Francisco, California, USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Cid, A., Camus, P., Castanedo, S., Méndez, F. J., and Medina, R.: Global reconstructed daily surge levels from the 20th Century Reanalysis (1871–2010), Global Planet. Change, 148, 9–21, https://doi.org/10.1016/j.gloplacha.2016.11.006, 2017.
Cid, A., Wahl, T., Chambers, D. P., and Muis, S.: Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century, JGR Oceans, 123, 437–451, https://doi.org/10.1002/2017JC013143, 2018.
Codiga, D. L.: Unified Tidal Analysis and Prediction Using the UTide Matlab Functions, Technical Report No. 2011-01, Graduate School of Oceanography, University of Rhode Island, https://doi.org/10.13140/RG.2.1.3761.2008, 2011.
Copernicus Climate Change Service: ERA5 hourly data on single levels from 1940 to present [data set], https://doi.org/10.24381/cds.adbb2d47, 2018.
Copernicus Climate Change Service: Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections [data set], https://doi.org/10.24381/cds.a6d42d60, 2022.
Dullaart, J. C. M., Muis, S., Bloemendaal, N., Chertova, M. V., Couasnon, A., and Aerts, J. C. J. H.: Accounting for tropical cyclones more than doubles the global population exposed to low-probability coastal flooding, Commun. Earth Environ., 2, 135, https://doi.org/10.1038/s43247-021-00204-9, 2021.
Ebel, P., Victor, B., Naylor, P., Meoni, G., Serva, F., and Schneider, R.: Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 17–18 June 2024, Seattle, WA, USA, 471–480, https://doi.org/10.1109/CVPRW63382.2024.00052, 2024.
Fang, J., Wahl, T., Zhang, Q., Muis, S., Hu, P., Fang, J., Du, S., Dou, T., and Shi, P.: Extreme sea levels along coastal China: uncertainties and implications, Stoch. Env. Res. Risk A., 35, 405–418, https://doi.org/10.1007/s00477-020-01964-0, 2021.
Gahtan, J., Knapp, K. R., Schreck, C. J. I., Diamond, H. J., Kossin, J. P., and Kruk, M. C.: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4.01 [data set], https://doi.org/10.25921/82ty-9e16, 2024.
Graham, R. M., Hudson, S. R., and Maturilli, M.: Improved Performance of ERA5 in Arctic Gateway Relative to Four Global Atmospheric Reanalyses, Geophys. Res. Lett., 46, 6138–6147, https://doi.org/10.1029/2019GL082781, 2019.
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A., Fukimori, I., Gomez, N., Kopp, R. E., Landerer, F., Cozannet, G. L., Ponte, R. M., Stammer, D., Tamisiea, M. E., and Van De Wal, R. S. W.: Concepts and Terminology for Sea Level: Mean, Variability and Change, Both Local and Global, Surv. Geophys., 40, 1251–1289, https://doi.org/10.1007/s10712-019-09525-z, 2019.
Haigh, I. D., Marcos, M., Talke, S. A., Woodworth, P. L., Hunter, J. R., Hague, B. S., Arns, A., Bradshaw, E., and Thompson, P.: GESLA Version 3: A major update to the global higher-frequency sea-level dataset, Geosci. Data J., 10, 293–314, https://doi.org/10.1002/gdj3.174, 2023.
Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R. S. J., Marzeion, B., Fettweis, X., Ionescu, C., and Levermann, A.: Coastal flood damage and adaptation costs under 21st century sea-level rise, P. Natl. Acad. Sci. USA, 111, 3292–3297, https://doi.org/10.1073/pnas.1222469111, 2014.
Horsburgh, K. J. and Wilson, C.: Tide-surge interaction and its role in the distribution of surge residuals in the North Sea, J. Geophys. Res., 112, 2006JC004033, https://doi.org/10.1029/2006JC004033, 2007.
Kernkamp, H. W. J., Stelling, G. S., and de Goede, E. D.: Efficient scheme for the shallow water equations on unstructured grids with application to the Continental Shelf, Ocean Dynam., 61, 1175–1188, https://doi.org/10.1007/s10236-011-0423-6, 2011.
Kirezci, E., Young, I. R., Ranasinghe, R., Muis, S., Nicholls, R. J., Lincke, D., and Hinkel, J.: Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st Century, Sci. Rep.-UK, 10, 11629, https://doi.org/10.1038/s41598-020-67736-6, 2020.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data, B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010.
Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C.-H., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical Cyclones and Climate Change Assessment: Part II: Projected Response to Anthropogenic Warming, B. Am. Meteorol. Soc., 101, E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1, 2020.
Kron, W.: Coasts: the high-risk areas of the world, Nat. Hazards, 66, 1363–1382, https://doi.org/10.1007/s11069-012-0215-4, 2013.
Lee, J.-W., Irish, J. L., Bensi, M. T., and Marcy, D. C.: Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning, Coast. Eng., 170, 104024, https://doi.org/10.1016/j.coastaleng.2021.104024, 2021.
Lockwood, J. W., Lin, N., Oppenheimer, M., and Lai, C.: Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics, J. Geophys. Res.-Atmos., 127, e2022JD037617, https://doi.org/10.1029/2022JD037617, 2022.
Lockwood, J. W., Lin, N., Gori, A., and Oppenheimer, M.: Increasing Flood Hazard Posed by Tropical Cyclone Rapid Intensification in a Changing Climate, Geophys. Res. Lett., 51, e2023GL105624, https://doi.org/10.1029/2023GL105624, 2024.
Marcos, M., Wöppelmann, G., Matthews, A., Ponte, R. M., Birol, F., Ardhuin, F., Coco, G., Santamaría-Gómez, A., Ballu, V., Testut, L., Chambers, D., and Stopa, J. E.: Coastal Sea Level and Related Fields from Existing Observing Systems, Surv. Geophys., 40, 1293–1317, https://doi.org/10.1007/s10712-019-09513-3, 2019.
Mentaschi, L., Vousdoukas, M. I., García-Sánchez, G., Fernández-Montblanc, T., Roland, A., Voukouvalas, E., Federico, I., Abdolali, A., Zhang, Y. J., and Feyen, L.: A global unstructured, coupled, high-resolution hindcast of waves and storm surge, Front. Mar. Sci., 10, 1233679, https://doi.org/10.3389/fmars.2023.1233679, 2023.
Merkens, J.-L., Reimann, L., Hinkel, J., and Vafeidis, A. T.: Gridded population projections for the coastal zone under the Shared Socioeconomic Pathways, Global Planet. Change, 145, 57–66, https://doi.org/10.1016/j.gloplacha.2016.08.009, 2016.
Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H., and Ward, P. J.: A global reanalysis of storm surges and extreme sea levels, Nat. Commun., 7, 11969, https://doi.org/10.1038/ncomms11969, 2016.
Muis, S., Lin, N., Verlaan, M., Winsemius, H. C., Ward, P. J., and Aerts, J. C. J. H.: Spatiotemporal patterns of extreme sea levels along the western North-Atlantic coasts, Sci. Rep.-UK, 9, 3391, https://doi.org/10.1038/s41598-019-40157-w, 2019.
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su, J., Yan, K., and Verlaan, M.: A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections, Front. Mar. Sci., 7, 263, https://doi.org/10.3389/fmars.2020.00263, 2020.
Muis, S., Aerts, J. C. J. H., Á. Antolínez, J. A., Dullaart, J. C., Duong, T. M., Erikson, L., Haarsma, R. J., Apecechea, M. I., Mengel, M., Le Bars, D., O'Neill, A., Ranasinghe, R., Roberts, M. J., Verlaan, M., Ward, P. J., and Yan, K.: Global Projections of Storm Surges Using High-Resolution CMIP6 Climate Models, Earths Future, 11, e2023EF003479, https://doi.org/10.1029/2023EF003479, 2023.
Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T. Y., Weitzner, D., and Matias, Y.: Global prediction of extreme floods in ungauged watersheds, Nature, 627, 559–563, https://doi.org/10.1038/s41586-024-07145-1, 2024.
Nevo, S., Morin, E., Gerzi Rosenthal, A., Metzger, A., Barshai, C., Weitzner, D., Voloshin, D., Kratzert, F., Elidan, G., Dror, G., Begelman, G., Nearing, G., Shalev, G., Noga, H., Shavitt, I., Yuklea, L., Royz, M., Giladi, N., Peled Levi, N., Reich, O., Gilon, O., Maor, R., Timnat, S., Shechter, T., Anisimov, V., Gigi, Y., Levin, Y., Moshe, Z., Ben-Haim, Z., Hassidim, A., and Matias, Y.: Flood forecasting with machine learning models in an operational framework, Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022, 2022.
Palmer, M. D., Domingues, C. M., Slangen, A. B. A., and Boeira Dias, F.: An ensemble approach to quantify global mean sea-level rise over the 20th century from tide gauge reconstructions, Environ. Res. Lett., 16, 044043, https://doi.org/10.1088/1748-9326/abdaec, 2021.
Parker, K., Erikson, L., Thomas, J., Nederhoff, K., Barnard, P., and Muis, S.: Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast, Nat. Hazards, 117, 2219–2248, https://doi.org/10.1007/s11069-023-05939-6, 2023.
Pörtner, H.-O., Roberts, D. C., and Masson-Delmotte, V.: The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157964, 2022.
Resio, D. T. and Westerink, J. J.: Modeling the physics of storm surges, Phys. Today, 61, 33–38, https://doi.org/10.1063/1.2982120, 2008.
Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Villaume, S., Haimberger, L., Woollen, J., Buontempo, C., and Thépaut, J.: The ERA5 global reanalysis from 1940 to 2022, Q. J. Roy. Meteorol. Soc., 150, 4014–4048, https://doi.org/10.1002/qj.4803, 2024.
Tadesse, M., Wahl, T., and Cid, A.: Data-Driven Modeling of Global Storm Surges, Front. Mar. Sci., 7, 260, https://doi.org/10.3389/fmars.2020.00260, 2020.
Tadesse, M. G. and Wahl, T.: A database of global storm surge reconstructions, Sci. Data, 8, 125, https://doi.org/10.1038/s41597-021-00906-x, 2021.
Tiggeloven, T., Couasnon, A., van Straaten, C., Muis, S., and Ward, P. J.: Exploring deep learning capabilities for surge predictions in coastal areas, Sci. Rep.-UK, 11, 17224, https://doi.org/10.1038/s41598-021-96674-0, 2021.
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996.
Woodworth, P. L., Melet, A., Marcos, M., Ray, R. D., Wöppelmann, G., Sasaki, Y. N., Cirano, M., Hibbert, A., Huthnance, J. M., Monserrat, S., and Merrifield, M. A.: Forcing Factors Affecting Sea Level Changes at the Coast, Surv. Geophys., 40, 1351–1397, https://doi.org/10.1007/s10712-019-09531-1, 2019.
Xiong, J., Yu, F., Fu, C., Dong, J., and Liu, Q.: Evaluation and improvement of the ERA5 wind field in typhoon storm surge simulations, Appl. Ocean Res., 118, 103000, https://doi.org/10.1016/j.apor.2021.103000, 2022.
Yang, L., Jin, T., Xiao, M., Gao, X., Jiang, W., and Li, J.: Extreme Events and Probability Analysis Along the United States East Coast Based on High Spatial-Coverage Reconstructed Storm Surges, Geophys. Res. Lett., 50, e2023GL103492, https://doi.org/10.1029/2023GL103492, 2023.
Yang, L., Jin, T., and Jiang, W.: ASM-SS: The First Quasi-Global High Spatial Resolution Coastal Storm Surge Dataset Reconstructed from Tide Gauge Records [data set], https://doi.org/10.5281/zenodo.14034726, 2024a.
Yang, L., Jin, T., and Jiang, W.: Improving Coastal Storm Surge Monitoring Through Joint Modeling Based on Permanent and Temporary Tide Gauges, Geophys. Res. Lett., 51, e2024GL108886, https://doi.org/10.1029/2024GL108886, 2024b.
Short summary
Storm surges (SSs) cause massive loss of life and property in coastal areas each year. High-spatial-resolution and long-term SS records are important for assessing such events. However, tide gauges can provide limited SS information due to sparse and uneven distributions. Based on artificial intelligence technology and tide gauges, a high-spatial-coverage SS dataset was generated for the period from 1940 to 2020, which can provide possible alternative support for deepening our understanding of SSs.
Storm surges (SSs) cause massive loss of life and property in coastal areas each year....
Altmetrics
Final-revised paper
Preprint