Articles | Volume 19, issue 12
https://doi.org/10.5194/gmd-19-5641-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-5641-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ImpactETC1.0: impact-oriented tracking of extratropical cyclones with global optimisation and track reconciliation
Danish Meteorological Institute, Sankt Kjelds Plads 11, Copenhagen, 2100, Denmark
Danish Meteorological Institute, Sankt Kjelds Plads 11, Copenhagen, 2100, Denmark
Jonas Wied Pedersen
Danish Meteorological Institute, Sankt Kjelds Plads 11, Copenhagen, 2100, Denmark
DTU Sustain, Technical University of Denmark, Bygningstorvet, Building 115, Kgs. Lyngby, 2800, Denmark
Ida Margrethe Ringgaard
Danish Meteorological Institute, Sankt Kjelds Plads 11, Copenhagen, 2100, Denmark
Morten Andreas Dahl Larsen
Danish Meteorological Institute, Sankt Kjelds Plads 11, Copenhagen, 2100, Denmark
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Cited articles
Agertoft, N., Su, J., Pedersen, J. W., Ringgaard, I. M., and Larsen, M. A. D.: ImpactETC1.0 release, Zenodo [code], https://doi.org/10.5281/zenodo.20309449, 2026. a, b, c
Andrée, E., Su, J., Larsen, M. A. D., Drews, M., Stendel, M., and Skovgaard Madsen, K.: The role of preconditioning for extreme storm surges in the western Baltic Sea, Nat. Hazards Earth Syst. Sci., 23, 1817–1834, https://doi.org/10.5194/nhess-23-1817-2023, 2023. a
Andrée, E., Su, J., Larsen, M. A. D., Madsen, K. S., and Drews, M.: Simulating major storm surge events in a complex coastal region, Ocean Model., 162, 101 802, https://doi.org/10.1016/j.ocemod.2021.101802, 2021. a
Andrée, E., Drews, M., Su, J., Larsen, M. A. D., Drønen, N., and Madsen, K. S.: Simulating wind-driven extreme sea levels: Sensitivity to wind speed and direction, Weather and Climate Extremes, 36, 100422, https://doi.org/10.1016/j.wace.2022.100422, 2022. a
Bengtsson, L., Hodges, K. I., and Keenlyside, N.: Will Extratropical Storms Intensify in a Warmer Climate?, J. Climate, 22, 2276–2301, https://doi.org/10.1175/2008JCLI2678.1, 2009. a
Catto, J. L.: Extratropical cyclone classification and its use in climate studies, Rev. Geophys., 54, 486–520, https://doi.org/10.1002/2016RG000519, 2016. a
Feser, F., Barcikowska, M., Krueger, O., Schenk, F., Weisse, R., and Xia, L.: Storminess over the North Atlantic and northwestern Europe – A review, Q. J. Roy. Meteor. Soc., 141, 350–382, https://doi.org/10.1002/qj.2364, 2015. a, b
Flaounas, E., Kotroni, V., Lagouvardos, K., and Flaounas, I.: CycloTRACK (v1.0) – tracking winter extratropical cyclones based on relative vorticity: sensitivity to data filtering and other relevant parameters, Geosci. Model Dev., 7, 1841–1853, https://doi.org/10.5194/gmd-7-1841-2014, 2014. a, b, c, d
Flaounas, E., Aragão, L., Bernini, L., Dafis, S., Doiteau, B., Flocas, H., Gray, S. L., Karwat, A., Kouroutzoglou, J., Lionello, P., Miglietta, M. M., Pantillon, F., Pasquero, C., Patlakas, P., Picornell, M. Á., Porcù, F., Priestley, M. D. K., Reale, M., Roberts, M. J., Saaroni, H., Sandler, D., Scoccimarro, E., Sprenger, M., and Ziv, B.: A composite approach to produce reference datasets for extratropical cyclone tracks: application to Mediterranean cyclones, Weather Clim. Dynam., 4, 639–661, https://doi.org/10.5194/wcd-4-639-2023, 2023. a
Froude, L. S. R.: TIGGE: Comparison of the Prediction of Northern Hemisphere Extratropical Cyclones by Different Ensemble Prediction Systems, Weather Forecast., 25, 819–836, https://doi.org/10.1175/2010WAF2222326.1, 2010. a
Gonçalves, A., Liberato, M. L. R., and Nieto, R.: Wind Energy Assessment during High-Impact Winter Storms in Southwestern Europe, Atmosphere, 12, https://doi.org/10.3390/atmos12040509, 2021. a
Gramcianinov, C., Campos, R., de Camargo, R., Hodges, K., Guedes Soares, C., and da Silva Dias, P.: Analysis of Atlantic extratropical storm tracks characteristics in 41 years of ERA5 and CFSR/CFSv2 databases, Ocean Eng., 216, 108111, https://doi.org/10.1016/j.oceaneng.2020.108111, 2020. a
Grieger, J., Leckebusch, G. C., Raible, C. C., Rudeva, I., and and, I. S.: Subantarctic cyclones identified by 14 tracking methods, and their role for moisture transports into the continent, Tellus A, 70, 1–18, https://doi.org/10.1080/16000870.2018.1454808, 2018. a
Hodges, K. I.: Feature Tracking on the Unit Sphere, Mon. Weather Rev., 123, 3458–3465, https://doi.org/10.1175/1520-0493(1995)123<3458:FTOTUS>2.0.CO;2, 1995. a
Hodges, K. I., Hoskins, B. J., Boyle, J., and Thorncroft, C.: A Comparison of Recent Reanalysis Datasets Using Objective Feature Tracking: Storm Tracks and Tropical Easterly Waves, Mon. Weather Rev., 131, 2012–2037, https://doi.org/10.1175/1520-0493(2003)131<2012:ACORRD>2.0.CO;2, 2003. a
Hofstätter, M., Chimani, B., Lexer, A., and Blöschl, G.: A new classification scheme of European cyclone tracks with relevance to precipitation, Water Resour. Res., 52, 7086–7104, https://doi.org/10.1002/2016WR019146, 2016. a, b, c
Hoskins, B. J. and Hodges, K. I.: The annual cycle of Northern Hemisphere storm tracks. Part I: Seasons, J. Climate, 32, 1743–1760, https://doi.org/10.1175/JCLI-D-17-0870.s1, 2019. a, b
Hunter, A., Stephenson, D. B., Economou, T., Holland, M., and Cook, I.: New perspectives on the collective risk of extratropical cyclones, Q. J. Roy. Meteor. Soc., 142, 243–256, https://doi.org/10.1002/qj.2649, 2016. a
Kuhn, H. W.: The Hungarian method for the assignment problem, Nav. Res. Logist. Q., 2, 83–97, https://doi.org/10.1002/nav.3800020109, 1955. a
Lakkis, S. G., Canziani, P., Yuchechen, A., Rocamora, L., Caferri, A., Hodges, K., and O'Neill, A.: A 4D feature-tracking algorithm: A multidimensional view of cyclone systems, Q. J. Roy. Meteor. Soc., 145, 395–417, https://doi.org/10.1002/qj.3436, 2019. a, b
Lodise, J., Merrifield, S., Collins, C., Rogowski, P., Behrens, J., and Terrill, E.: Global climatology of extratropical cyclones from a new tracking approach and associated wave heights from satellite radar altimeter, J. Geophys. Res.-Oceans, 127, e2022JC018925, https://doi.org/10.1029/2022JC018925, 2022. a, b, c, d, e
Medina, S. and Houze Jr., R. A.: Kelvin–Helmholtz waves in extratropical cyclones passing over mountain ranges, Q. J. Roy. Meteor. Soc., 142, 1311–1319, https://doi.org/10.1002/qj.2734, 2016. a
Munkres, J.: Algorithms for the Assignment and Transportation Problems, J. Soc. Ind. Appl. Math., 5, 32–38, https://doi.org/10.1137/0105003, 1957. a
Neu, U., Akperov, M. G., Bellenbaum, N., Benestad, R., Blender, R., Caballero, R., Cocozza, A., Dacre, H. F., Feng, Y., Fraedrich, K., Grieger, J., Gulev, S., Hanley, J., Hewson, T., Inatsu, M., Keay, K., Kew, S. F., Kindem, I., Leckebusch, G. C., Liberato, M. L. R., Lionello, P., Mokhov, I. I., Pinto, J. G., Raible, C. C., Reale, M., Rudeva, I., Schuster, M., Simmonds, I., Sinclair, M., Sprenger, M., Tilinina, N. D., Trigo, I. F., Ulbrich, S., Ulbrich, U., Wang, X. L., and Wernli, H.: IMILAST: A Community Effort to Intercompare Extratropical Cyclone Detection and Tracking Algorithms, B. Am. Meteorol. Soc., 94, 529–547, https://doi.org/10.1175/BAMS-D-11-00154.1, 2013. a, b, c, d
Pauley, P. M.: An Example of Uncertainty in Sea Level Pressure Reduction, Weather Forecast., 13, 833–850, https://doi.org/10.1175/1520-0434(1998)013<0833:AEOUIS>2.0.CO;2, 1998. a
Priestley, M. D., Ackerley, D., Catto, J. L., Hodges, K. I., McDonald, R. E., and Lee, R. W.: An overview of the extratropical storm tracks in CMIP6 historical simulations, J. Climate, 33, 6315–6343, https://doi.org/10.1175/JCLI-D-19-0928.1, 2020. a, b
Ragone, F., Mariotti, M., Parodi, A., von Hardenberg, J., and Pasquero, C.: A climatological study of Western Mediterranean Medicanes in numerical simulations with explicit and parameterized convection, Atmosphere, 397, https://doi.org/10.3390/atmos9100397, 2018. a, b, c
Raible, C. C., Della-Marta, P. M., Schwierz, C., Wernli, H., and Blender, R.: Northern Hemisphere Extratropical Cyclones: A Comparison of Detection and Tracking Methods and Different Reanalyses, Mon. Weather Rev., 136, 880–897, https://doi.org/10.1175/2007MWR2143.1, 2008. a, b
Ridal, M., Bazile, E., Le Moigne, P., Randriamampianina, R., Schimanke, S., Andrae, U., Berggren, L., Brousseau, P., Dahlgren, P., Edvinsson, L., El-Said, A., Glinton, M., Hagelin, S., Hopsch, S., Isaksson, L., Medeiros, P., Olsson, E., Unden, P., and Wang, Z. Q.: CERRA, the Copernicus European Regional Reanalysis system, Q. J. Roy. Meteor. Soc., 150, 3385–3411, https://doi.org/10.1002/qj.4764, 2024. a, b
Sanchez-Gomez, E. and Somot, S.: Impact of the internal variability on the cyclone tracks simulated by a regional climate model over the Med-CORDEX domain, Clim. Dynam., 51, 1005–1021, https://doi.org/10.1007/s00382-016-3394-y, 2018. a, b, c, d
Schimanke, S., Ridal, M., Le Moigne, P., Berggren, L., Undén, P., Randriamampianina, R., Andrea, U., Bazile, E., Bertelsen, A., Brousseau, P., Dahlgren, P., Edvinsson, L., El Said, A., Glinton, M., Hopsch, S., Isaksson, L., Mladek, R., Olsson, E., Verrelle, A., and Wang, Z.: CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.622a565a, 2021. a
Seda, M.: The Assignment Problem and Its Relation to Logistics Problems, Algorithms, 15, https://doi.org/10.3390/a15100377, 2022. a
Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021. a, b, c, d, e, f, g
Zappa, G., Shaffrey, L. C., Hodges, K. I., Sansom, P. G., and Stephenson, D. B.: A Multimodel Assessment of Future Projections of North Atlantic and European Extratropical Cyclones in the CMIP5 Climate Models, J. Climate, 26, 5846–5862, https://doi.org/10.1175/JCLI-D-12-00573.1, 2013. a
Zhang, S., Xue, Y., Zhang, H., Zhou, X., Li, K., and Liu, R.: Improved Hungarian algorithm–based task scheduling optimization strategy for remote sensing big data processing, Geo-Spatial Information Science, 27, 1141–1154, https://doi.org/10.1080/10095020.2023.2178339, 2024. a
Short summary
Extratropical cyclones (ETCs) drive severe weather and cause significant socio-economic impacts. We present ImpactETC1.0, a framework that identifies ETC tracks and links them to local impacts, here storm surges. It uses global optimisation, BLOB analysis, and several post-processing options to improve tracking quality and identify impact-relevant tracks. Results show ImpactETC1.0 enables efficient, impact-focused ETC tracking.
Extratropical cyclones (ETCs) drive severe weather and cause significant socio-economic impacts....