Algorithm for detecting cyclone and anticyclone centres from mean sea level pressure layer

References

• Ahrens, C.D. and Henson, R., 2018. Essentials of meteorology: an invitation to the atmosphere. 8th ed. Boston, USA: Cengage Learning.
   Google Scholar
• Blender, R. and Schubert, M., 2000. Cyclone tracking in different spatial and temporal resolutions. Monthly Weather Review, 128 (2), 377–384. doi:10.1175/1520-0493(2000)128<0377:CTIDSA>2.0.CO;2
   Web of Science ®Google Scholar
• Chamberlain, R. and Duquette, W., 2007. Some algorithms for polygons on a sphere. Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration. Available from: http://hdl.handle.net/2014/41271 [Accessed 16 Jun 2022].
   Google Scholar
• Cochran, W.G., 1977. Sampling techniques. 3rd ed. New York: John Wiley & Sons.
   Google Scholar
• Dacre, H.F. and Gray, S.L., 2009. The spatial distribution and evolution characteristics of North Atlantic cyclones. Monthly Weather Review, 137 (1), 99–115. doi:10.1175/2008MWR2491.1
   Web of Science ®Google Scholar
• Douglas, D.H. and Peucker, T.K., 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 10 (2), 112–122. doi:10.3138/FM57-6770-U75U-7727
   Google Scholar
• Flaounas, E., et al. 2014. CycloTRACK (v1.0) – tracking winter extratropical cyclones based on relative vorticity: sensitivity to data filtering and other relevant parameters. Geoscientific Model Development, 7 (4), 1841–1853. Available from: https://gmd.copernicus.org/articles/7/1841/2014/.
   Web of Science ®Google Scholar
• Frame, T., et al. 2017. Meteorological risk: extra-tropical cyclones, tropical cyclones and convective storms. In: K. Poljansek, et al., eds. Science for disaster risk management 2017: knowing better and losing less. Luxembourg: Publications Office of the European Union, 246–256. Available from:https://centaur.reading.ac.uk/88331/ [Accessed 23 Nov 2021].
   Google Scholar
• Geng, Q. and Sugi, M., 2001. Variability of the North Atlantic cyclone activity in winter analyzed from NCEP–NCAR reanalysis data. Journal of Climate, 14 (18), 3863–3873. doi:10.1175/1520-0442(2001)014<3863:VOTNAC>2.0.CO;2
   Web of Science ®Google Scholar
• Goldberg, D.M. and Gott, R.J., 2007. Flexion and skewness in map projections of the earth. Cartographica: The International Journal for Geographic Information and Geovisualization, 42, 297–318. doi:10.3138/carto.42.4.297
   Google Scholar
• Gonzalez, R.C. and Woods, R.E., 2008. Digital image processing. Upper Saddle River, N.J: Prentice Hall.
   Google Scholar
• Hanley, J. and Caballero, R., 2012. Objective identification and tracking of multicentre cyclones in the era-interim reanalysis dataset. Quarterly Journal of the Royal Meteorological Society, 138 (664), 612–625. doi:10.1002/qj.948
   Web of Science ®Google Scholar
• Hersbach, H., et al., 2021. ERA5 hourly data on single levels from 1979 to present. Copernicus climate change service (C3S) climate data store (CDS). December. doi:10.24381/cds.adbb2d47
   Google Scholar
• Hinton, G., Osindero, S., and Teh, Y.W., 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554. doi:10.1162/neco.2006.18.7.1527
   PubMed Web of Science ®Google Scholar
• Hodges, K.I., 1994. A general method for tracking analysis and its application to meteorological data. Monthly Weather Review, 122 (11), 2573–2586. doi:10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2
   Web of Science ®Google Scholar
• Jelenak, Z., Chang, P.S., and Sienkiewicz, J.M., 2012. Wind field distribution within hurricane force extratropical cyclones over North Pacific and Atlantic using quikscat scatterometer measurements. In: 92nd American Meteorological Society annual meeting, 22–26 January 2012 New Orleans, Louisiana.
   Google Scholar
• Jiang, L., Fu, S., and Sun, J., 2020. New method for detecting extratropical cyclones: the eight-section slope detecting method. Atmospheric and Oceanic Science Letters, 13 (5), 436–442. doi:10.1080/16742834.2020.1754124
   Web of Science ®Google Scholar
• Kanopoulos, N., Vasanthavada, N., and Baker, R.L., 1988. Design of an image edge detection filter using the Sobel operator. IEEE Journal of Solid-State Circuits, 23 (2), 358–367. doi:10.1109/4.996
   Web of Science ®Google Scholar
• Kew, S.F., Sprenger, M., and Davies, H.C., 2010. Potential vorticity anomalies of the lowermost stratosphere: a 10-yr winter climatology. Monthly Weather Review, 138 (4), 1234–1249. doi:10.1175/2009MWR3193.1
   Web of Science ®Google Scholar
• König, W., Sausen, R., and Sielmann, F., 1993. Objective identification of cyclones in gcm simulations. Journal of Climate, 6 (12), 2217–2231. doi:10.1175/1520-0442(1993)006<2217:OIOCIG>2.0.CO;2
   Web of Science ®Google Scholar
• Lim, E.P. and Simmonds, I., 2007. Southern hemisphere winter extratropical cyclone characteristics and vertical organization observed with the era-40 data in 1979–2001. Journal of Climate, 20 (11), 2675–2690. doi:10.1175/JCLI4135.1
   Web of Science ®Google Scholar
• Lorensen, W.E. and Cline, H.E., 1987. Marching cubes: a high resolution 3d surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 87, New York, NY, USA. Association for Computing Machinery, 163–169. doi:10.1145/37401.37422
   Google Scholar
• Lu, C., 2017. A modified algorithm for identifying and tracking extratropical cyclones. Advances in Atmospheric Sciences, 34 (7), 909–924. doi:10.1007/s00376-017-6231-2
   Web of Science ®Google Scholar
• Lu, C., Kong, Y., and Guan, Z., 2020. A mask R-CNN model for reidentifying extratropical cyclones based on quasi-supervised thought. Scientific Reports, 10 (1), 15011. doi:10.1038/s41598-020-71831-z
   PubMed Web of Science ®Google Scholar
• Matsuoka, D., et al., 2018. Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model. Progress in Earth and Planetary Science, 5 (1), 80. doi:10.1186/s40645-018-0245-y
   Google Scholar
• MetOffice, 2021. Met office pressure systems. December. Available from: https://www.metoffice.gov.uk/weather/maps-and-charts/surface-pressure [Accessed 25 June 2021].
   Google Scholar
• Müller, G. and Floors, R., 2021. Wetterzentrale pressure systems archive. December. Available from: https://www.wetterzentrale.de/de/reanalysis.php?map=1model=bravar=45 [Accessed 25 June 2021].
   Google Scholar
• Murray, R.J. and Simmonds, I., 1991. A numerical scheme for tracking cyclone centres from digital data. Australian Meteorological Magazine, 39 (3), 155–166.
   Google Scholar
• Neu, U., et al. 2013. Imilast: a community effort to intercompare extratropical cyclone detection and tracking algorithms. Bulletin of the American Meteorological Society, 94 (4), 529–547. Available from: https://journals.ametsoc.org/view/journals/bams/94/4/bams-d-11-00154.1.xml .
   Web of Science ®Google Scholar
• NWS Weather Prediction, 2022. Coded surface frontal positions. electronic. Available form: https://www.wpc.ncep.noaa.gov/ [Accessed 13 February 2022].
   Google Scholar
• Pharr, M., Jakob, W., and Humphreys, G., 2016. Physically based rendering: from theory to implementation. 3rd ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. Chapter 4.3 Bounding Volume Hierarchies.
   Google Scholar
• Raible, C.C., et al., 2008. Northern hemisphere extratropical cyclones: a comparison of detection and tracking methods and different reanalyses. Monthly Weather Review, 136 (3), 880–897. doi:10.1175/2007MWR2143.1
   Web of Science ®Google Scholar
• Rudeva, I., 2008. On the relation of the number of extratropical cyclones to their sizes. Izvestiya, Atmospheric and Oceanic Physics, 44 (3), 273–278. doi:10.1134/S000143380803002X
   Web of Science ®Google Scholar
• Rudeva, I. and Gulev, S.K., 2007. Climatology of cyclone size characteristics and their changes during the cyclone life cycle. Monthly Weather Review, 135 (7), 2568–2587. doi:10.1175/MWR3420.1
   Web of Science ®Google Scholar
• Serreze, M.C., et al., 1997. Icelandic low cyclone activity: climatological features, linkages with the nao, and relationships with recent changes in the northern hemisphere circulation. Journal of Climate, 10 (3), 453–464. doi:10.1175/1520-0442(1997)010<0453:ILCACF>2.0.CO;2
   Web of Science ®Google Scholar
• Serreze, M.C., 2009. Northern hemisphere cyclone locations and characteristics from NCEP/NCAR reanalysis data, version 1. electronic. Available form: https://nsidc.org/data/nsidc-0423 [Accessed 20 January 2022].
   Google Scholar
• Simmonds, I., Burke, C., and Keay, K., 2008. Arctic climate change as manifest in cyclone behavior. Journal of Climate, 21 (22), 5777–5796. doi:10.1175/2008JCLI2366.1
   Web of Science ®Google Scholar
• Simmonds, I. and Keay, K., 2000. Variability of southern hemisphere extratropical cyclone behavior, 1958–97. Journal of Climate, 13 (3), 550–561. doi:10.1175/1520-0442(2000)013<0550:VOSHEC>2.0.CO;2
   Web of Science ®Google Scholar
• Ullrich, P.A. and Zarzycki, C.M., 2017. Tempestextremes: a framework for scale-insensitive pointwise feature tracking on unstructured grids. Geoscientific Model Development, 10 (3), 1069–1090. Available from: https://gmd.copernicus.org/articles/10/1069/2017/
   Web of Science ®Google Scholar
• Wang, P., et al. 2020. A center location algorithm for tropical cyclone in satellite infrared images. IEEE Journal of Selected Topics in Applied, 13, 2161–2172.
   Web of Science ®Google Scholar
• Wernli, H. and Schwierz, C., 2006. Surface cyclones in the era-40 dataset (1958–2001). part I: novel identification method and global climatology. Journal of the Atmospheric Sciences, 63 (10), 2486–2507. Available from: https://journals.ametsoc.org/view/journals/atsc/63/10/jas3766.1.xml
   Web of Science ®Google Scholar
• WikiProject Tropical cyclones, 2021. WikiProject tropical cyclones. December. Available from: https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Tropical_cyclones/Tracks
   Google Scholar