Extreme values of wind speed were studied based on the highly detailed ERA5 dataset covering the central part of the Kara Sea. Cases in which the ice coverage of the cells exceeded 15% were filtered. Our study shows that the wind speed extrema obtained from station observations, as well as from modelling results in the framework of mesoscale models, can be divided into two groups according to their probability distribution laws. One group is specifically designated as black swans, with the other referred to as dragons (or dragon-kings). In this study we determined that the data of ERA5 accurately described the swans, but did not fully reproduce extrema related to the dragons; these extrema were identified only in half of ERA5 grid points. Weibull probability distribution function (PDF) parameters were identified in only a quarter of the pixels. The parameters were connected almost deterministically. This converted the Weibull function into a one-parameter dependence. It was not clear whether this uniqueness was a consequence of the features of the calculation algorithm used in ERA5, or whether it was a consequence of a relatively small area being considered, which had the same wind regime. Extremes of wind speed arise as mesoscale features and are associated with hydrodynamic features of the wind flow. If the flow was non-geostrophic and if its trajectory had a substantial curvature, then the extreme velocities were distributed according to a rule similar to the Weibull law.
KeywordsERA5Kara SeaWeibull Probability Distribution Function Wind SpeedHydrodynamics and Statistics of Extreme Events
Kislov, A. and Matveeva, T. (2020) The Monsoon over the Barents Sea and Kara Sea. Atmospheric and Climate Sciences, 10, 339-356. https://doi.org/10.4236/acs.2020.103019
Hundecha, Y., St-Hilaire, A., Ouarda, T.B.M.J., El Adlouni, S. and Gachon, P. (2008) A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada. Journal of Applied Meteorology and Climatology, 47, 2745-2759.
Wan, H., Wang, X.L. and Swail, V.R. (2010) Homogenization and Trend Analysis of Canadian Near-Surface Wind Speeds. Journal of Climate, 23, 1209-1225. https://doi.org/10.1175/2009JCLI3200.1
Kislov, A. and Matveeva, T. (2016) An Extreme Value Analysis of Wind Speed over the European and Siberian Parts of Arctic Region. Atmospheric and Climate Sciences, 6, 205-223. http://dx.doi.org/10.4236/acs.2016.62018
Kislov, A. and Platonov, V. (2019) Analysis of Observed and Modelled Near-Surface Wind Extremes over the Sub-Arctic Northeast Pacific. Atmospheric and Climate Sciences, 9, 146-158. https://doi.org/10.4236/acs.2019.91010
Zhang, H.M., Bates, J.J., Reynolds, R.W. (2006) Assessment of Composite Global Sampling: Sea Surface Wind Speed. Geophysical Research Letters, 33, Article ID: L17714. https://doi.org/10.1029/2006GL027086
Sampe, T. and Xie, S.-P. (2007) Mapping High Sea Winds from Space: A Global Climatology. Bulletin of the American Meteorological Society, 88, 1965-1978. https://doi.org/10.1175/BAMS-88-12-1965
Young, I.R., Zieger, S. and Babanin, A.V. (2011) Global Trends in Wind Speed and Wave Height. Science, 332, 451-455. https://doi.org/10.1126/science.1197219
Zieger, S., Babanin, A.V. and Young, I.R. (2014) Changes in Ocean Surface Wind with a Focus on Trends in Regional and Monthly Mean Values. Deep Sea Research Part I: Oceanographic Research Papers, 86, 56-67. https://doi.org/10.1016/j.dsr.2014.01.004
Hullinger, W.J. and Long, D.G. (2014) Mitigation of Sea Ice Contamination in QuikSCAT Wind Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 52, 2149-2158. https://doi.org/10.1109/TGRS.2013.2258400
Monahan, A.H. (2006a) The Probability Distribution of Sea Surface Wind Speeds. Part I: Theory and Sea Winds Observations. Journal of Climate, 19, 497-520. https://doi.org/10.1175/JCLI3640.1
Monahan, A.H. (2006b) The Probability Distribution of Sea Surface Wind Speeds. Part II: Dataset Intercomparison and Seasonal Variability. Journal of Climate, 19, 521-534. https://doi.org/10.1175/JCLI3641.1
Stopa, J.E., Cheung, K.F., Tolman, H.L. and Chawla A. (2013) Patterns and Cycles in the Climate Forecast System Reanalysis Wind and Wave Data. Ocean Modelling, 70, 207-220. https://doi.org/10.1016/j.ocemod.2012.10.005
Hughes, M. and Cassano, J.J. (2015) The Climatological Distribution of Extreme Arctic Winds and Implications for Ocean and Sea Ice Processes. Journal of Geophysical Research: Atmospheres, 120, 7358-7377. https://doi.org/10.1002/2015JD023189
Surkova, G. and Krylov, A. (2019) Extremely Strong Winds and Weather Patterns over Arctic Seas. Geography, Environment, Sustainability, 12, 34-42. https://doi.org/10.24057/2071-9388-2019-22
Stegall, S.T. and Zhang, J. (2012) Wind Field Climatology, Changes, and Extremes in the Chukchi-Beaufort Seas and Alaska North Slope during 1979-2009. Journal of Climate, 25, 8075-8089. https://doi.org/10.1175/JCLI-D-11-00532.1
Redilla, K., Pearl, S.T., Bieniek, P.A. and Walsh, J.E. (2019) Wind Climatology for Alaska: Historical and Future. Atmospheric and Climate Sciences, 9, 683-702. https://doi.org/10.4236/acs.2019.94042
Palutikof, J.P., Brabson, B.B., Lister, D.H. and Adcock, S.T. (1999) A Review of Methods to Calculate Extreme Wind Speeds. Meteorological Applications, 6, 119-132. https://doi.org/10.1017/S1350482799001103
Beirlant, J., Goegebeur, Y., Segers, J., Jozef Teugels, J., De Waal, D. and Ferro, C. (2004) Statistics of Extremes: Theory and Applications. Willey Series in Probability and Statistics, John Wiley & Sons Ltd., Chichester. https://doi.org/10.1002/0470012382
Coles, S. (2001) An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics. Springer-Verlag, London. https://doi.org/10.1007/978-1-4471-3675-0
Taleb, N.N. (2010) The Black Swan: The Impact of the Highly Improbable. 2nd Edition, Penguin, New York.
Sornette, D. (2009) Dragon-Kings, Black Swans and the Prediction of Crises. Research Paper No. 09-36, Swiss Finance Institute, Zürich. http://dx.doi.org/10.2139/ssrn.1470006
Sornette, D. and Ouillon, G. (2012) Dragon-Kings: Mechanisms, Statistical Methods and Empirical Evidence. The European Physical Journal Special Topics, 205, 1-26. https://doi.org/10.1140/epjst/e2012-01559-5
Wheatley, S. and Sornette, D. (2015) Multiple Outlier Detection in Samples with Exponential & Pareto Tails: Redeeming the Inward Approach & Detecting Dragon Kings. Research Paper No. 15-28, Swiss Finance Institute, Zürich. https://doi.org/10.2139/ssrn.2645709
Kislov, A., Rivin, G., Platonov, V., Varentsov, M.I., Rozinkina, I.A., Nikitin, M.A. and Chumakov, M.M. (2018) Mesoscale Atmospheric Modelling of Extreme Velocities over the Sea of Okhotsk and Sakhalin. Izvestiya, Atmospheric and Oceanic Physics, 54, 322-326. https://doi.org/10.1134/S0001433818040242
Hersbach, H. and Dee, D. (2016) ERA-5 Reanalysis Is in Production. ECMWF Newsletter, No. 147.
Hersbach, H., Bell, B., Berrisford, P., Horányi, A., Sabater, J.M., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C. and Dee, D. (2019) Global Reanalysis: Goodbye ERA-Interim, Hello ERA5. ECMWF Newsletter, 159, 17-24.
Ramon, J., Lledó, L., Torralba, V., Soret, A. and Doblas-Reyes, F.J. (2019) What Global Reanalysis Best Represents Near-Surface Winds? Quarterly Journal of the Royal Meteorological Society, 145, 3236-3251. https://doi.org/10.1002/qj.3616
Rivas, M.B. and Stoffelen, A. (2019) Characterizing ERA-Interim and ERA5 Surface Wind Biases Using ASCAT. Ocean Science, 15, 831-852. https://doi.org/10.5194/os-15-831-2019
Meissner, T., Smith, D. and Wentz F. (2001) A 10 Year Intercomparison between Collocated Special Sensor Microwave Imager Oceanic Surface Wind Speed Retrievals and Global Analyses. Journal of Geophysical Research, 106, 11731-11742. https://doi.org/10.1029/1999JC000098
Cakmur, R., Miller, R. and Torres, O. (2004) Incorporating the Effect of Small-Scale Circulations upon Dust Emission in an Atmospheric General Circulation Model. Journal of Geophysical Research, 109, Article ID: D07201. https://doi.org/10.1029/2003JD004067
Coles, S.G. and Walshaw, D. (1994) Directional Modelling of Extreme Wind Speeds. Journal of the Royal Statistical Society. Series C (Applied Statistics), 43, 139-157. https://doi.org/10.2307/2986118
Gusella, V. (1991) Estimation of Extreme Winds from Short-Term Records. Journal Struct. Engineering, 117, 375-390. https://doi.org/10.1061/(ASCE)0733-9445(1991)117:2(375)
Mai, S., Wamser, C. and Kottmeier, C. (1996) Geometric and Aerodynamic Roughness of Sea Ice. Boundary-Layer Meteorology, 77, 233-248. https://doi.org/10.1007/BF00123526
Fisher, R.A. and Tippett, L.H.C. (1928) Limiting Forms of the Frequency Distribution of the Largest or Smallest Members of a Sample. Mathematical Proceedings of the Cambridge Philosophical Society, 24, 180-190. https://doi.org/10.1017/S0305004100015681
Gnedenko, B. (1943) Sur la Distribution limite du Terme Maximum D’unesériealéatoire. Annals of Mathematics, 44, 423-453. (In French) https://doi.org/10.2307/1968974
Holton, J.R. and Hakim, G.J. (2013) An Introduction to Dynamic Meteorology. 5th Edition, Academic Press, Cambridge, 552 p. https://doi.org/10.1016/C2009-0-63394-8
Brasseur, O. (2001) Development and Application of a Physical Approach to Estimating Wind Gusts. Monthly Weather Review, 129, 5-25. https://doi.org/10.1175/1520-0493(2001)129 2.0.CO;2
Schulz, J.-P. and Heise, E. (2003) A New Scheme for Diagnosting Near-Surface Convective Gusts. COSMO Newsletter, 3, 221-225.