Global Wind Speed and Wave Height Extremes Derived from Long-Duration Satellite Records

Alicia Takbash Department of Infrastructure Engineering, University of Melbourne, Parkville, Victoria, Australia

Search for other papers by Alicia Takbash in
Current site
Google Scholar
PubMed
Close
,
Ian R. Young Department of Infrastructure Engineering, University of Melbourne, Parkville, Victoria, Australia

Search for other papers by Ian R. Young in
Current site
Google Scholar
PubMed
Close
, and
Øyvind Breivik Norwegian Meteorological Institute, and University of Bergen, Bergen, Norway

Search for other papers by Øyvind Breivik in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The application of extreme-value analysis to long-duration (30 year) global altimeter and radiometer datasets is considered. In contrast to previous extreme-value analyses of satellite data, the dataset is sufficiently long to enable a peaks over threshold analysis to be undertaken. When applied to altimeter data for wind speed and significant wave height, this analysis produces values consistent with buoy validation data and previous numerical model reanalysis datasets. The spatial distributions produced are also consistent with the model reanalysis data. However, the altimeter data shows much greater finescale structure for wind speed, which is consistent with known tropical cyclone activity. The greater data density provided by radiometer measurements offers the potential to address altimeter undersampling. However, issues associated with the radiometer’s inability to measure wind speed in heavy rain events appears to create an unacceptable “fair weather” bias at extreme wind speeds. This renders the radiometer data of wind speed largely unusable for the investigation of wind speed extremes. The study also clearly demonstrates the limitations of the initial distribution method for extreme-value analysis, which is heavily biased by mean conditions.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ian R. Young, ian.young@unimelb.edu.au

Abstract

The application of extreme-value analysis to long-duration (30 year) global altimeter and radiometer datasets is considered. In contrast to previous extreme-value analyses of satellite data, the dataset is sufficiently long to enable a peaks over threshold analysis to be undertaken. When applied to altimeter data for wind speed and significant wave height, this analysis produces values consistent with buoy validation data and previous numerical model reanalysis datasets. The spatial distributions produced are also consistent with the model reanalysis data. However, the altimeter data shows much greater finescale structure for wind speed, which is consistent with known tropical cyclone activity. The greater data density provided by radiometer measurements offers the potential to address altimeter undersampling. However, issues associated with the radiometer’s inability to measure wind speed in heavy rain events appears to create an unacceptable “fair weather” bias at extreme wind speeds. This renders the radiometer data of wind speed largely unusable for the investigation of wind speed extremes. The study also clearly demonstrates the limitations of the initial distribution method for extreme-value analysis, which is heavily biased by mean conditions.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ian R. Young, ian.young@unimelb.edu.au
Save
  • Aarnes, O. J., Ø. Breivik, and M. Reistad, 2012: Wave extremes in the northeast Atlantic. J. Climate, 25, 15291543, https://doi.org/10.1175/JCLI-D-11-00132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aarnes, O. J., S. Abdalla, J.-R. Bidlot, and Ø. Breivik, 2015: Marine wind and wave height trends at different ERA-Interim forecast ranges. J. Climate, 28, 819837, https://doi.org/10.1175/JCLI-D-14-00470.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abdalla, S., 2007: Ku-band radar altimeter surface wind speed algorithm. Proc. Envisat Symp. 2007, Montreux, Switzerland, European Centre for Medium-Range Weather Forecasts, 463250, https://earth.esa.int/envisatsymposium/proceedings/sessions/3E4/463250sa.pdf.

  • Alves, J. H. G. M., and I. R. Young, 2003: On estimating extreme wave heights using combined Geosat, Topex/Poseidon and ERS-1 altimeter data. Appl. Ocean Res., 25, 167186, https://doi.org/10.1016/j.apor.2004.01.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, C. W., D. J. T. Carter, and P. D. Cotton, 2001: Wave climate variability and impact on offshore structure design extremes. Shell International Rep., 88 pp.

  • Bender, L. C., III, N. Guinasso Jr., J. N. Walpert, and S. D. Howden, 2010: A comparison of methods for determining significant wave heights—Applied to a 3-m discus buoy during Hurricane Katrina. J. Atmos. Oceanic Technol., 27, 10121028, https://doi.org/10.1175/2010JTECHO724.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Breivik, Ø., O. J. Aarnes, J.-R. Bidlot, A. Carrasco, and Ø. Saetra, 2013: Wave extremes in the northeast Atlantic from ensemble forecasts. J. Climate, 26, 75257540, https://doi.org/10.1175/JCLI-D-12-00738.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Breivik, Ø., O. J. Aarnes, S. Abdalla, J.-R. Bidlot, and P. A. E. M. Janssen, 2014: Wind and wave extremes over the world oceans from very large ensembles. Geophys. Res. Lett., 41, 51225131, https://doi.org/10.1002/2014GL060997.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caires, S., and A. Sterl, 2005: 100-year return value estimates for ocean wind speed and significant wave height from the ERA-40 data. J. Climate, 18, 10321048, https://doi.org/10.1175/JCLI-3312.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Castillo, E., 1988: Extreme Value Theory in Engineering. Academic Press, 389 pp.

  • Challenor, P. G., W. Wimmer, and I. Ashton, 2005: Climate change and extreme wave heights in the North Atlantic. Proc. 2004 Envisat and ERS Symp., Salzburg, Austria, European Space Agency, SP-572.

  • Chen, G., S.-W. Bi, and R. Ezraty, 2004: Global structure of extreme wind and wave climate derived from TOPEX altimeter data. Int. J. Remote Sens., 25, 10051018, https://doi.org/10.1080/01431160310001598980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coles, S., 2001: An Introduction to Statistical Modelling of Extreme Value Theory. Springer, 209 pp.

    • Crossref
    • Export Citation
  • Cooper, C. K., and G. Z. Forristall, 1997: The use of satellite data to estimate extreme wave climate. J. Atmos. Oceanic Technol., 14, 254266, https://doi.org/10.1175/1520-0426(1997)014<0254:TUOSAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, D., C. L. Conrad, and F. M. Paul, 2003: Handbook of automated data quality control checks and procedures of the National Data Buoy Center. NOAA National Data Buoy Center Tech. Doc. 03-02, 44 pp.

  • Ferreira, J. A., and C. G. Soares, 1998: An application of the peaks over threshold method to predict extremes of significant wave height. J. Offshore Mech. Arct. Eng., 120, 165176, https://doi.org/10.1115/1.2829537.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goda, Y., 1988: On the methodology of selecting design wave height. Proc. 21st Int. Conf. on Coastal Eng., Malaga, Spain, American Society of Civil Engineers, 899–913.

    • Crossref
    • Export Citation
  • Goda, Y., 1992: Uncertainty in design parameter from the viewpoint of statistical variability. J. Offshore Mech. Arct. Eng., 114, 7682, https://doi.org/10.1115/1.2919962.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hasselmann, S., and Coauthors, 1988: The WAM Model—A third generation ocean wave prediction model. J. Phys. Oceanogr., 18, 17751810, https://doi.org/10.1175/1520-0485(1988)018<1775:TWMTGO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hemer, M. A., Y. Fan, N. Mori, A. Semedo, and X. L. Wang, 2013: Projected changes in wave climate from a multi-model ensemble. Nat. Climate Change, 3, 471476, https://doi.org/10.1038/nclimate1791.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Howden, S., D. Gilhousen, N. Guinasso, J. Walpert, M. Sturgeon, and L. Bender, 2008: Hurricane Katrina winds measured by a buoy-mounted sonic anemometer. J. Atmos. Oceanic Technol., 25, 607616, https://doi.org/10.1175/2007JTECHO518.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, R. E., V. R. Swail, R. H. Bouchard, R. E. Riley, T. J. Hesser, M. Blaseckie, and C. MacIsaac, 2015: Field Laboratory for Ocean Sea State Investigation and Experimentation: FLOSSIE: Intra-measurement evaluation of 6N wave buoy systems. 14th Int. Workshop on Wave Hindcasting and Forecasting and Fifth Coastal Hazard Symp., Key West, FL, WMO/IOC JCOMM, A1, http://www.waveworkshop.org/14thWaves/Papers/WW14%20FLOSSIE%20Jensen%20et%20al.pdf.

  • Knaff, J. A., S. P. Longmore, and D. A. Molenar, 2014: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455476, https://doi.org/10.1175/JCLI-D-13-00096.1; Corrigendum, 28, 8648–8651, https://doi.org/10.1175/JCLI-D-15-0610.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91, 363376, https://doi.org/10.1175/2009BAMS2755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Large, W. G., J. Morzel, and G. B. Crawford, 1995: Accounting for surface wave distortion of the marine wind profile in low-level ocean storms wind measurements. J. Phys. Oceanogr., 25, 29592971, https://doi.org/10.1175/1520-0485(1995)025<2959:AFSWDO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lopatoukhin, L. J., V. A. Rozhkov, V. E. Ryabinin, V. R. Swail, A. V. Boukhanovsky, and A. B. Degtyarev, 2000: Estimation of extreme wind wave heights. WMO/TD-1041, 84 pp., https://www.wmo.int/pages/prog/amp/mmop/documents/JCOMM-TR/J-TR-9-ExtWaveHeight/JCOMM-TR-9-Extr-Wave-Height-Full.pdf.

  • Meucci, A., I. R. Young, and Ø. Breivik, 2018: Wind and wave extremes from atmosphere and wave model ensembles. J. Climate, 31, 88198843, https://doi.org/10.1175/JCLI-D-18-0217.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ochi, M. K., 1992: New approach in estimating the severest sea state from statistical data. Proc. Coastal Eng. Conf., New York, NY, American Society of Civil Engineers, 512–523.

  • Ranjha, R., and Coauthors, 2015: Structure and variability of the Oman coastal low-level jet. Tellus, 67A, 25285, https://doi.org/10.3402/tellusa.v67.25285.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sterl, A., and S. Caires, 2005: Climatology, variability and extrema of ocean waves: The web-based KNMI/ERA-40 wave atlas. Int. J. Climatol., 25, 963977, https://doi.org/10.1002/joc.1175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stopa, J. E., and K. F. Cheung, 2014: Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis. Ocean Modell., 75, 6583, https://doi.org/10.1016/j.ocemod.2013.12.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, P. K., and M. J. Yelland, 2001: Comments on “On the effect of ocean waves on the kinetic energy balance and consequences for the inertial dissipation technique.” J. Phys. Oceanogr., 31, 25322536, https://doi.org/10.1175/1520-0485(2001)031<2532:COOTEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teng, C. C., 1998: Long-term and extreme waves in the Gulf of Mexico. Proc. Conf. on Ocean Wave Kinematics and Loads on Structures, Houston, TX, ASME, 342–349.

  • Tucker, M. J., 1991: Waves in Ocean Engineering. Ellis Horwood, 431 pp.

  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012, https://doi.org/10.1256/qj.04.176.

  • Van Gelder, P. H. A. J. M., and J. K. Vrijling, 1999: On the distribution function of the maximum wave height in front of reflecting structures. Proc. Coastal Structures Conf., Santander, Spain, American Society of Civil Engineers, 37–46.

  • Vinoth, J., and I. R. Young, 2011: Global estimates of extreme wind speed and wave height. J. Climate, 24, 16471665, https://doi.org/10.1175/2010JCLI3680.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wimmer, W., P. Challenor, and C. Retzler, 2006: Extreme wave heights in the North Atlantic from altimeter data. Renewable Energy, 31, 241248, https://doi.org/10.1016/j.renene.2005.08.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., 1993: An estimate of the Geosat altimeter wind speed algorithm at high wind speeds. J. Geophys. Res., 98, 20 27520 285, https://doi.org/10.1029/93JC02117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., 1994: Global ocean wave statistics obtained from satellite observations. Appl. Ocean Res., 16, 235248, https://doi.org/10.1016/0141-1187(94)90023-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., 1999: Seasonal variability of the global ocean wind and wave climate. Int. J. Climatol., 19, 931950, https://doi.org/10.1002/(SICI)1097-0088(199907)19:9<931::AID-JOC412>3.0.CO;2-O.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., and M. A. Donelan, 2018: On the determination of global ocean wind and wave climate from satellite observations. Remote Sens. Environ., 215, 228241, https://doi.org/10.1016/j.rse.2018.06.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., S. Zieger, and A. V. Babanin, 2011: Global trends in wind speed and wave height. Science, 332, 451455, https://doi.org/10.1126/science.1197219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., J. Vinoth, S. Zieger, and A. V. Babanin, 2012: Investigation of trends in extreme value wave height and wind speed. J. Geophys. Res., 117, C00J06, https://doi.org/10.1029/2011JC007753.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., A. V. Babanin, and S. Zieger, 2013: The decay rate of ocean swell observed by altimeter. J. Phys. Oceanogr., 43, 23222333, https://doi.org/10.1175/JPO-D-13-083.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., E. Sanina, and A.V. Babanin, 2017: Calibration and cross-validation of a global wind and wave database of altimeter, radiometer and scatterometer measurements. J. Atmos. Oceanic Technol., 34, 12851306, https://doi.org/10.1175/JTECH-D-16-0145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zeng, L., and R. A. Brown, 1998: Scatterometer observations at high wind speeds. J. Appl. Meteor., 37, 14121420, https://doi.org/10.1175/1520-0450(1998)037<1412:SOAHWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1673 519 41
PDF Downloads 1227 232 34