Validation of High Ocean Surface Winds from Satellites Using Oil Platform Anemometers

Andrew Manaster Remote Sensing Systems, Santa Rosa, California

Search for other papers by Andrew Manaster in
Current site
Google Scholar
PubMed
Close
,
Lucrezia Ricciardulli Remote Sensing Systems, Santa Rosa, California

Search for other papers by Lucrezia Ricciardulli in
Current site
Google Scholar
PubMed
Close
, and
Thomas Meissner Remote Sensing Systems, Santa Rosa, California

Search for other papers by Thomas Meissner in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Reliable sources for validating wind observations made by spaceborne microwave radiometer and scatterometer sensors above 15 m s−1 are scarce. Anemometers mounted on oil platforms provide usable wind speed measurements that can help fill this gap. In our study we compare wind speed observations from six microwave satellites (WindSat, AMSR-E, AMSR2, SMAP, QuikSCAT, and ASCAT) with wind speed records from 10 oil platform anemometers in the North and Norwegian Seas that were provided by the Norwegian Meteorological Institute. We study various forms of the vertical wind profile, which is required to convert anemometer winds to a reference height of 10 m above sea level. We create and analyze matchups between satellite and anemometer winds and find good agreement up to wind speeds of 30 m s−1 within the margin of errors. We also evaluate wind speeds from several analyses [ECMWF, NCEP, and Cross-Calibrated Multi-Platform (CCMP)]. We find them to be significantly lower than the anemometer winds with their biases increasing systematically with increasing wind speed. Important components of our analysis include a detailed discussion on the quality control of the anemometer winds and a quantitative analysis of the uncertainties in creating the matchups.

© 2019 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: Andrew Manaster, manaster@remss.com

Abstract

Reliable sources for validating wind observations made by spaceborne microwave radiometer and scatterometer sensors above 15 m s−1 are scarce. Anemometers mounted on oil platforms provide usable wind speed measurements that can help fill this gap. In our study we compare wind speed observations from six microwave satellites (WindSat, AMSR-E, AMSR2, SMAP, QuikSCAT, and ASCAT) with wind speed records from 10 oil platform anemometers in the North and Norwegian Seas that were provided by the Norwegian Meteorological Institute. We study various forms of the vertical wind profile, which is required to convert anemometer winds to a reference height of 10 m above sea level. We create and analyze matchups between satellite and anemometer winds and find good agreement up to wind speeds of 30 m s−1 within the margin of errors. We also evaluate wind speeds from several analyses [ECMWF, NCEP, and Cross-Calibrated Multi-Platform (CCMP)]. We find them to be significantly lower than the anemometer winds with their biases increasing systematically with increasing wind speed. Important components of our analysis include a detailed discussion on the quality control of the anemometer winds and a quantitative analysis of the uncertainties in creating the matchups.

© 2019 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: Andrew Manaster, manaster@remss.com
Save
  • Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, and D. Gombos, 2011: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Amer. Meteor. Soc., 92, 157174, https://doi.org/10.1175/2010BAMS2946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bidlot, J. R., D. J. Holmes, P. A. Wittmann, R. Lalbeharry, and H. S. Chen, 2002: Intercomparison of the performance of operational ocean wave forecasting systems with buoy data. Wea. Forecasting, 17, 287310, https://doi.org/10.1175/1520-0434(2002)017<0287:IOTPOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bourassa, M. A., S. T. Gille, D. L. Jackson, J. B. Roberts, and G. A. Wick, 2010: Ocean winds and turbulent air-sea fluxes inferred from remote sensing. Oceanography, 23 (4), 3651, https://doi.org/10.5670/oceanog.2010.04.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draper, D. W., and D. G. Long, 2004: Evaluating the effect of rain on SeaWinds scatterometer measurements. J. Geophys. Res., 109, C02005, https://doi.org/10.1029/2002JC001741.

    • Search Google Scholar
    • Export Citation
  • Ebuchi, N., H. C. Graber, and M. J. Caruso, 2002: Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data. J. Atmos. Oceanic Technol., 19, 20492062, https://doi.org/10.1175/1520-0426(2002)019<2049:EOWVOB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fore, A. G., S. H. Yueh, W. Tang, B. W. Stiles, and A. K. Hayashi, 2016: Combined active/passive retrievals of ocean vector wind and sea surface salinity with SMAP. IEEE Trans. Geosci. Remote Sens., 54, 73967404, https://doi.org/10.1109/TGRS.2016.2601486.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furevik, B. R., and H. Haakenstad, 2012: Near-surface marine wind profiles from rawinsonde and NORA10 hindcast. J. Geophys. Res., 117, D23106, https://doi.org/10.1029/2012JD018523.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., 2008: CMOD5.n: A C-band geophysical model function for equivalent neutral wind. ECMWF Tech. Memo. 554, 20 pp.

  • Hersbach, H., 2010: Comparison of C-band scatterometer CMOD5.n equivalent neutral winds with ECMWF. J. Atmos. Oceanic Technol., 27, 721736, https://doi.org/10.1175/2009JTECHO698.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilburn, K. A., and F. J. Wentz, 2008: Intercalibrated passive microwave rain products from the Unified Microwave Ocean Retrieval Algorithm (UMORA). J. Appl. Meteor. Climatol., 47, 778794, https://doi.org/10.1175/2007JAMC1635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilburn, K. A., F. J. Wentz, D. K. Smith, and P. D. Ashcroft, 2006: Correcting active scatterometer data for the effects of rain using passive radiometer data. J. Appl. Meteor. Climatol., 45, 382398, https://doi.org/10.1175/JAM2357.1.

    • 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
  • Kettle, A. J., 2015: A diagram of wind speed versus air-sea temperature difference to understand the marine atmospheric boundary layer. Energy Procedia, 76, 138147, https://doi.org/10.1016/j.egypro.2015.07.879.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and E. W. Uhlhorn, 2014: Improved Stepped Frequency Microwave Radiometer tropical cyclone surface winds in heavy precipitation. J. Atmos. Oceanic Technol., 31, 23922408, https://doi.org/10.1175/JTECH-D-14-00028.1.

    • 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
  • Liu, W. T., and W. Tang, 1996: Equivalent neutral wind. JPL Publ. 96-17, 22 pp., https://ntrs.nasa.gov/search.jsp?R=19970010322.

  • Mears, C. A., D. K. Smith, and F. J. Wentz, 2001: Comparison of special sensor microwave imager and buoy-measured wind speeds from 1987 to 1997. J. Geophys. Res., 106, 11 71911 729, https://doi.org/10.1029/1999JC000097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meissner, T., and F. J. Wentz, 2009: Wind-vector retrievals under rain with passive satellite microwave radiometers. IEEE Trans. Geosci. Remote Sens., 47, 30653083, https://doi.org/10.1109/TGRS.2009.2027012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meissner, T., and F. J. Wentz, 2012: The emissivity of the ocean surface between 6 and 90 GHz over a large range of wind speeds and Earth incidence angles. IEEE Trans. Geosci. Remote Sens., 50, 30043026, https://doi.org/10.1109/TGRS.2011.2179662.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meissner, T., L. Ricciardulli, and F. J. Wentz, 2017: Capability of the SMAP mission to measure ocean surface winds in storms. Bull. Amer. Meteor. Soc., 98, 16601677, https://doi.org/10.1175/BAMS-D-16-0052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meissner, T., L. Ricciardulli, and F. J. Wentz, 2018: Remote Sensing Systems SMAP daily sea surface winds speeds on 0.25 deg grid, FINAL, version 01.0. Remote Sensing Systems, accessed 15 November 2017, www.remss.com/missions/smap/.

  • Peixoto, J., and A. Oort, 1991: Physics of Climate. American Institute of Physics, 520 pp.

  • Pineau-Guillou, L., F. Ardhuin, M. N. Bouin, J. L. Redelsperger, B. Chapron, J. R. Bidlot, and Y. Quilfen, 2018: Strong winds in a coupled wave–atmosphere model during a North Atlantic storm event: Evaluation against observations. Quart. J. Roy. Meteor. Soc., 144, 317332, https://doi.org/10.1002/qj.3205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poe, G. A., 1990: Optimum interpolation of imaging microwave radiometer data. IEEE Trans. Geosci. Remote Sens., 28, 800810, https://doi.org/10.1109/36.58966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portabella, M., A. Stoffelen, W. Lin, A. Turiel, A. Verhoef, J. Verspeek, and J. Ballabrera-Poy, 2012: Rain effects on ASCAT-retrieved winds: Toward an improved quality control. IEEE Trans. Geosci. Remote Sens., 50, 24952506, https://doi.org/10.1109/TGRS.2012.2185933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Renfrew, I. A., G. N. Petersen, D. A. J. Sproson, G. W. K. Moore, H. Adiwidjaja, S. Zhang, and R. North, 2009: A comparison of aircraft-based surface-layer observations over Denmark Strait and the Irminger Sea with meteorological analyses and QuikSCAT winds. Quart. J. Roy. Meteor. Soc., 135, 20462066, https://doi.org/10.1002/qj.444.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reul, N., B. Chapron, E. Zabolotskikh, C. Donlon, Y. Quilfen, S. Guimbard, and J. F. Piolle, 2016: A revised L-band radio-brightness sensitivity to extreme winds under tropical cyclones: The five year SMOS-storm database. Remote Sens. Environ., 180, 274291, https://doi.org/10.1016/j.rse.2016.03.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reul, N., and Coauthors, 2017: A new generation of tropical cyclone size measurements from space. Bull. Amer. Meteor. Soc., 98, 23672385, https://doi.org/10.1175/BAMS-D-15-00291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ricciardulli, L., 2016: ASCAT on MetOp-A data product update notes. Remote Sensing Systems Tech. Rep. 40416, 5 pp.

  • Ricciardulli, L., and F. J. Wentz, 2015: A scatterometer geophysical model function for climate-quality winds: QuikSCAT Ku-2011. J. Atmos. Oceanic Technol., 32, 18291846, https://doi.org/10.1175/JTECH-D-15-0008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ricciardulli, L., and F. J. Wentz, 2016: Remote Sensing Systems ASCAT C-2015 daily ocean vector winds on 0.25 deg grid, version 2.1. Remote Sensing Systems, accessed 15 November 2017.

  • Ricciardulli, L., F. J. Wentz, and D. K. Smith, 2011: Remote Sensing Systems QuikSCAT Ku-2011 daily orbital swath ocean vector winds L2B, version 4. Remote Sensing Systems, accessed 15 November 2017, www.remss.com/missions/qscat.

  • Saetra, Ø., and J. R. Bidlot, 2004: Potential benefits of using probabilistic forecasts for waves and marine winds based on the ECMWF Ensemble Prediction System. Wea. Forecasting, 19, 673689, https://doi.org/10.1175/1520-0434(2004)019<0673:PBOUPF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., and A. J. Schrader, 2000: The automated Tropical Cyclone Forecasting System (version 3.2). Bull. Amer. Meteor. Soc., 81, 12311240, https://doi.org/10.1175/1520-0477(2000)081<1231:TATCFS>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skey, S. G. P., and M. D. Miles, 1999: Advances in buoy technology for wind/wave data collection and analysis. OCEANS’99, Seattle, WA, IEEE, 113–118, https://doi.org/10.1109/OCEANS.1999.799716.

    • Crossref
    • Export Citation
  • Soisuvarn, S., Z. Jelenak, P. S. Chang, S. O. Alsweiss, and Q. Zhu, 2013: CMOD5.h—A high wind geophysical model function for C-band vertically polarized satellite scatterometer measurements. IEEE Trans. Geosci. Remote Sens., 51, 37443760, https://doi.org/10.1109/TGRS.2012.2219871.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stiles, B. W., and S. H. Yueh, 2002: Impact of rain on spaceborne Ku-band wind scatterometer data. IEEE Trans. Geosci. Remote Sens., 40, 19731983, https://doi.org/10.1109/TGRS.2002.803846.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoffelen, A., J. A. Verspeek, J. Vogelzang, and A. Verhoef, 2017: The CMOD7 geophysical model function for ASCAT and ERS wind retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 21232134, https://doi.org/10.1109/JSTARS.2017.2681806.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stogryn, A., 1978: Estimates of brightness temperatures from scanning radiometer data. IEEE Trans. Antennas Propag., 26, 720726, https://doi.org/10.1109/TAP.1978.1141919.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, P. K., and M. J. Yelland, 2001: The dependence of sea surface roughness on the height and steepness of the waves. J. Phys. Oceanogr., 31, 572590, https://doi.org/10.1175/1520-0485(2001)031<0572:TDOSSR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tournadre, J., and Y. Quilfen, 2003: Impact of rain cell on scatterometer data: 1. Theory and modeling. J. Geophys. Res., 108, 3225, https://doi.org/10.1029/2002JC001428.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., and P. G. Black, 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99116, https://doi.org/10.1175/1520-0426(2003)020<0099:VORSSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., P. G. Black, J. L. Franklin, M. Goodberlet, J. Carswell, and A. S. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon. Wea. Rev., 135, 30703085, https://doi.org/10.1175/MWR3454.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verhoef, A., M. Portabella, A. Stoffelen, and H. Hersbach, 2008: CMOD5.n—The CMOD5 GMF for neutral winds. KNMI Tech. Note SAF/OSI/CDOP/KNMI/TEC/TN/3, 13 pp.

  • Verspeek, J., A. Stoffelen, M. Portabella, H. Bonekamp, C. Anderson, and J. F. Saldaña, 2010: Validation and calibration of ASCAT using CMOD5.n. IEEE Trans. Geosci. Remote Sens., 48, 386395, https://doi.org/10.1109/TGRS.2009.2027896.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weissman, D. E., and M. A. Bourassa, 2008: Measurements of the effect of rain-induced sea surface roughness on the QuikSCAT scatterometer radar cross section. IEEE Trans. Geosci. Remote Sens., 46, 28822894, https://doi.org/10.1109/TGRS.2008.2001032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., 1997: A well-calibrated ocean algorithm for special sensor microwave/imager. J. Geophys. Res., 102, 87038718, https://doi.org/10.1029/96JC01751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., and R. W. Spencer, 1998: SSM/I rain retrievals within a unified all-weather ocean algorithm. J. Atmos. Sci., 55, 16131627, https://doi.org/10.1175/1520-0469(1998)055<1613:SIRRWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., L. Ricciardulli, C. Gentemann, T. Meissner, K. A. Hilburn, and J. Scott, 2013: Remote Sensing Systems Coriolis WindSat daily environmental suite on 0.25 deg grid, version 7. Remote Sensing Systems, accessed 15 November 2017, www.remss.com/missions/windsat.

  • Wentz, F. J., T. Meissner, C. Gentemann, K. A. Hilburn, and J. Scott, 2014a: Remote Sensing Systems GCOM-W1 AMSR2 daily environmental suite on 0.25 deg grid, version 8. Remote Sensing Systems, accessed 15 November 2017, www.remss.com/missions/amsr.

  • Wentz, F. J., T. Meissner, C. Gentemann, and M. Brewer, 2014b: Remote Sensing Systems AQUA AMSR-E daily environmental suite on 0.25 deg grid, version 7. Remote Sensing Systems, accessed 15 November 2017, www.remss.com/missions/amsr.

  • Wentz, F. J., J. Scott, R. Hoffman, M. Leidner, R. Atlas, and J. Ardizzone, 2015: Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, version 2.0. Remote Sensing Systems, accessed 15 November 2017, www.remss.com/measurements/ccmp.

  • Wentz, F. J., and Coauthors, 2017: Evaluating and extending the ocean wind climate data record. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 21652185, https://doi.org/10.1109/JSTARS.2016.2643641.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zabolotskikh, E., L. Mitnik, and B. Chapron, 2014: GCOM-W1 AMSR2 and MetOp-A ASCAT wind speeds for the extratropical cyclones over the North Atlantic. Remote Sens. Environ., 147, 8998, https://doi.org/10.1016/j.rse.2014.02.016.

    • 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 791 97 11
PDF Downloads 566 61 9