Advertisement

Ocean Surface Vector Wind Observations

  • Ad Stoffelen
  • Raj Kumar
  • Juhong Zou
  • Vladimir Karaev
  • Paul S. Chang
  • Ernesto Rodriguez
Chapter

Abstract

Ocean surface vector winds (OSVW) play a fundamental role in the Asian Seas through air-sea interaction; this applies to the modest winds in the trades, the winds associated with the extensive areas of tropical convection, sea and land breezes and, of most direct human relevance, the winds associated with hurricane-force typhoons. Predicting the air-sea exchanges in the cold polar seas and the atmospheric dynamics of tropical mesoscale convective systems or the strength and track of typhoons remains equally a challenge, but is of fundamental importance for weather forecasting and climate change studies. It is briefly described how wind vector information is obtained from satellite microwave active and passive measurements off the wind-roughened ocean surface, and subsequently an evaluation of the wind vector product services, for example, in coastal areas, is provided. India, China, Russia and Japan, inter alia, have been, are, or will be contributing to a global virtual constellation of scatterometers that provide increasing temporal coverage of ocean surface vector wind information. The application of scatterometer winds for weather nowcasting, for mesoscale and global numerical weather prediction and for oceanography and climate studies is highlighted.

Keywords

Scatterometer Ocean surface vector winds Virtual constellation Air-sea interaction Typhoons Moist convection Mesoscale Nowcasting Oceanography Climate 

Notes

Acknowledgements

The views expressed in this chapter have been developed through discussions in a wide international forum, among which the IOVWST and the International Winds Working Group (IWWG). Figure 2 was provided by student Patrick Bunn. Colleagues at KNMI provided much of the background material used, through the EUMETSAT OSI and NWP Satellite Application Facilities and the Copernicus Marine Environment Monitor. Service (CMEMS).

References

  1. Belmonte Rivas M, Stoffelen A (2011) New Bayesian algorithm for sea ice detection with QuikSCAT. IEEE Trans Geosci Remote Sens 2(49).  https://doi.org/10.1109/tgrs.2010.2101608CrossRefGoogle Scholar
  2. Blanke B et al (2005) Modelling the structure and variability of the southern Benguela upwelling using QuikSCAT wind forcing. J Geophys Res 110(C).  https://doi.org/10.1029/2004JC002529
  3. Bourassa M et al (2009) Remotely sensed winds and wind stresses for marine forecasting and ocean modelling. In: Proceedings of OceanObs’09, https://coaps.fsu.edu/scatterometry/reports/docs/OceanObs09_Winds_White_Paper.pdf
  4. Chang P, Jelenak Z, Sienkiewicz J, Knabb R, Brennan M, Long D, Freeberg M (2009) Operational use and impact of satellite remotely sensed ocean surface vector winds in the marine warning and forecasting environment. Oceanography 22(2):194–207CrossRefGoogle Scholar
  5. Charnock H (1955) Wind stress on a water surface. Quart J Royal Meteorol Soc 81(350):639–640CrossRefGoogle Scholar
  6. Chelton DB, Schlax MG, Freilich MH, Milliff RE (2004) Satellite measurements reveal persistent small-scale features in ocean winds. Science 303:978–983CrossRefGoogle Scholar
  7. de Kloe J et al (2017) Improved use of scatterometer measurements by using stress-equivalent reference winds. IEEE JSTARS. Accepted, JSTARS-2016-00580Google Scholar
  8. Dong J et al. (2013) Evaluating the spatio- temporal variation of China’s offshore wind resources based on remotely sensed wind field data. Renew Sust. Energy Rev 24:142–148.  https://doi.org/10.1016/j.rser.2013.03.058CrossRefGoogle Scholar
  9. ESA (1999) Atmospheric dynamics mission: core earth explorer mission selection report. ESA SP-1233(4), European Space Agency, Noordwijk, The NetherlandsGoogle Scholar
  10. GCOS (2011) Systematic observation requirements for satellite-based products for climate, 2011. GCOS Report 154. www.wmo.int/pages/prog/gcos/Publications/gcos-154.pdf
  11. Isaksen L, Stoffelen A (2000) ERS-scatterometer wind data impact on ECMWF’s tropical cyclone forecasts. IEEE Trans Geosci Remote Sens 38(4):1885–1892CrossRefGoogle Scholar
  12. Isaksen L, Janssen PAEM (2004) Impact of ERS scatterometer winds in ECMWF’s assimilation system. Q J R Meteorol Soc 130(600):1793–1814CrossRefGoogle Scholar
  13. King G P, Portabella M, Lin W, Stoffelen A (2017) Correlating extremes in wind and stress, EUMETSAT ocean and sea ice SAF, Scientific Report OSI_AVS_15_02, https://www.osi-saf.org
  14. King GP, Vogelzang J, Stoffelen A (2015) Upscale and downscale energy transfer over the tropical Pacific revealed by scatterometer winds. J Geophys Res.  https://doi.org/10.1002/2014JC009993CrossRefGoogle Scholar
  15. Kolmogorov AN (1941) Dissipation of energy in the locally isotropic turbulence. Proc USSR Acad Sci 32:16–18 (in Russian). English by Kolmogorov AN (1991) The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Proc R Soc A 434(1980):15–17Google Scholar
  16. Lin W, Portabella M, Stoffelen A, Verhoef A, Turiel A (2015) ASCAT wind quality control near rain. IEEE Trans Geosci Remote Sens 53(8):4165–4177CrossRefGoogle Scholar
  17. Lin W, Portabella M, Stoffelen A, Vogelzang J, Verhoef A (2016) On mesoscale analysis and ASCAT ambiguity removal. Quart J R Meteorol Soc QJ-15-0247.R2 (in press)Google Scholar
  18. Liu WT, Xie X (2006) Measuring ocean surface wind from space. In: Gower J (ed) Remote sensing of the marine environment, 3rd edn, vol 6. ISBN1570830800Google Scholar
  19. Moore GW, Renfrew IA (2005) Tip jets and barrier winds: a QuikSCAT climatology of high wind speed events around Greenland. J Climate 18:3713–3725CrossRefGoogle Scholar
  20. Nastrom GD, Gage KS (1985) A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J Atmos Sci 42:950–960CrossRefGoogle Scholar
  21. Portabella M, Stoffelen A (2009) On scatterometer ocean stress. J Atm Oceanic Technol 2(26):368–382.  https://doi.org/10.1175/2008JTECHO578.1CrossRefGoogle Scholar
  22. Portabella M, Stoffelen A, Verhoef A, Verspeek J (2012) A new method for improving scatterometer wind quality control. IEEE Geosci Remote Sens Lett 9(4):579–583CrossRefGoogle Scholar
  23. Sandu I et al (2011) Why is it so difficult to represent stably stratified conditions in numerical weather prediction (NWP) models? J Adv Model Earth Syst 5:117–133CrossRefGoogle Scholar
  24. Sherwood SC, Bony S, Dufresne J-L (2014) Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505:37–42.  https://doi.org/10.1038/nature12829CrossRefGoogle Scholar
  25. Shimada T, Kawamura H (2006) Satellite observations of sea surface temperature and sea surface wind coupling in the Japan Sea. J Geophys Res 111(C).  https://doi.org/10.1029/2005jc003345
  26. Stoffelen A (1998) Scatterometry. PhD thesis at the University of Utrecht. ISBN 90-393-1708-9Google Scholar
  27. Stoffelen A, Anderson D (1997) Ambiguity removal and assimilation of scatterometer data. Quart J R Meteorol Soc 123:491–518CrossRefGoogle Scholar
  28. Stoffelen A, Aaboe S, Calvet J-C, Cotton J, De Chiara G, Saldana JF, Mouche AA, Portabella M, Scipal K, Wagner W (2017) Scientific developments and the EPS-SG scatterometer. IEEE J Sel Topics Appl Earth Observ Remote Sensing 10(5):2086–2097 CrossRefGoogle Scholar
  29. Stoffelen A et al (2006) ADM-Aeolus doppler wind lidar observing system simulation experiment. Quart J R Meteor Soc 132:1927–1947.  https://doi.org/10.1256/qj.05.83CrossRefGoogle Scholar
  30. Stoffelen A et al (2013) Research and development in Europe on global application of the OceanSat-2 Scatterometer Winds. NWP SAF report NWPSAF-KN-TR-022. http://www.knmi.nl/publications/fulltexts/oceansat_cal_val_report_final_copy1.pdf
  31. Stoffelen A, Vogelzang J, Lin W (2015) On buoys, scatterometers and reanalyses for globally representative winds. EUMETSAT NWP SAF, Document NWPSAF-KN-TR-024, V1.1Google Scholar
  32. Tokinaga H et al (2009) Ocean frontal effects on the vertical development of clouds over the Western North Pacific: in situ and satellite observations. J Climate 22.  https://doi.org/10.1175/2009jcli2763.1CrossRefGoogle Scholar
  33. Tokmakian R (2005) An ocean model’s response to scatterometer winds. Ocean Model 9:89–103CrossRefGoogle Scholar
  34. Verhoef A, Vogelzang J, Stoffelen A (2015) SeaWinds wind climate data record validation report. www.knmi.nl/scatterometer/publications/pdf/seawinds_cdr_validation.pdf
  35. Vogelzang J, King GP, Stoffelen A (2015) Spatial variances of wind fields and their relation to second-order structure functions and spectra. J Geophys Res.  https://doi.org/10.1002/2014JC010239CrossRefGoogle Scholar
  36. Vogelzang J, Stoffelen A, Verhoef A, Figa-Saldaña J (2011) On the quality of high-resolution scatterometer winds. J Geophys Res 116(C10033).  https://doi.org/10.1029/2010jc006640
  37. Von Ahn J, Sienkiewicz J, Chang C (2006) Operational impact of QuikSCAT winds at the NOAA ocean prediction center. Weather Forecast 21:523–539CrossRefGoogle Scholar
  38. Wang Z, Stoffelen A, Zhao C, Vogelzang J, Verhoef A, Verspeek J, Lin M, Chen G (2017) A SST-dependent Ku-band geophysical model function for RapidScat. Accepted for J Geoph Res Oceans.  https://doi.org/10.1002/2016jc012619Google Scholar
  39. Xie S-P et al (2007) Intraseasonal variability in the summer South China Sea: wind jet, cold filament, and recirculations. J Geophys Res 112:C10008.  https://doi.org/10.1029/2007JC004238CrossRefGoogle Scholar
  40. Yu Y et al (2015) Assimilation of HY-2A sea surface wind data in a 3DVAR DAS—a case study of Typhoon Bolaven. Front Earth Sci 9:192.  https://doi.org/10.1007/s11707-014-0461-8CrossRefGoogle Scholar
  41. van Zadelhoff G-J et al (2014) Retrieving hurricane wind speeds using cross-polarization C-band measurements. Atmos Meas Technol 7(2):437–449.  https://doi.org/10.5194/amt-7-437-2014CrossRefGoogle Scholar
  42. Žagar N et al. (2016) Mesoscale data assimilation and the role of winds in limited-area NWP. http://meteo.fmf.uni-lj.si/sites/default/files/MesoWindsWorkshopLjubljana2016_Summary.pdf

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Ad Stoffelen
    • 1
  • Raj Kumar
    • 2
  • Juhong Zou
    • 3
  • Vladimir Karaev
    • 4
  • Paul S. Chang
    • 5
  • Ernesto Rodriguez
    • 6
  1. 1.Royal, Netherlands Meteorological Institute (KNMI)de BiltThe Netherlands
  2. 2.Indian Space Research Organisation (ISRO)AhmedabadIndia
  3. 3.National Space Ocean Application Service (NSOAS)BeijingChina
  4. 4.Institute of Applied Physics, Russian Academy of SciencesNizhny NovgorodRussia
  5. 5.National Ocean and Atmosphere Administration (NOAA)WashingtonUSA
  6. 6.National Aeronautics and Space Administration (NASA)PasadenaUSA

Personalised recommendations