Boundary-Layer Meteorology

, Volume 148, Issue 1, pp 207–226 | Cite as

Assimilating QuikSCAT Ocean Surface Winds with the Weather Research and Forecasting Model for Surface Wind-Field Simulation over the Chukchi/Beaufort Seas

  • Xingang FanEmail author
  • Jeremy R. Krieger
  • Jing Zhang
  • Xiangdong Zhang


To achieve a high-quality simulation of the surface wind field in the Chukchi/Beaufort Sea region, quick scatterometer (QuikSCAT) ocean surface winds were assimilated into the mesoscale Weather Research and Forecasting model by using its three-dimensional variational data assimilation system. The SeaWinds instrument on board the polar-orbiting QuikSCAT satellite is a specialized radar that measures ice-free ocean surface wind speed and direction at a horizontal resolution of 12.5 km. A total of eight assimilation case studies over two five-day periods, 1–5 October 2002 and 20–24 September 2004, were performed. The simulation results with and without the assimilation of QuikSCAT winds were then compared with QuikSCAT data available during the subsequent free-forecast period, coastal station observations, and North American Regional Reanalysis data. It was found that QuikSCAT winds are a potentially valuable resource for improving the simulation of ocean near-surface winds in the Chukchi/Beaufort Seas region. Specifically, the assimilation of QuikSCAT winds improved, (1) offshore surface winds as compared to unassimilated QuikSCAT winds, (2) sea-level pressure, planetary boundary-layer height, as well as surface heat fluxes, and (3) low-level wind fields and geopotential height. Verification against QuikSCAT data also demonstrated the temporal consistency and good quality of QuikSCAT observations.


Data assimilation Numerical weather prediction QuikSCAT ocean surface winds Three-dimensional variational data assimilation Weather Research and Forecasting model 



This work was supported by funding from the Bureau of Ocean Energy Management of the U.S. Department of the Interior under contract 0106CT39787 and by funding from the National Oceanic and Atmospheric Administration (NOAA) under project SOVACC. Computational support was provided by the Arctic Region Supercomputing Center (ARSC) at the University of Alaska Fairbanks (UAF).


  1. Barker DM, Huang W, Guo YR, Xiao QN (2004) A three-dimensional (3DVAR) data assimilation system for use with MM5: implementation and initial results. Mon Weather Rev 132:897–914CrossRefGoogle Scholar
  2. Bengtsson L, Ghil M, Kallen E (1981) Dynamic meteorology: data assimilation methods. Springer, New York, 330 ppGoogle Scholar
  3. Chen SH (2007) The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulations. Mon Weather Rev 135:549–566. doi: 10.1175/MWR3283.1 CrossRefGoogle Scholar
  4. Chen F, Dudhia J (2001) Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modelling system. Part I: Model description and implementation. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
  5. Chen SH, Vandenberghe F, Petty GW, Bresch JF (2004) Application of SSM/I satellite data to a hurricane simulation. Q J R Meteorol Soc 130:801–825CrossRefGoogle Scholar
  6. Daley R (1991) Atmospheric data analysis. Cambridge University Press, New York, 457 ppGoogle Scholar
  7. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  8. Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132:103–120CrossRefGoogle Scholar
  9. Ide K, Courtier P, Ghil M, Lorenc A (1997) Unified notation for data assimilation: operational, sequential and variational. J Meteorol Soc Jpn Special Issue 75(1B):181–189Google Scholar
  10. Isaksen L, Janssen PAEM (2004) Impact of ERS scatterometer winds in ECMWF’s assimilation system. Q J R Meteorol Soc 130:1793–1814CrossRefGoogle Scholar
  11. Janjic ZI (1990) The step-mountain coordinate: physical package. Mon Weather Rev 118:1429–1443CrossRefGoogle Scholar
  12. Janjic ZI (1996) The surface layer in the NCEP eta model. In: Eleventh conference on numerical weather prediction, Norfolk, VA, 19–23 August, American Meteorological Society, Boston, pp 354–355Google Scholar
  13. Janjic ZI (2002) Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note, No. 437, 61 ppGoogle Scholar
  14. Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802CrossRefGoogle Scholar
  15. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritcsh scheme. In: Emanuel KA, Raymond DJ (eds) The representation of cumulus convection in numerical models. American Meteorological Society, Boston, 246 ppGoogle Scholar
  16. Kalnay E (2003) Atmospheric modelling, data assimilation and predictability. Cambridge University Press, New York, 341 ppGoogle Scholar
  17. Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82:247–267. doi: 10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2
  18. Kozo T (1980) Mountain barrier baroclinity effects on surface winds along the Alaskan coast. Geophys Res Lett 7:377–380CrossRefGoogle Scholar
  19. Kozo T (1982) An observational study of sea breezes along the Alaskan Beaufort Sea coast: part I. J Appl Meteorol 21(7):891–905CrossRefGoogle Scholar
  20. Leidner SM, Isaksen L, Hoffman RN (2003) Impact of NSCAT winds on tropical cyclones in the ECMWF 4DVAR assimilation system. Mon Weather Rev 131:3–26CrossRefGoogle Scholar
  21. Long DG, Mendel JM (1990) Model-based estimation of wind fields over the ocean from wind scatterometer measurements. I. Development of the wind field model. IEEE Trans Geosci Remote Sens 28(3):349–360CrossRefGoogle Scholar
  22. Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran PC, Ebisuzaki W, Jović D, Woollen J, Rogers E, Berbery EH, Ek MB, Fan Y, Grumbine R, Higgins W, Li H, Lin Y, Manikin G, Parrish D, Shi W (2006) North American regional reanalysis. Bull Am Meteorol Soc 87:343–360. doi: 10.1175/BAMS-87-3-343 CrossRefGoogle Scholar
  23. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long wave. J Geophys Res 102(D14):16663–16682CrossRefGoogle Scholar
  24. Parrish DF, Derber J (1992) The National Meteorological Center’s spectral statistical interpolation analysis system. Mon Weather Rev 120:1747–1763CrossRefGoogle Scholar
  25. Pichel W, Monaldo F, Nicoll J (2005) SAR-derived coastal winds. Alaska Satellite Facility News and Notes, winter 2:4Google Scholar
  26. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF version2. NCAR Technical Note, NCAR/TN-468+STR, 88 ppGoogle Scholar
  27. Stegall ST, Zhang J (2012) Wind field climatology, changes, and extremes in the Chukchi–Beaufort Seas and Alaska North Slope during 1979–2009. J Clim 25:8075–8089. doi: 10.1175/JCLI-D-11-00532.1 Google Scholar
  28. Talagrand O (1997) Assimilation of observations, an introduction. J Meteorol Soc Jpn Special Issue 75(1B):191–209Google Scholar
  29. Thompson DW, Wallace JM (1998) The Arctic Oscillation signature in the winter time geopotential height and temperature fields. Geophys Res Lett 25:1297–1300CrossRefGoogle Scholar
  30. Walsh JE, Chapman WL, Shy TL (1996) Recent decrease of sea level pressure in the central Arctic. J Clim 9:480–488CrossRefGoogle Scholar
  31. Zhang X, Walsh JE, Zhang J, Bhatt US, Ikeda M (2004) Climatology and interannual variability of Arctic cyclone activity: 1948–2002. J Clim 17:2300–2317CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Xingang Fan
    • 1
    Email author
  • Jeremy R. Krieger
    • 2
  • Jing Zhang
    • 3
  • Xiangdong Zhang
    • 4
  1. 1.Meteorology Program, Department of Geography and GeologyWestern Kentucky UniversityBowling GreenUSA
  2. 2.Arctic Region Supercomputing CenterUniversity of Alaska FairbanksFairbanksUSA
  3. 3.Departments of Physics and Energy and Environmental SystemsNorth Carolina A&T State UniversityGreensboroUSA
  4. 4.International Arctic Research CenterUniversity of Alaska FairbanksFairbanksUSA

Personalised recommendations