Article

Boundary-Layer Meteorology

, Volume 148, Issue 1, pp 207-226

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

  • Xingang FanAffiliated withMeteorology Program, Department of Geography and Geology, Western Kentucky University Email author 
  • , Jeremy R. KriegerAffiliated withArctic Region Supercomputing Center, University of Alaska Fairbanks
  • , Jing ZhangAffiliated withDepartments of Physics and Energy and Environmental Systems, North Carolina A&T State University
  • , Xiangdong ZhangAffiliated withInternational Arctic Research Center, University of Alaska Fairbanks

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Abstract

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.

Keywords

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