The QSCAT dataset flags rain contaminated wind vector cells where retrievals are either highly uncertain or not available. Missing QSCAT surface wind retrievals due to rain contamination lead to statistically significant discrepancies of up to 50% in the implied Sverdrup transports in sub-tropical and sub-polar gyre regions of the Northern and Southern Hemispheres. Blended QSCAT+NCEP surface winds ameliorate the rain-flag induced biases in zonal averages of wind stress curl and wind stress divergence, while retaining high-wavenumber properties of the scatterometer winds. Evidence to support rain flag algorithm refinement for high wind speeds is presented.
This chapter provides a summary of current capabilities and near-future plans for surface vector wind observation sampling frequency, record length, spatial resolution, and global coverage. These properties are put in proper physical oceanographic context by means of several examples described in the appendix.
The time-average wind stress curl field for the global ocean is computed from the wind retrievals of the NSCAT mission, October 1, 1996 - June 29, 1997. "Patchiness" (small-scale, large-amplitude features) in the average NSCAT curl is considered in the context of aliases introduced by the complex spatio-temporal sampling pattern, and given the intermittency and large gradients that characterize the true wind stress curl field over the ocean. NSCAT curl is computed at 0.5° and 1.0° resolutions; and on a T62 Gaussian grid. The latter is compared with the average of NCEP analyses for the same period.
Observations of the surface wind speed and direction in the Labrador Sea for the period October 1996 through May 1997 were obtained by the NASA scatterometer (NSCAT), and by 21 newly developed Minimet drifting buoys. Minimet wind speed and direction retrievals in the Labrador Sea were calibrated with colocated NSCAT data.
Wind speed and direction RMS differences vs. spatial separation comparisons (from 0 to 400km) for the NSCAT and Minimet records demonstrate similar RMS differences in wind speed as a function of spatial separation, but O(20°) larger RMS differences in Minimet direction. These differences are consistent with spatial smoothing effects in the median filter step for wind direction retrievals within the NSCAT swath. Zonal and meridional surface wind components are constructed from the calibrated Minimet wind speed and direction dataset. RMS differences vs. spatial separation for these components are used to estimate mesoscale spatial correlation length scales of 250km and 290km in the zonal and meridional directions, respectively.
Figures (1-11) and Tables (1-3) for are presented here.