A more detailed description of a blended product similar to this one (12 months of NSCAT/ERS and NCEP, August 1996 to July 1997) can be found in the appendix of Milliff et al. (1999), and the details of the blending methodology are the subject of Chin et al. (1998); see Referencs listed below. Here is a quick summary of the product and method.
Caution This blended wind product was developed for general circulation scale analyses. It should not be used when mesoscale or ultra-high resolution is required. Moreover, each 6-hourly surface wind field is derived from the latest 12-hours of QSCAT observations (centered in time on the analysis time). This means that alternating halves of the globe retain the same QSCAT obs in each blended field output. Therefore, the blended winds are not suited for point-by-point, temporal comparisons (i.e. with buoy data), or when true 6-hourly resolution is needed.
Due to seasonal ice coverage, QSCAT data poleward of 60 degree is sparse. The NCEP re-analysis are of much lower quality in polar regions as well. QSCAT winds can be contaminated near ice edges resulting in very high wind speeds that are not always removed by quality control. Therefore, blended winds poleward of 60 degree latitude are of reduced quality, and gradient wind products, such as wind stress curl and divergence, cannot be trusted.
The Dirth product was developed to improve the accuracy of retrieved wind directions in two portions of the swath, the far swath and nadir regions. For reference see Special Wind Vector Data Product: Direction Interval with Threshold Nudging (DIRTH) by Bryan W. Stiles, September 13, 1999.
"At far swath, ambiguity removal skill is degraded due to the absence of inner beam measurements, limited azimuth diversity, and boundary effects. Near nadir, due to nonoptimal measurement geometry, (fore and aft looking measurement azimuths approximately 180° apart) there is a marked decrease in directional accuracy even when ambiguity removal works correctly. Direction interval retrieval (DIR) addresses the nadir performance issue, and threshold nudging (TN) improves ambiguity removal at far swath" (Stiles). In normal processing, there are up to four ambiguities available at each wind vector cell (WVC). Median filtering selects one of the ambiguities based on the relative likelihoods. In areas where the likelihood values are relatively similar for a large range of direction, the DIR median filtering does not select just between four direction values but considers ranges of direction around the likelihood maxima.
For the purpose of blending, the outermost three WVC's along the outer edges of the satellite swath are excluded. Also excluded are all rain-flagged QSCAT data.
NCEP Input Data
The National Centers for Environmental Prediction (NCEP) analysis fields are
the products of the NCEP Climate Data Assimilation System (CDAS), which was the
operational system developed for the NCEP-NCAR reanalysis (Kalnay et al. 1996).
The CDAS surface winds are available 4 times each day (at 0000, 0600, 1200, and
1800 UTC) on a Gaussian grid consistent with T62 resolution (i.e., triangular
truncation, admitting 62 zonal wavenumbers). The grid is 1.875° lon
x ca. 1.9° lat, but the true spatial resolution is coarser than T62.
NCEP reanalysis data are available from NOAA's
NOAA's Climate Diagnostics Center .
Global maps of the exponent p get computed from QSCAT data as monthly averages on a 8° x 8° grid. An entire month of scatterometer data is partitioned into continuous along track segments of at least 30° length. Wavelet coefficients are computed from the projections of segmented data onto nested wavelet basis functions for 8°, 4°, 2°, and 1° resolution intervals. The coefficients are accumulated in the latitude, longitude bin that contains the mid-point of the scatterometer data segment. Wavelet coefficient means and standard deviations are computed for each bin, and for each resolution interval. The means are essentially zero in all cases. The global maps of wavelet coefficient standard deviations for all resolution intervals are smoothed once with a 5-point spatial filter. Those coefficients are used to synthesize, locally in space and time, the high-wavenumber variability of the surface wind.
Formally, this blending method is an efficient manipulation of 2-dimensional cubic B-splines for the joint interpolation of scatterometer and NCEP analyzed surface wind fields, and for wavelet synthesis of high-wavenumber variability wherever there are gaps in the scatterometer observations. The B-spline operation on a discrete wind component field, say û, is denoted as [ û ] SS , where SS is the spline scale in kilometers and ^ implies a discrete field. The synthesis procedure is a B-spline implementation of orthonormal wavelet-based, multiresolution analysis (Wornell, 1993), that is forced to be statistically consistent with the scatterometer data. The overall procedure is applied independently for each velocity component at each new analysis time.
Construction of a blended, global field of a surface wind component, say u(x,y), first requires an analytic low-pass filtered NCEP field, u LP(x,y), at each analysis time. It is given by
Next global, analytic, realistic, high-wavenumber variability, u SYN (x,y), is synthesized from statistically generated wavelet coefficients. For a nested sequence of finer resolutions (8°, 4°, 2°, 1°), wavelet coefficients are obtained by randomly sampling a logarithmic-spike distribution (see Chin et al., 1998) of zero mean. The standard deviation of the distribution varies to match the regional standard deviation of the wavelet coefficients computed from QSCAT data for each resolution as 8° x 8° monthly averages. Therefore, statistically, u SYN (x,y) obeys the power-law constraint imposed by the QSCAT data. Finally, the enhanced analytic representation of the the wind component field is computed as
u(x,y) = u SUB (x,y) + u SYN (x,y)
In the blended wind product, spline approximations for u and v allow analytic computation of the spatial derivatives as follows
for the computation of curl:
This field can be sampled at any desired resolution. It contains
realistic variability to 50 km (0.5°), given that both
the Nyquist interval of the scatterometer observations, and the
highest wavelet resolution correspond to about 100 km.
Wind Stress and Curl calculations
Wind stress is computed as
where u = (u,v) is the wind vetor, |u| = (u2 + v2)1/2
is the wind speed, &rhoa is a typical air density (1.2 kg/m3), and CD
is the neutral 10-m drag coefficient based on Large, McWilliams, and Doney (1994): "Oceanic vertical mixing:
A review and a model with a nonlocal boundary layer parameterization", Rev.Geophys., 32, 363-403, and given by
Wind stress curlis calculated as the vertical component of the curl of &tau. And can be computed from
the gradient of t&tau:
For this procedure, maps of blended winds (on 0.5° × 0.5° grid) are used to compute wind stress,
and the above equation is discretized in a centered finite-difference form to approximate wind stress curl.
For details, see Milliff and Morzel (2001).
References
last modified on February 20, 2008
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