QSCAT-KU2000 and NCEP Blended Winds

Summary

Global 6-hourly maps of ocean surface winds are derived from a space and time blend of QSCAT-KU2000 scatterometer observations and NCEP analyses. This blending method creates global fields by retaining QSCAT wind retrievals in swath regions, and in the unsampled regions (nadir and interswath gaps) augmenting the low-wavenumber NCEP fields with a high-wavenumber component that is derived from monthly regional QSCAT statistics.

6-hourly maps of 10m zonal and meridional wind-components (U and V) are available at a resolution of 0.5° x 0.5° resolution. The data starts on July 20, 1999, is complete thru December 31, 2000, and will be continued as more QSCAT data is processed. Data files are available from the National Center for Atmospheric Research (NCAR) Data Supprt Section (DSS): DS744.4 - QSCAT/NCEP Blended Ocean Surface Winds. Fortran code to read the binary data files is available from the same webpage.

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). Here is a quick summary of the product and method.

Blended Data

The blended data set includes files for 10m surface wind-components U and V, as well as for windstress curl. There are three different products available:

bln: This is the finished product. Blended QSCAT observations and NCEP analyses with realistic high-wavenumber variability everywhere and the satellite data precisely preserved where it occurs.
low: The NCEP analysis field, splined to the 0.5° x 0.5° grid.
sub: This is an intermediate product of no particular scientific use, but very useful in comparing details of the flow (e.g. fronts, cyclones, etc). This product is the superposition of QSCAT observations on NCEP before blending; 12hrs of QSCAT data centered on the analysis time overlay the NCEP analyses.

The output grid has a resolution of 0.5° x 0.5°, and spans from 88S to 88N. Land points are not set to some missing values, but instead the technique is applied everywhere: the NCEP analyses include wind values over land and the blending adds statistical high-wavenumber variability to this background field wherever there are no satellite observations. This way, the dataset can be used to force any ocean model, regardles of its particular configuration of the land/ocean mask.

Input Data for Blending

QSCAT Input Data

The scatterometer data is the KU-2000 product from Remote Sensing Systems (RSS). At a resolution of 25km, the QSCAT measurement swath cross-section covers at least 1800km, or 72 wind vector cells (WVC), and occasionally 1900km (76 WVC). RSS advises users of the KU2000 product to increase wind speed by 4.8%, which was done for the input data for the blending scheme. Their model function was initially calibrated against NCEP's 10m winds. Based on more recent buoy data, they have found that NCEP winds are a little low relative to the buoys (and ECMWF winds are way low relative to the buoys). They do not understand, however, why the GCM's are biased low, given that these models ingest buoy data.

There are several quality control criteria that were used to extract only the highest-quality satellite data for blending (see also the QSCAT Curl webpage). The following data were excluded from blending:

Rain-contaminated data, as derived from the radar return signal: KU2000 quality flag "IRAIN_SCAT"=1;
Rain-contaminated data, as measured by co-located (and within 30min) TMI or SSMI satellite data: "RAD_RAIN">0.15 and "MIN_DIFF" <= 30;
Data with only two or fewer look angles, usually 8 WVC along both outside edges: "CLASS" < 2;
Data for which the reflected radar signal does not match very well the model function: "SOSAL" > 1.9;
Nadir Data, where there is sytematically greater high-wavenumber variability than in the other part of the satellite swath: ten WVC, indeces 34 thru 43; and
High wind speed data, where the wind speed is greater than 40 m/s.

Current research focuses on the reason for higher-wavenumber variability under the satellite nadir, how KU-2000 differs from the standard JPL product, and whether this variability is artifically too high or whether the other part of the satellite swath is too smooth. Further research questions are: whether rain-contaminated WVC should or should not be included in derived wind products, how they could be corrected, and what the effect is of not including any data from rainy areas (see the QSCAT Curl webpage for more details on some of these issues).

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.

High-wavenumber Power-Law Constraint for Blending

The blending creates global fields of surface winds by retaining QSCAT wind retrievals in swath regions, and in the unsampled regions (nadir and interswath gaps) augmenting the low-wavenumber NCEP fields with a high-wavenumber component that obeys observed power law relations:

PSD ~ k ^p,

where PSD is the power-spectral density, k the wavenumber, and the exponent p characterizes the power-law behavior. QSCAT data show that p takes values between -2 at high latitudes and -5/3 at the equator.

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.

Blending Method

Scatterometer wind speed and direction estimates arise from co-located radar backscatter measurements as processed by the geophysical model function specific to the QSCAT radar system. Determining the accuracies of these wind estimates is an area of active current research, however it is not an issue here. For the purpose of blending, it is assumed that the scatterometer estimates for surface wind speed and direction, that survive the above stated quality controls, are correct.

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

u _LP (x,y) = [ û _NCEP ] ₂₂₀,
where the B-spline operation is denoted by the square brackets, whose subscript, SS = 220, is the spline scale in kilometers. This low-pass field is used to detrend all the scatterometer data in a ± 6-hour temporal window, wherever QSCAT and NCEP overlap in space. A small scale spline fit to the high-pass filtered scatterometer data gives

u _HP (x,y) = [û _QSCAT - û _LP (x,y) ] ₅₅.
An analytic NCEP/QSCAT wind component field is then given by

u _SUB (x,y) = u _LP (x,y) + u _HP (x,y).
Within a swath, u _SUB (x,y) exactly reflects the scatterometer data, because the filtered NCEP field is first removed then added back. The high-pass spline provides a smooth transition over 3 spline scales (165 km) between the scatterometer winds in a swath and and the NCEP wind in the gaps between swaths.

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) within swaths, and

u(x,y) = u _SUB (x,y) + u _SYN (x,y) within gaps.

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.

References

Chin, T.M., R.F. Milliff, and W.G. Large, 1998: Basin-scale, high-wavenumber sea surface wind fields from a multiresolution analysis of scatterometer data. Journal of Atmospheric and Oceanic Technology , 15, 741-763.
Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M.-Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K.C. Mo, C. Ropelewski, A. Leetmaa, R. Reynolds, and R. Jenne, 1996: The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society , 77, 437-471.
Milliff, R.F., W.G. Large, J. Morzel, G. Danabasoglu, and T.M. Chin, 1999: Ocean general circulation model sensitivity to forcing from scatterometer winds. Journal of Geophysical Research, Oceans, Vol. 104, No. C5, 11337-11358.

last modified on May 31, 2001

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