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the ocean surface wind vectors varies from 12.5 km, 25 km to 50 km. It may be noted that
the swath of scatterometers is wide and this results in frequent coverage. QuikSCAT and
OSCAT have swaths around 1800 km and ASCAT has two 500 km swaths. Near-global coverage
is achieved twice per day for QuikSCAT and OSCAT and once per day for ASCAT-1/2
combined. For details see Karagali 2013.
15.3 Wind retrieval from SAR
NRCS is the observed quantity of a SAR. NRCS depends on the size and geometry of roughness
elements on the scale of the radar wavelength at the Earth’s surface (Figure 187-left).
Over a calm ocean surface, the returned NRCS is limited because radar pulses are reflected
away from the SAR at an angle equal to the angle of incidence. As the wind picks up, roughness
in the form of capillary and short-gravity waves is generated by the surface wind stress.
Thedominantscatteringmechanism isthen diffuse and knownas Bragg scattering(resonance
scattering). The Bragg waves ride on longer-period waves (Valenzuela, 1978). Equation 348
gives the simple relationship between the wavelength of Bragg waves and radar wavelength:
λBragg = λradar
(348)
2sinθ
where λ is the wavelength for Bragg and radar, respectively, and θ is the incidence angle.
The relation of NRCS to the local wind speed and direction, and to the radar viewing geometry
forms the key principle in ocean wind retrievals from SAR. High-frequency radars (Xor
Ku-band) are generally the most sensitive to small-scale waves generated by the instantaneous
local wind. Lower-frequency SAR sensors (L-band) are more sensitive to longer-period
surface waves that, because of their longer growth time are not so sensitive to local wind
fluctuations.
SAR sensors operate with a single antenna and view each ground target from one angle
only. As a consequence, several wind speed and direction pairs correspond to a given NRCS.
The number of possible solutions may be reduced if a priori information about the wind
direction is used to retrieve the wind speed.
The wind direction may be inferred directly from SAR images using FFT (Gerling, 1986;
Lehner et al., 1998; Furevik et al., 2002), wavelet (Fichaux & Ranchin, 2002; Du et al.,
2002) or gradient methods (Horstmann et al., 2000; 2003; Koch, 2004). The methods outline
the orientation of km-scale wind streaks visible in many SAR images. These wind streaks
are aligned approximately with the wind direction. The streaks originate from atmospheric
roll vortices and other phenomena impacting the sea surface. However, the wind direction
methods do not always produce reliable results. In addition the 180◦ ambiguity has to be
resolved. In other words, the methods may resolve the orientation of linear features “the wind
streaks” in the SAR images but none of the methods can identify the direction of the wind
along the orientation lines. If the SAR image includes a coastline, the shadow effect from land
may be visible, hence revealing the wind direction. Recent work on using the Doppler Centroid
anomaly for more accurate wind direction retrieval is promising but not yet applicable for all
SAR data (Mouche et al., 2013).
Reverting to external sources of information on wind direction, one option is wind direction
from scatterometry. The method requires nearly simultaneous overpasses of a SAR and
scatterometer, which becomes more practical with increasing latitudes (Monaldo et al., 2004;
He et al., 2005).
Foroperationalnearreal-timeprocessingofSARscenesintowindmaps,themostfrequently
used external source is a priori wind direction from atmospheric models such as ECMWF (European
Centre for Medium-range Weather Forecasting), NOGAPS (Navy Operational Global
Atmospheric Prediction System), WRF (Weather Research and Forecasting) or similar.
At offshore sites with high-quality meteorological data, the locally observed wind direction
has been tested for use as an input. The wind speeds were retrieved with a standard deviation
error as low as ±1.1 ms−1 (Hasager et al., 2004; Hasager et al., 2005; Hasager et al., 2006;
278 DTU Wind Energy-E-Report-0029(EN)