Publishers version - DTU Orbit

educe speckle noise, a random noise in SAR data, and reduce effects of longer-period ocean
waves.
It is possible to use the NEST open source toolbox from ESA (nest.array.ca) for calibration
and geo-coding. NEST may also be used for wind retrieval, yet without many options.
The commercial software SARTool from CLS offers various options for wind retrieval. It is
used operationally by KSAT, EDISOFT and CLS at VIGISAT (www.vigisat.eu) to process
data into wind fields in near real time. These data can be viewed on EODA GIS technology
based web portal (eoda.cls.fr) with various examples on SAR applications. At Johns
Hopkins University, Applied Physics Laboratory (JHU APL), the APL/NOAA SAR Wind Retrieval
System (ANSWRS) wind retrieval software was developed. It is implemented at NOAA
(National Oceanic and Atmospheric Administration), the Alaska SAR facility and DTU Wind
Energy. Both SARTool and ANSWRS provide near real-time (NRT) SAR wind retrieval facility.
In SARTool the ECMWF wind directions typically are used a priori while in the ANSWRS
the NOGAPS wind directions are used a priori. It is possible to use other wind direction input
in both.
Wind retrieval is onlypossible over open water such as ocean and large lakes. It is necessary
to mask out land and sea ice before wind inversion. Hard targets such as large ships, oil and
gas rigs, wind turbines, long bridges and other structures in the ocean should ideally also be
masked out. This is not implemented yet but work is on-going to provide this (A. Mouche
pers.com).Until then these hard targets contributenoise to the wind retrieval and mayresult
in spurious high wind observations.
Another important issue to consider is the ocean surface. To first order the ocean surface
canbedescribedasmodulatedbythesurfacewind.However,lookingintogreaterdetailawide
range of phenomena influences the sea surface and hence the radar backscatter. Obviously,
oil spill and oil slicks that are operationally monitored from SAR are visible. Ocean surface
surfactants, oil and algae blooms dampen the radar backscatter resulting in winds which
are too low. More complicated is the effect of oceanic processes of currents, fronts, eddies,
swell and internal waves. The surface wave spectrum is modulated and generally speaking
the NRCS modulation is positive for current convergence and negative for current divergence
but also a function of radar frequency, local wind velocity, etc. (see Badger et al., 2008 for
references). The use of Doppler Centroid anomaly may reveal some of these phenomena and
may in combination with GMF based wind retrieval improve SAR wind retrieval.
Open access to SAR scenes is provided from ESA (earth.esa.int). The SAR scenes are
stored in a searchable archive and may be accessed as Level 1 data with permission. In the
archive many scenes can be inspected visually from Quicklook images. The image frames over
a given site have different spatial coverage and orientations. The orbital characteristics reflect
whether a particular frame is from a descending or an ascending track and the approximate
local overpass time is given from this. At present the user has to perform this digital image
processing before wind inversion. It is anticipated that wind fields (Level 2) will become
available from Sentinel-1. This will be a major step forward making SAR wind maps more
useful for end-users.
SAR retrieved winds have been validated by comparing to observations from meteorological
masts, ships, buoys, scatterometer or atmospheric model results. In general, the validation
resultsshowabiaslessthan0.5ms −1 andstandarddeviationfrom 1.2to2.0ms −1 .Pleasesee
Dagestad et al. 2013 and references herein. The accuracy of collocation, the uncertainty on
the in-situ observations and atmospheric model results, and also the time-averaging method
have to be considered. The fact is that satellite SAR provides spatial statistics and in-situ
data are time-averaged statistics. The hypothesis of frozen turbulence (Taylor’s hypothesis)
is generally used. It is important to ensure that the spatial and temporal scales are adequate.
The SAR calibration of 0.5 dB gives an uncertainty on wind speed of roughly 0.5 ms −1 . As
the standard deviation from the validation results is found to be much larger, around two to
four times the calibration uncertainty, there is scope for improvement in SAR wind retrieval.
DTU Wind Energy-E-Report-0029(EN) 281