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Calibration and Validation of Scatterometer Data

A wide variety of wind scatterometers have been and developed from the U.S., ESA, India, and China (see Figure). Wind scatterometer directly measure the normalized radar cross section (NRCS, sometimes called "sigma-0" or backscatter) of the Earth's surface [Ulaby and Long, 2014]. Originally, scatterometer measurements were used only to determine (retrieve) near-surface wind (speed and direction) over the ocean. However, the backscatter is valuable in a wide number of other applications, including measuring the extent of sea ice [Hill and Long, 2017] [Remund and Long, 2014] [Haarpainter et al., 2004], sea ice age [Swan and Long, 2012], evaluting the freeze/thaw state of snow and ice [Long, 2017], mapping iceberg positions [Budge and Long, 2017] [Stuart and Long, 2011], measuring rain [Owen and Long, 2011][Nie and Long, 2008] [Allen and Long, 2005], evaluating vegetation vigour [Li et al. 2017], measuring winds over sand [Stephen and Long, 2007] and snow dunes [Long and Drinkwater, 2000] , among other things. With their rapid global coverage, day or night and all-weather operation, scatterometers offer a unique tool for long-term climate studies.

There are two primary components to scatterometer calibration and validation: (1) backscatter calibration and (2) derived product validation. The latter includes scatterometer wind calibration. These are well-understood problems whose solutions have been standardized over the past few decades. While early scatterometer systems required extensive calibration/validation campaigns to develop new parametersizations of backscatter versus wind, building on these successfull ealier missions, newer scatterometers have been deployed with much smaller calibration and validation efforts.

Scatterometer Backscatter Calibration

In order to use existing geophysical model functions (parameterizations) that relate backscatter to wind, as well as to use the backscatter in long-term climate studies, wind scatterometer sigma-0 measurements are carefully calibrated. This is a widely studied issue for which there is an extensive literature. Multiple standard techniques have been used including ground-based transponders, but most rely on the used of extended areas of homogenous vegetation such as the Amazon rainforest, for example, [Madsen and Long, 2016] [Anderson et al., 2012] [Tsai et al., 1999] [Long and Skouson, 1995]. Other areas have been used, including other rain forests, deserts, tundra, and ice sheets [Moon and Long, 2012]. Backscatter calibration has also been done using ocean backscatter [Portabella 2007] [Verspeek et al. 2008].

Validation scatterometer backscatter resolution enhancement is accomplished by both modelling and actual data, this is covered in a number of papers, including [Long, 2017] [Lindsley and Long, 2016] [Early and Long, 2001] . The availabity of contemporanous scatterometers has made cross-calibration of sigma-0 from difference sensors, and validation of the sigma-0 resolution enhancement, much easier in recent times.

In summary, well-established backcatter calibration techniques enable very precise calibration of the radar backscatter measurements to of order 0.1 dB over mission life.

Scatterometer Wind Validation

The validation of high resolution remote sensing wind measurements using a combination of wind buoys (which are only point measurements) and models (which have resolution limitations) is one of the classic problems of wind vector scatterometry. The scatterometer wind community has developed multiple techniques that are standard and well documented in the literature that allow the validation of scatterometer winds [Wentz et al., 2017]. For instance, both the NASA QuikSCAT and EUMETSAT ASCAT instruments have been validated using lower resolution ECMWF and/or NOAA GFS models, combined with buoy point observations. The classical method adopted by the community is to use triple collocation [Stoffelen, 1998] and extended triple collocation [McColl et al., 2014] between three data sets to arrive at a relative allocation of error. Additional techniques for using lower resolution models for validation is to examine the wind spectrum and structure functions of coincident data [Vogelzang et al., 2015]. There is cross-spectral coherence between the model and data at wavelengths the model can resolve, while at higher frequencies (smaller wavelengths), the model spectrum (or structure function) is reduced in power. The validity of higher frequency signals can be assessed with the continuation of the spectrum following a power-law, a behavior that has been validated with high resolution observations over a variety of scales (e.g., [Wikle et al., 1999] [Lindsley et al., 2016] ).

Anextensive list of scatterometer wind validation papers is available from FSU's Center for Ocean-Atmospheric Prediction Studies (COAPS) www.coaps.fsu.edu/scatterometry/bibliography.

Conventional resolution scatterometer winds are available from a number international sources, including the NASA/JPL Physical Oceanography Distributed Active Archive podaac.jpl.nasa.gov, NOAA Center for Saltellite Application and Research manati.star.nesdis.noaa.gov, Remote Sensing Systems www.remes.com, Wamong others. The Scatterometer Climate Record Pathfinder www.scp.byu.edu generates ultra-high resolution scatterometer winds based on enhanced resolution backscatter measurements. These products have been validated in a number of studies including [Plagge et al., 2008] [Lindsley et al., 2016] [Hutchings et al., 2018]

Validation of Other Scatterometer-Derived Products

A wide variety of non-wind (e.g., rain [Owen and Long, 2011][Nie and Long, 2008] [Allen and Long, 2005], sea ice extent [Hill and Long, 2017] [Remund and Long, 2014] [Haarpainter et al., 2004], and sea ice age [Swan and Long, 2012] ) scatterometer-derived products have been developed and validated. A partial list of publications on scatterometer-derived products is provided on the Scatterometer Climate Record Pathfinder publications site https://www.scp.byu.edu/pub.html,