Inconsistencies in scatterometer wind products based on ASCAT and OSCAT-2 collocations

Wang, Zhixiong; Stoffelen, Ad; Zhang, Biao; He, Yijun; Lin, Wenming; Li, Xiuzhong

doi:10.1016/j.rse.2019.03.005

Cited by 27 publications

(19 citation statements)

References 28 publications

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“…Note, that there are other Ku-band GMFs from other data providers. Wang et al (2017b) recently presented a new GMF for Ku band, which corrects for SSTs, named NSCAT-5. Another GMF, which accounts for SSTs, is used for RapidSCAT, a scatterometer mounted on the International Space Station (ISS).…”

Section: Scatterometer Datamentioning

confidence: 99%

“…The second scatterometer data product is ASCAT-A, also obtained from RSS. Note, there are several different products with different quality using the same original data (e.g., Wang et al 2019). ASCAT is a scatterometer onboard the MetOp-A satellite and operating in C band.…”

Section: Scatterometer Datamentioning

confidence: 99%

“…A correction due to misrepresented viscosity, related to SSTs, was investigated following Bentamy et al (2012) (not shown). In a recent paper, Wang et al (2017a) showed the dependence of the SST effect on radar frequency, polarization, and the incidence angle. The temporal variations in water viscosity are important for scatterometers because this affects the wave behavior at the centimeter scales that dominate the radar backscatter.…”

Section: ) Viscositymentioning

confidence: 99%

“…The GMFs for Ku band are also SST dependent (see, e.g., Wang et al 2017b), which is neglected in the Ku-2011 GMF used here. The SST varies at the Stratus site seasonally between 188 and 238C, at NTAS between 258 and 298C, and at WHOTS between 238 and 288C.…”

Section: ) Gmf and Scatterometer Errorsmentioning

confidence: 99%

“…The first scatterometer we use, operational from 1999 until 2009 in Ku band, is NASA QuikSCAT (Lungu and Callahan 2006) with its most current GMF: ''QuikSCAT Ku-2011'' (Ricciardulli and Wentz 2015). Neglect of a sea surface temperature (SST) dependency in the wind stress relationship can be a large error source (see, e.g., Wang et al 2017b). Generally, pencil-beam scatterometers, like QuikSCAT, can have systematic wind direction biases, as shown, for example, by Stoffelen and Portabella (2006).…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of Wind Observations from Open-Ocean Buoys: Correction for Flow Distortion

Schlundt

Farrar

Bigorre

et al. 2020

Journal of Atmospheric and Oceanic Technology

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The comparison of equivalent neutral winds obtained from (i) four WHOI buoys in the subtropics and (ii) scatterometer estimates at those locations reveals a root-mean-square (RMS) difference of 0.56–0.76 m s−1. To investigate this RMS difference, different buoy wind error sources were examined. These buoys are particularly well suited to examine two important sources of buoy wind errors because 1) redundant anemometers and a comparison with numerical flow simulations allow us to quantitatively assess flow distortion errors, and 2) 1-min sampling at the buoys allows us to examine the sensitivity of buoy temporal sampling/averaging in the buoy–scatterometer comparisons. The interanemometer difference varies as a function of wind direction relative to the buoy wind vane and is consistent with the effects of flow distortion expected based on numerical flow simulations. Comparison between the anemometers and scatterometer winds supports the interpretation that the interanemometer disagreement, which can be up to 5% of the wind speed, is due to flow distortion. These insights motivate an empirical correction to the individual anemometer records and subsequent comparison with scatterometer estimates show good agreement.

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Section: Scatterometer Datamentioning

confidence: 99%

Section: Scatterometer Datamentioning

confidence: 99%

Section: ) Viscositymentioning

confidence: 99%

Section: ) Gmf and Scatterometer Errorsmentioning

confidence: 99%