Statistical distribution of wind speeds and directions globally observed by NSCAT

Ebuchi, Naoto

doi:10.1029/98jc02061

Cited by 35 publications

(19 citation statements)

References 16 publications

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“…The differences between the monthly QSCAT‐RSS and ECMWF winds are relatively minor, representing a very small bias of 0.02 m/s for the study period. This Southern Ocean bias indicates that QuikSCAT observed stronger wind speeds than did model simulations, in contrast with earlier studies showing a global mean bias between NSCAT and ECMWF (approximately −0.1 to −0.2 m/s) and NCEP/NCAR reanalysis (−0.1 m/s) [ Atlas et al , 1999; Ebuchi , 1999]. Among different wind products, QSCAT‐JPL monthly winds are only slightly higher than QSCAT‐RSS winds while ECMWF winds are slightly stronger than NCEP/NCAR winds in general.…”

Section: Comparison With Simulated Wind Productscontrasting

confidence: 88%

“…Earlier studies [ Atlas et al , 1999; Ebuchi , 1999] have shown that NSCAT winds are in good agreement with ECMWF and NCEP/NCAAR reanalysis winds in terms of a global average. Moreover, the NSCAT provides more spatial structures than the model simulations because of its higher spatial resolution [ Liu et al , 1998].…”

Section: Comparison With Simulated Wind Productsmentioning

confidence: 59%

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High‐wind‐speed evaluation in the Southern Ocean

Yuan

2004

J. Geophys. Res.

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[1] Space-based scatterometer instruments provide crucial surface wind measurements with high resolution over global oceans. Midlatitude regions in the Southern Ocean are unique places to evaluate scatterometer winds at high-wind bands because these regions host the strongest wind fields at the ocean surface. The objective of this study is to evaluate high wind speeds observed by Quick Scatterometer (QuikSCAT) wind measurements and produced by simulation models and compare them with weather station data in the Southern Ocean. The occurrence and intensity of high-wind events in scatterometer measurements are compared with that of reanalysis winds, and the spatial and seasonal variability of high-wind characteristics is examined. The results show that the speeds of scatterometer winds are similar to model simulations in the monthly mean field but consistently stronger than both European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction/National Center for Atmospheric Research winds in high-wind bands. When scatterometer winds are compared with the weather station observations at Macquarie Island, the present study finds no systematic bias at high-wind bands across all months. However, both weather station and QuikSCAT winds are higher than the model simulations in high-wind bands most of the time. This suggests that model simulations may underestimate surface wind strength in high-wind bands. Such underestimation would lead to up to an 80% reduction in energy flux between the atmosphere and ocean. Even though high winds occur only sporadically and the reanalysis underestimation in high wind speed is not in itself of great magnitude, they have a significant impact on global climate.

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Section: Comparison With Simulated Wind Productscontrasting

confidence: 88%

Section: Comparison With Simulated Wind Productsmentioning

confidence: 59%

High‐wind‐speed evaluation in the Southern Ocean

Yuan

2004

J. Geophys. Res.

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“…For this study, PDFs are used to estimate the mean, variance, skewness, and kurtosis of the wind fields. This differs from some previous applications of PDFs for scatterometry, which have looked at wind speed (rather than vector wind stress) in order to derive improved model functions and to quality control data (e.g., Freilich and Challenor 1994;Bauer 1996;Ebuchi 1999). Extreme events may occur in one data product but not others, because anomalous events are edited out of some data products.…”

Section: Probability Density Functions: Characterizing Extreme Eventsmentioning

confidence: 57%

Statistical Characterization of Zonal and Meridional Ocean Wind Stress

Gille

2005

Journal of Atmospheric and Oceanic Technology

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Four years of ocean vector wind data are used to evaluate statistics of wind stress over the ocean. Raw swath wind stresses derived from the Quick Scatterometer (QuikSCAT) are compared with five different global gridded wind products, including products based on scatterometer observations, meteorological analysis winds from the European Centre for Medium-Range Weather Forecasts, and reanalysis winds from the National Centers for Environmental Prediction. Buoy winds from a limited number of sites in the Pacific Ocean are also considered.Probability density functions (PDFs) computed for latitudinal bands show that mean wind stresses for the six global products are largely in agreement, while variances differ substantially, by a factor of 2 or more, with swath wind stresses indicating highest variances for meridional winds and for zonal winds outside the Tropics. Higher moments of the PDFs also differ. Kurtoses are large for all wind products, implying that PDFs are not Gaussian. None of the available gridded products fully captures the range of extreme wind events seen in the raw swath data.Frequency spectra for the five gridded products agree with frequency spectra from swath data at low frequencies, but spectral slopes differ at higher frequencies, particularly for frequencies greater than 100 cycles per year (cpy), which are poorly resolved by a single scatterometer. In the frequency range between 10 and 90 cpy that is resolved by the scatterometer, spectra derived from swath data are flatter than spectra from gridded products and are judged to be flatter than Ϫ2/3 at all latitudes.

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“…Potential sources of relative bias between the different functions are systematic differences in the estimation of whitecap fraction and wind speed via different techniques, and from averaging over different ranges or probability distributions of secondary factors. We note that the best agreement between all the different functions is for winds in the range 5–10 m s −1 , those most commonly encountered over the ocean [ Ebuchi , ]. Both the satellite winds and whitecap fraction estimates are spatial averages, while in situ winds are time averages at a single point and in situ whitecap fractions are joint time and spatial averages, albeit over a much more restricted area.…”

Section: Discussionmentioning

confidence: 99%

On the variability of whitecap fraction using satellite-based observations

Salisbury

Anguelova

Brooks

2013

J. Geophys. Res. Oceans

101

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[1] Despite decades of effort to accurately quantify whitecap fraction W using in situ photography of the ocean surface, there remains significant scatter in estimates for any given 10 m wind speed (U 10 ). It is believed that the resulting, commonly used, W(U 10 ) parameterizations do not fully account for the true variability in W, by failing to incorporate the impact of the wavefield and other environmental conditions. This paper attests to the variability in whitecap fraction attributed to these additional factors, by analyzing satellitederived W estimates over the globe for a full year. A comparison is made between the wind speed dependence of satellite estimates and three W(U 10 ) relationships formulated from in situ photographic data. The influence of various secondary factors on W is investigated once the dominant wind speed dependence is accounted for. The W retrieval's sensitivity to secondary forcings is dependent upon microwave frequency; at 37 GHz it varies by up to 25% of the mean at a given wind speed, while at 10 GHz it is a maximum of 8%. This results from a frequency-dependent sensitivity to foam depth; at 10 GHz predominantly foam from active breaking waves is detected, while at 37 GHz thin foam in residual whitecaps is also seen. Principal component analysis is used to rank variables by their success in accounting for variability in W. After wind speed, the most important secondary factor that accounts for variability in W is the wavefield. A wind-wave Reynolds number accounts for almost as much variability in W as wind speed.

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Statistical distribution of wind speeds and directions globally observed by NSCAT

Abstract: Abstract.In

Cited by 35 publications

References 16 publications

High‐wind‐speed evaluation in the Southern Ocean

High‐wind‐speed evaluation in the Southern Ocean

Statistical Characterization of Zonal and Meridional Ocean Wind Stress

On the variability of whitecap fraction using satellite-based observations

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