Aspects of the determination of winds by means of scatterometry and of the utilization of vector wind data for meteorological forecasts

Pierson, Willard J.; Sylvester, Winfield B.; Donelan, Mark A.

doi:10.1029/jc091ic02p02263

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…At least one of the key aspects here appears to be the asynoptic use of the NSCAT data. This has been emphasized by Pierson et al [1986] and seems to be required to get a regular positive impact in the northern hemisphere. In addition to the above experiments, further research is being performed to optimize the use of NSCAT wind data in combination with other remotely sensed data, including ERS 2 scatterometer winds and SSM/I wind speeds.…”

Section: Summary and Concluding Remarksmentioning

confidence: 99%

Geophysical validation of NSCAT winds using atmospheric data and analyses

Atlas¹,

Bloom²,

Hoffman³

et al. 1999

J. Geophys. Res.

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Abstract. A detailed geophysical evaluation of the initial NASA scatterometer (NSCAT) wind data sets was performed in order to determine the error characteristics of these data and their applicability to ocean surface analysis and numerical prediction. The first component of this evaluation consisted of collocations of NSCAT data to ship and buoy wind reports, special sensor microwave imager wind observations, and National Centers for Environmental Prediction and Goddard Earth Observing System (GEOS) model wind analyses. This was followed by data assimilation experiments to determine the impact of NSCAT data on analysis and forecasting. The collocation comparisons showed the NSCAT wind velocity data to be of higher accuracy than operational ERS 2 wind data. The impact experiments showed that NSCAT has the ability to correct major errors in analyses over the oceans and also to improve numerical weather prediction. NSCAT data typically show the precise locations of both synoptic-scale and smaller-scale cyclones and fronts over the oceans. This often results in significant improvements to analyses. Forecast experiments using the GEOS model show approximately a 1-day extension of useful forecast skill in the southern hemisphere, in good agreement with the results of Observing System Simulation Experiments conducted prior to launch. IntroductionOcean surface winds are the most important factor coupling the ocean and atmosphere. Surface winds control the fluxes of momentum, heat, and moisture at the atmosphere-ocean interface. They are key to the scientific understanding of climate and to operational forecasting at the long range (1 month to 1 year). Surface wind data are also needed for nowcasting conditions at sea, for wave forecasting, for providing boundary conditions for ocean general circulation models, and for refining the analyses of ocean surface wind which are part of the initial conditions for numerical weather prediction (NWP).The ocean is vast; ships and buoys are few and irregularly located. Satellite microwave remote sensing of the ocean surface now provides a complete and accurate depiction of the surface wind over the ocean. The focus of this paper is the validation of the wind vectors observed by the NASA scatterometer (

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Section: Summary and Concluding Remarksmentioning

confidence: 99%

Geophysical validation of NSCAT winds using atmospheric data and analyses

Atlas¹,

Bloom²,

Hoffman³

et al. 1999

J. Geophys. Res.

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“…The remote sensing of atmospheric and oceanic parameters from satellites has recently become important in understanding and constraining the global-scale distributions of various physical properties [Sandwell and Agreen, 1984; Black and Eisner, 1986;Pierson et al, 1986;Monahah and Muircheartaigh, 1986]. Many of the presently employed remote sensing techniques use electromagnetic radiation to probe the atmosphere and surface ocean [Zheng et al, 1983;Brown, 1986].…”

Section: Introductionmentioning

confidence: 99%

Seasonal estimates of global oceanic whitecap coverage

Erickson¹,

Merrill²,

Duce³

1986

J. Geophys. Res.

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Seasonal estimates of oceanic whitecap coverage are presented on global contour maps. The instantaneous fraction of the sea surface covered by whitecaps as a function of wind speed has been described previously using a power law fitting technique [Monahan and Muircheartaigh, 1980]. This relationship, coupled with a Gaussian wind speed frequency distribution, allows us to calculate the oceanic whitecap coverage accounting for the variance about mean wind speeds. We use monthly mean wind speed and variance information in 5° latitude × 5° longitude areas over the world ocean to estimate the global oceanic whitecap coverage distribution. The whitecap coverage in the northern hemisphere oceans displays a substantial seasonal dependence. The boreal summer average whitecap coverage in the high‐latitude North Atlantic is about 0.5% and increases by a factor of roughly 6 to over 3.0% for a boreal winter average. The whitecap coverage in the high‐latitude southern hemisphere oceans exhibits only a factor of 3 difference for the same seasons, from about 1% in the austral summer to over 3% for the austral winter. There are several high‐latitude areas with greater than 3% average whitecap coverage over several months of the year. The monsoonal winds in the Indian Ocean result in over 3% mean whitecap coverage for the June‐July‐August period.

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“…One problem with current formulations is an inconsistency between the parameterizations for neutral wind and friction velocity. The current choices of power law relations to backscatter for both are not compatible with the expected connection between the two for a neutrally stratified atmosphere (Pierson et al, 1986).…”

Section: Empirical U_to-qo Relationmentioning

confidence: 68%

Comparison study of SEASAT scatterometer and conventional wind fields

Holderied¹

1988

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Chapter 1: Introduction. Chapter 2: Background. Wind velocity and wind stress. Ocean wave spectrum and radar backscatter. Empirical U-to-aO relation.. .. . Chapter 3: SEASAT Scatterometer (SASS). SASS geophysical algorithm.. SASS performance evaluations. SASS data reprocessing. Chapter 4: Data description and handling. Objective mapping procedure Chapter 5: Results and discussion. Field statistics.. .. . Ungridded Atlas winds. Mapped Atlas winds. FNOC winds. Boxed mean differences.. Scatter plots of wind speed and direction. Difference field statistics Wind stress curl.. .. .. .

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Aspects of the determination of winds by means of scatterometry and of the utilization of vector wind data for meteorological forecasts

Cited by 13 publications

References 25 publications

Geophysical validation of NSCAT winds using atmospheric data and analyses

Geophysical validation of NSCAT winds using atmospheric data and analyses

Seasonal estimates of global oceanic whitecap coverage

Comparison study of SEASAT scatterometer and conventional wind fields

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