Ocean model validation of the NASA scatterometer winds

Verschell, M.A.; Bourassa, Mark A.; Weissman, David E.; O'Brien, J. J.

doi:10.1029/1998jc900105

Cited by 29 publications

(26 citation statements)

References 37 publications

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“…The OAFLUX is derived from wind speed, sea and T air , and humidity from NCEPR2, NCEPR1, and ERA-40, combined with satellite retrievals of wind speed and humidity from SSM/I and the SeaWinds scatterometer on QuikSCAT, and sea temperature from Advanced Very High Resolution Radiometer (AVHRR), Tropical Rainfall Measuring Mission Microwave Imager (TMI), Advanced Microwave Scanning Radiometer -E. The OAFLUX method synthesises the satellite observations and NWP outputs using a variational objective analysis to determine the best fit for the four independent variables (wind speed, air and sea temperature, and specific humidity). Earlier versions of the OAFLUX product incorrectly treated satellite winds, which are calibrated to equivalent neutral winds (Verschell et al, 1999), as actual winds (there are stability dependent differences), resulting in regional biases in wind speed. One clear benefit of this product comparison has been several improvements of the OAFLUX product (e.g.…”

Section: Hybridmentioning

confidence: 99%

A comparison of nine monthly air–sea flux products

Smith

Hughes

Bourassa

2010

Intl Journal of Climatology

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A comparison is conducted between nine monthly turbulent air-sea flux products. The analysis includes in situ-based (Florida State University fluxes, FSU3 and National Oceanography Centre, NOC), satellite-based (Hamburg Ocean-Atmosphere Parameters from Satellite data, HOAPS2 and the French Research Institute for Exploitation of the Sea, IFREMER), hybrid (Goddard Satellite-based Surface Turbulent Fluxes, GSSTF2 and objectively analysed fluxes, OAFLUX), and reanalysis (National Centers for Environmental Prediction, NCEPR2, Japanese 25-year reanalysis, JRA, and European Centre for Medium Range Weather Forecasts reanalysis, ERA-40) products. Objectives include documenting the varying analysis methodologies and quantifying the differences and similarities between the nine products. Recommendations are made for developers of future flux products and to guide users to select products most suitable for their application.The comparison examines turbulent fluxes of heat and momentum along with the forcing variables (air temperature, wind speed, humidity, and ocean skin temperature) that are necessary to estimate turbulent fluxes. The wide range of turbulent flux parameterisations, sampling patterns, and averaging techniques within the products are described, including some of the difficulties product differences pose when trying to compare or apply the individual products. Global comparisons of monthly means tend to reveal similar spatial patterns in latent heat flux (LHF) and sensible heat flux (SHF) for the nine products; however, the magnitudes and patterns of variability (expressed as maps of standard deviations) are widely different. Basin scale and regional analysis further reveals large differences in the products (in some cases the interquartile ranges (IQRs) do not overlap for different products), but also reveals potential sources of the differences. For example, some of the variations in LHF can be explained by large differences in the distribution of specific humidity between the products. As a final analysis, we examine how each product represents the variations in turbulent fluxes in the equatorial Pacific (EP) Ocean. This analysis provides an example of how the choice of a flux product, and understanding the strengths and weaknesses of that product, can alter research findings.

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Section: Hybridmentioning

confidence: 99%

A comparison of nine monthly air–sea flux products

Smith

Hughes

Bourassa

2010

Intl Journal of Climatology

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“…The scatterometer winds are equivalent neutral winds [Cardone, 1969;Ross et al, 1985;Cardone et al, 1996;Verschell et al, 1999], which means that atmospheric stratification does not need to be considered in the conversion between winds and stress. The resultant surface wind stress fields are then interpolated, using bicubic splines, to a 0.05°Â 0.05°grid for later use with an ocean model.…”

Section: à3mentioning

confidence: 99%

“…Similar scatterometer data sets have thus been used for large-scale ocean modeling studies JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110, C10024, doi:10.1029/2004JC002338, 2005 [Verschell et al, 1999;Milliff et al, 1999;Chu et al, 2000]. However, the QuikSCAT data are not generally available within 30 km of land and can be questionable near precipitation [Weissman et al, 2002]; therefore the data have not been used extensively for coastal and regional ocean modeling.…”

Section: Introductionmentioning

confidence: 99%

Remotely sensed winds for episodic forcing of ocean models

et al. 2005

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[1] A new method is described for forcing regional ocean models with wind stress fields derived from satellite scatterometer data. A variational technique is applied to produce regularly gridded surface wind (stress) fields in time and space using data from the NASA SeaWinds scatterometer aboard the QuikSCAT satellite. Three uniformly gridded wind stress products are produced with satellite scatterometer data for the Gulf of Mexico, one based solely on scatterometer data and the other two constrained to a background field derived from numerical weather prediction (NWP) models. These, along with winds from the Eta-29 NWP model, are used to force a high-resolution ocean model of the Gulf of Mexico. The four wind products are compared to National Data Buoy Center in situ observations. Ocean model data are compared to coastal sea level stations and moored acoustic Doppler current profiler data over the West Florida Shelf. The results show that the satellite products capture much of the variability at atmospheric synoptic scales and show great promise for representing energetic episodic events such as tropical cyclones. The techniques presented in this work are applicable to improving numerical ocean simulations of inaccessible or data-sparse regions of the world.

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“…Greenaert and Katsaros [3] define ENWS as the mean wind speed in a neutrally stratified atmosphere. In contrast, Tang and Liu [1] and Verschell et al [4] define ENWS as the wind speed based on the stress and roughness length consistent with the observed atmospheric stratification. Since the surface roughness measured by the scatterometer is more closely related to ocean surface stress than wind speed [4], [5], the latter definition of ENWS is more consistent with scatterometry.…”

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confidence: 99%

“…In contrast, Tang and Liu [1] and Verschell et al [4] define ENWS as the wind speed based on the stress and roughness length consistent with the observed atmospheric stratification. Since the surface roughness measured by the scatterometer is more closely related to ocean surface stress than wind speed [4], [5], the latter definition of ENWS is more consistent with scatterometry. Wind stress is the most important forcing in the tropics [6] because ocean surface currents are driven by wind stress.…”

mentioning

confidence: 99%

Estimating Wind Stress at the Ocean Surface From Scatterometer Observations

Ali

Bhat

Long

et al. 2013

IEEE Geosci. Remote Sensing Lett.

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Abstract-Wind stress is the most important ocean forcing for driving tropical surface currents. Stress can be estimated from scatterometer-reported wind measurements at 10 m that have been extrapolated to the surface, assuming a neutrally stable atmosphere and no surface current. Scatterometer calibration is designed to account for the assumption of neutral stability; however, the assumption of a particular sea state and negligible current often introduces an error in wind stress estimations. Since the fundamental scatterometer measurement is of the surface radar backscatter (sigma-0) which is related to surface roughness and, thus, stress, we develop a method to estimate wind stress directly from the scatterometer measurements of sigma-0 and their associated azimuth angle and incidence angle using a neural network approach. We compare the results with in situ estimations and observe that the wind stress estimations from this approach are more accurate compared with those obtained from the conventional estimations using 10-m-height wind measurements.

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Ocean model validation of the NASA scatterometer winds

Cited by 29 publications

References 37 publications

A comparison of nine monthly air–sea flux products

A comparison of nine monthly air–sea flux products

Remotely sensed winds for episodic forcing of ocean models

Estimating Wind Stress at the Ocean Surface From Scatterometer Observations

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