A third-generation numerical wave model to compute random, short-crested waves in coastal regions with shallow water and ambient currents (Simulating Waves Nearshore (SWAN)) has been developed, implemented, and validated. The model is based on a Eulerian formulation of the discrete spectral balance of action density that accounts for refractive propagation over arbitrary bathymetry and current fields. It is driven by boundary conditions and local winds. As in other third-generation wave models, the processes of wind generation, whitecapping, quadruplet wave-wave interactions, and bottom dissipation are represented explicitly. In SWAN, triad wave-wave interactions and depth-induced wave breaking are added. In contrast to other third-generation wave models, the numerical propagation scheme is implicit, which implies that the computations are more economic in shallow water. The model results agree well with analytical solutions, laboratory observations, and (generalized) field observations.

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A third-generation wave model for coastal regions: 1. Model description and validation

The monsoon circulation of the Indian Ocean

The air–sea fluxes of momentum, heat, freshwater and their components have been computed globally from 1948 at frequencies ranging from 6-hourly to monthly. All fluxes are computed over the 23 years from 1984 to 2006, but radiation prior to 1984 and precipitation before 1979 are given only as climatological mean annual cycles. The input data are based on NCEP reanalysis only for the near surface vector wind, temperature, specific humidity and density, and on a variety of satellite based radiation, sea surface temperature, sea-ice concentration and precipitation products. Some of these data are adjusted to agree in the mean with a variety of more reliable satellite and in situ measurements, that themselves are either too short a duration, or too regional in coverage. The major adjustments are a general increase in wind speed, decrease in humidity and reduction in tropical solar radiation. The climatological global mean air–sea heat and freshwater fluxes (1984–2006) then become 2 W/m2 and −0.1 mg/m2 per second, respectively, down from 30 W/m2 and 3.4 mg/m2 per second for the unaltered data. However, decadal means vary from 7.3 W/m2 (1977–1986) to −0.3 W/m2 (1997–2006). The spatial distributions of climatological fluxes display all the expected features. A comparison of zonally averaged wind stress components across ocean sub-basins reveals large differences between available products due both to winds and to the stress calculation. Regional comparisons of the heat and freshwater fluxes reveal an alarming range among alternatives; typically 40 W/m2 and 10 mg/m2 per second, respectively. The implied ocean heat transports are within the uncertainty of estimates from ocean observations in both the Atlantic and Indo-Pacific basins. They show about 2.4 PW of tropical heating, of which 80% is transported to the north, mostly in the Atlantic. There is similar good agreement in freshwater transport at many latitudes in both basins, but neither in the South Atlantic, nor at 35°N.

The global climatology of an interannually varying air–sea flux data set

Boussinesq‐type equations can be used to model the nonlinear transformation of surface waves in shallow water due to the effects of shoaling, refraction, diffraction, and reflection. Different linear dispersion relations can be obtained by expressing the equations in different velocity variables. In this paper, a new form of the Boussinesq equations is derived using the velocity at an arbitrary distance from the still water level as the velocity variable instead of the commonly used depth‐averaged velocity. This significantly improves the linear dispersion properties of the Boussinesq equations, making them applicable to a wider range of water depths. A finite difference method is used to solve the equations. Numerical and experimental results are compared for the propagation of regular and irregular waves on a constant slope beach. The results demonstrate that the new form of the equations can reasonably simulate several nonlinear effects that occur in the shoaling of surface waves from deep to shallow w...

Alternative form of Boussinesq equations for nearshore wave propagation

[1] Objective measures of climate model performance are proposed and used to assess simulations of the 20th century, which are available from the Coupled Model Intercomparison Project (CMIP3) archive. The primary focus of this analysis is on the climatology of atmospheric fields. For each variable considered, the models are ranked according to a measure of relative error. Based on an average of the relative errors over all fields considered, some models appear to perform substantially better than others. Forming a single index of model performance, however, can be misleading in that it hides a more complex picture of the relative merits of different models. This is demonstrated by examining individual variables and showing that the relative ranking of models varies considerably from one variable to the next. A remarkable exception to this finding is that the so-called “mean model” consistently outperforms all other models in nearly every respect. The usefulness, limitations and robustness of the metrics defined here are evaluated 1) by examining whether the information provided by each metric is correlated in any way with the others, and 2) by determining how sensitive the metrics are to such factors as observational uncertainty, spatial scale, and the domain considered (e.g., tropics versus extra-tropics). An index that gauges the fidelity of model variability on interannual time-scales is found to be only weakly correlated with an index of the mean climate performance. This illustrates the importance of evaluating a broad spectrum of climate processes and phenomena since accurate simulation of one aspect of climate does not guarantee accurate representation of other aspects. Once a broad suite of metrics has been developed to characterize model performance it may become possible to identify optimal subsets for various applications.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2007JD008972

Performance metrics for climate models

Four-year averages of 25-kilometer-resolution measurements of near-surface wind speed and direction over the global ocean from the QuikSCAT satellite radar scatterometer reveal the existence of surprisingly persistent small-scale features in the dynamically and thermodynamically important curl and divergence of the wind stress. Air-sea interaction over sea surface temperature fronts throughout the world ocean is evident in both the curl and divergence fields, as are the influences of islands and coastal mountains. Ocean currents such as the Gulf Stream generate distinctive patterns in the curl field. These previously unresolved features have important implications for oceanographic and air-sea interaction research.

https://science.sciencemag.org/content/sci/303/5660/978.full.pdf

Satellite measurements reveal persistent small-scale features in ocean winds.

Satellite measurements of surface wind stress from the QuikSCAT scatterometer and sea surface temperature (SST) from the Tropical Rainfall Measuring Mission Microwave Imager are analyzed for the three-month period 21 July‐20 October 1999 to investigate ocean‐atmosphere coupling in the eastern tropical Pacific. Oceanic tropical instability waves (TIWs) with periods of 20‐40 days and wavelengths of 1000‐2000 km perturb the SST fronts that bracket both sides of the equatorial cold tongue, which is centered near 1 8S to the east of 1308W. These perturbations are characterized by cusp-shaped features that propagate systematically westward on both sides of the equator. The space‐time structures of these SST perturbations are reproduced with remarkable detail in the surface wind stress field. The wind stress divergence is shown to be linearly related to the downwind component of the SST gradient with a response on the south side of the cold tongue that is about twice that on the north side. The wind stress curl is linearly related to the crosswind component of the SST gradient with a response that is approximately half that of the wind stress divergence response to the downwind SST gradient. The perturbed SST and wind stress fields propagate synchronously westward with the TIWs. This close coupling between SST and wind stress supports the Wallace et al. hypothesis that surface winds vary in response to SST modification of atmospheric boundary layer stability.

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Observations of coupling between surface wind stress and sea surface temperature in the Eastern Tropical Pacific

Scatterometry and scatterometer design issues are reviewed. The design of the NASA Scatterometer (NSCAT) to be flown on the Japanese ADEOS mission is presented. Building on Seasat experience, the NSCAT system includes several enhancements, such as three antenna azimuths in each of two swaths, and an onboard digital Doppler processor to allow backscatter measurements to be colocated everywhere within the orbit. These enhancements will greatly increase the quality of the NSCAT wind data. The ground processing of data is discussed, and scatterometers of the next decade are briefly described. >

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Spaceborne radar measurement of wind velocity over the ocean-an overview of the NSCAT scatterometer system

Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s 1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s 1 . While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s 1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

/pdf/scatterometer-based-assessment-of-10-m-wind-analyses-from-30uv94m69k.pdf

Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

The overall accuracies of vector wind measurements from the NASA scatterometer (NSCAT) are quantified by comparisons with collocated data from operational US National Data Buoy Center ocean buoys A vector correlation statistic is used to examine the geographical distribution of full-mission NSCAT-buoy differences The dependences of the vector correlation on collocation radius and scatterometer errors such as wind speed offset and random component errors are quantified For purposes of NSCAT validation, 30 ocean moored buoys having high full-mission sample correlations are considered When applied to this set of ocean buoys, a nonlinear analysis shows that the 25-km resolution NSCAT wind speeds have unity gain, an offset of −03 m s−1, an rms error of 13 m s−1, and component standard deviations of ∼13 m s−1 For wind speeds greater than 6 m s−1, fewer than 3% of all collocated solutions have significant ambiguity removal errors The rms directional difference for the remaining vectors is less than 17° At lower wind speeds the frequency of ambiguity removal errors increases and the directional accuracy of the “correct” vectors decreases with decreasing wind speed, consistent with the assumptions of the random error model underlying the nonlinear wind speed regression approach While the primary emphasis of this paper is on determining the quantitative accuracy of the NSCAT wind velocity measurements, the analysis techniques and interpretations have more general application to validation studies involving vector quantities

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/1998JC900091

Michael H. Freilich

Papers

Satellite measurements reveal persistent small-scale features in ocean winds.

Observations of coupling between surface wind stress and sea surface temperature in the Eastern Tropical Pacific

Spaceborne radar measurement of wind velocity over the ocean-an overview of the NSCAT scatterometer system

Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys