An investigation of altimeter sea state bias theories

Gommenginger, Christine; Srokosz, Meric; Wolf, Judith; Janssen, Peter A. E. M.

doi:10.1029/2001jc001174

Cited by 18 publications

(35 citation statements)

References 22 publications

(129 reference statements)

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“…Surface slope variance has long been suggested as a candidate for including the effects of hydrodynamic modulations in EM bias models, on both empirical and theoretical grounds [ Rodriguez et al , 1992]. Later studies lend further support to the importance of surface slope [ Gommenginger et al , 2003]. Indeed, a simple computation based on a two‐scale surface model and nonlinear hydrodynamic theory shows that to first order, the change in amplitude with displacement of small waves riding on long waves is in direct proportion to RMS long wave slope S (W.K.…”

Section: Rms Long Wave Slope and Hydrodynamic Modulationmentioning

confidence: 99%

Electromagnetic bias estimation using in situ and satellite data: 1. RMS wave slope

et al. 2003

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[1] The electromagnetic (EM) bias is the largest source of error in TOPEX/Poseidon and Jason-1 satellite altimeter sea surface height (SSH) estimates. Current operational EM bias models are based on empirical relationships between the bias, wind speed, and significant wave height. These models are limited in their accuracy because wind speed and wave height do not capture enough information about the sea state to uniquely specify the bias. In order to improve EM bias estimation, we have studied the correlation between the EM bias and RMS long wave slope using data from tower-based experiments in the Gulf of Mexico and Bass Straight, Australia. Models based on significant wave height and RMS slope are more accurate than models based on wave height and wind speed by at least 50% in RMS error between predicted and ground truth bias values. Furthermore, models which incorporate wave slope exhibit reduced regional variation between the two widely separated experiment locations.

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Section: Rms Long Wave Slope and Hydrodynamic Modulationmentioning

confidence: 99%

Electromagnetic bias estimation using in situ and satellite data: 1. RMS wave slope

et al. 2003

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“…The data have been averaged with the respective regressors. The RMS slope is used in the following presentations to permit evaluations consistent with recent work on the topic [ Gommenginger et al , 2003; Melville et al , 2004; Millet et al , 2003].…”

Section: Resultsmentioning

confidence: 99%

“…Theory is divided into several schools. The first shows that a bias can be predicted using weakly nonlinear (WNL) theory [ Longuet‐Higgins , 1963] where three wave interactions among longer gravity waves lead to β estimates of order 2–8% [e.g., Jackson , 1979; Srokosz , 1986; Glazman et al , 1996; Gommenginger et al , 2003]. The predicted bias is primarily due to a so‐called tilt bias or cross‐skewness term that arises owing to a nonzero cross correlation between the long‐wave elevation and slope.…”

Section: Introductionmentioning

confidence: 99%

Impact of high‐frequency waves on the ocean altimeter range bias

Vandemark¹,

Chapron²,

Elfouhaily³

et al. 2005

J. Geophys. Res.

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[1] New aircraft observations are presented on the range determination error in satellite altimetry associated with ocean waves. Laser-based measurements of the cross correlation between the gravity wave slope and elevation are reported for the first time. These observations provide direct access to a long, O(10 m), gravity wave statistic central to nonlinear wave theory prediction of the altimeter sea state bias. Coincident Ka-band radar scattering data are used to estimate an electromagnetic (EM) range bias analogous to that in satellite altimetry. These data, along with ancillary wind and wave slope variance estimates, are used alongside existing theory to evaluate the extent of long-versus shortwave, O(cm), control of the bias. The longer wave bias contribution to the total EM bias is shown to range from 25 to as much as 100%. Moreover, on average the term is linearly related to wind speed and to the gravity wave slope variance, consistent with WNL theory. The EM bias associated with interactions between long and short waves is obtained assuming the effect is additive to the independently observed long-wave factor. This second component is also a substantial contributor, is observed to be quadratic in wind speed or wave slope, and dominates at moderate wind speeds. The behavior is shown to be consistent with EM bias prediction based in hydrodynamic modulation theory. Study implications for improved correction of the on-orbit satellite sea state bias are discussed.

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“…They also found strong correlations between the tower‐based EM bias measurements and altimeter‐derived estimates [ Gaspar and Florens , 1998]. Gommenginger et al [2003] investigated the applicability of theoretical models of EM bias by Srokosz [1986] and Elfouhaily et al [2001] using WAM wave model data, buoy and TOPEX data. They found that the Srokosz [1986] model using WAM output displayed a quasi‐linear correlation with the RMS wave slope, which was an improvement over traditional empirical models based on wind speed and wave height.…”

Section: Discussion and Summarymentioning

confidence: 99%

“…Instead we will show indications that the empirical altimeter data‐based correction does also remove some high‐frequency ocean variability from the observations. To overcome the problem of overcorrection and thereby to help improving the altimetric data quality, we investigate therefore subsequently the applicability of the tower‐based Melville et al (submitted manuscript, 2002) parameterization of EM bias to the altimeter data and compare it with the EM bias algorithm for Gaspar et al [1994] and with the theoretical approach from Srokosz [1986] ( Elfouhaily et al [2001] provides an alternative theoretical approach, and both are discussed in detail by Gommenginger et al [2003]).…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic bias estimates based on TOPEX, buoy, and wave model data

et al. 2003

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[1] For quantitative studies of the ocean using altimetric measurements, the sea surface height measurements must be corrected for the electromagnetic (EM) bias effect. Projectprovided EM bias correction algorithms were derived from the altimeter data as a function of wind speed and wave height through variance minimization techniques [Gaspar et al., 1994]. In this paper we characterize the impact of those corrections on the altimeter data and compare it with an empirical algorithm based on tower observations of wave slopes and wave age (W. K. Melville et al., Wave slope and wave age effects in measurements of EM bias, submitted to Journal of Geophysical Research, 2002) and with theoretical predictions of EM bias [Srokosz, 1986]. We find a significant correlation between high-frequency ocean signals with the project-provided EM bias correction. This suggests that an EM bias algorithm cannot be estimated from altimeter data alone through variance minimization techniques. Since the EM bias depends on parameters that cannot be measured by an altimeter (e.g., wave slope), improved theoretical corrections appear to be preferable to altimetry-based empirical estimates. Using wave buoy and wave model (WAM) data, we find good agreement between existing theoretical correction and empirical corrections based on wave slope and wave age information over a significant range of parameters. Although further work is needed to extend our tests of algorithms to larger wave slopes, existing wave slope and wave age-based algorithms appear comparable in goodness to project-provided empirical algorithms and may be used for routine altimetry processing in combination with WAM output on a global basis.

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An investigation of altimeter sea state bias theories

Cited by 18 publications

References 22 publications

Electromagnetic bias estimation using in situ and satellite data: 1. RMS wave slope

Electromagnetic bias estimation using in situ and satellite data: 1. RMS wave slope

Impact of high‐frequency waves on the ocean altimeter range bias

Electromagnetic bias estimates based on TOPEX, buoy, and wave model data

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