Mesoscale Mapping Capabilities of Multiple-Satellite Altimeter Missions

Traon, P. Y. Le; Dibarboure, Gérald

doi:10.1175/1520-0426(1999)016<1208:mmcoms>2.0.co;2

Cited by 167 publications

(94 citation statements)

References 13 publications

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“…-The individual velocity error estimates, computed as the sum of two contributions: the altimeter velocity anomaly errors (equal to 30 % (40 %) of the zonal (meridian) following Le Dibarboure, 1999, andPascual et al, 2007) velocity on one side and the drifter geostrophic velocity error on the other side. This drifter geostrophic velocity error depends on the drifter type and is given in Table 2 of the paper by Poulain et al (2012).…”

Section: Computation Of the Synthetic Mean Velocitiesmentioning

confidence: 99%

Computation of a new mean dynamic topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in situ data

et al. 2014

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Abstract. The accurate knowledge of the ocean's mean dynamic topography (MDT) is a crucial issue for a number of oceanographic applications and, in some areas of the Mediterranean Sea, important limitations have been found pointing to the need of an upgrade. We present a new MDT that was computed for the Mediterranean Sea. It profits from improvements made possible by the use of extended data sets and refined processing. The updated data set spans the 1993-2012 period and consists of drifter velocities, altimetry data, hydrological profiles and model data. The methodology is similar to the previous MDT by Rio et al. (2007). However, in Rio et al. (2007) no hydrological profiles had been taken into account. This required the development of dedicated processing. A number of sensitivity studies have been carried out to obtain the most accurate MDT as possible. The main results from these sensitivity studies are the following: moderate impact to the choice of correlation scales but almost negligible sensitivity to the choice of the first guess (model solution). A systematic external validation to independent data has been made to evaluate the performance of the new MDT. Compared to previous versions, SMDT-MED-2014 (Synthetic Mean Dynamic Topography of the MEDiterranean sea) features shorter-scale structures, which results in an altimeter velocity variance closer to the observed velocity variance and, at the same time, gives better Taylor skills.

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Section: Computation Of the Synthetic Mean Velocitiesmentioning

confidence: 99%

Computation of a new mean dynamic topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in situ data

et al. 2014

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“…As combining data from different satellite missions improves the estimation of mesoscale signals (Le Traon and Dibarboure, 1999;Chelton and Schlax, 2003;Pascual et al, 2006), the SLA data set (October 1992 to April 2012) of the AVISO (http://www.aviso.oceanobs.com) Reference Series delayedtime altimeter products merging TOPEX/Poseidon (T/P), Jason-1, ERS-1/2, and Envisat data (Ducet et al, 2000) was used to identify mesoscale eddies. This merged altimeter data set consists of weekly SLA maps computed with respect to a seven-year mean and resampled on a 1/3 • × 1/3 • Mercator grid.…”

Section: Altimetry Datamentioning

confidence: 99%

Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly

et al. 2014

Ocean Sci.

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Abstract. Automated methods are important for automatically detecting mesoscale eddies in large volumes of altimeter data. While many algorithms have been proposed in the past, this paper presents a new method, called hybrid detection (HD), to enhance the eddy detection accuracy and the capability of recognizing eddy multi-core structures from maps of sea level anomaly (SLA). The HD method has integrated the criteria of the Okubo-Weiss (OW) method and the sea surface height-based (SSH-based) method, two commonly used eddy detection algorithms. Evaluation of the detection accuracy shows that the successful detection rate of HD is ∼ 96.6 % and the excessive detection rate is ∼ 14.2 %, which outperforms the OW and those methods using SLA extrema to identify eddies. The capability of recognizing multi-core structures and its significance in tracking eddy splitting or merging events have been illustrated by comparing with the detection results of different algorithms and observations in previous literature.

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“…This innovation produced a demand for ocean data. Data collection systems (e.g., Davis et al 1992;Greenslade et al 1997;Le Traon and Dibarboure 1999) and ocean modeling methods (e.g., Marshall et al 1997;Haidvogel et al 2000) also advanced at that time. Yet in spite of these improvements, data assimilation was still being perfected (Robinson et al 1998) and data were still sparse, and at this point the acoustic modeling capability surpassed environmental knowledge.…”

Section: Origins Through Current Practice: Knowing the Oceanmentioning

confidence: 99%

Modeling and Forecasting Ocean Acoustic Conditions

Duda¹

2017

j mar res

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Modeling acoustic conditions in an oceanic environment is a multiple-step process. The environmental conditions (features) in the area first must be measured or estimated; relevant features include seabed geometry, seabed composition, and four-dimensionally (4D) variable sound-speed and density variations related to evolving or wave motions. Often the dynamical wave modeling depends on first obtaining correct seabed and mean stratification conditions (for example, nonlinear internal wave modeling). Next, this information must be included in sound propagation modeling. A selection of the many methods and tools available for these tasks are described, with a focus on modeling sounds of 20 to 1000 Hz propagating through water-column features that are time-dependent and variable in three dimensions (i.e., 4D variable). An example of a 3D parabolic equation acoustic calculation shows how variability caused by evolving internal tidal waves affects sound propagation. Different propagation and scattering regimes are discussed, including the theoretically delineated weak scattering and strong scattering regimes, as well as the empirically examined regime found in nonlinear internal waves. The histories and the current state of our oceanographic knowledge (the input to acoustic modeling) and of our ability to effectively model complex acoustic conditions are discussed. Example acoustic simulation applications are also discussed; these are ocean acoustic tomography, coherence prediction, and signal-to-noise ratio prediction. Types of ocean models and acoustic models and how they are interfaced are also examined. These include deterministic, statistical analytic feature models.

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Mesoscale Mapping Capabilities of Multiple-Satellite Altimeter Missions

Cited by 167 publications

References 13 publications

Computation of a new mean dynamic topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in situ data

Computation of a new mean dynamic topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in situ data

Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly

Modeling and Forecasting Ocean Acoustic Conditions

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