Towards high resolution mapping of 3-D mesoscale dynamics from observations

Nardelli, Bruno Buongiorno; Guinehut, S.; Pascual, Ananda; Drillet, Yann; Ruiz, Simón; Mulet, Sandrine

doi:10.5194/os-8-885-2012

Cited by 47 publications

(50 citation statements)

References 49 publications

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“…Unfortunately, based on observations the mixing along fronts and around eddies remains difficult to validate properly. The coverage of the in situ observations and the resolution of satellite observations are not sufficient even though the recovery of vertical velocity based on satellite observations is promising (Buongiorno Nardelli et al, 2012) and though observations of water colour provide high-resolution estimates of ocean parameters directly affected by the vertical mixing. However, the effect of horizontal circulation, particularly around eddies or along strong fronts, is illustrated by Figs.…”

Section: Discussionmentioning

confidence: 99%

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

et al. 2014

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Abstract. Operational systems operated by Mercator Ocean provide daily ocean forecasts, and combining these forecasts we can produce ensemble forecast and uncertainty estimates. This study focuses on the mixed-layer depth in the Northeast Atlantic near the Porcupine Abyssal Plain for May 2013. This period is of interest for several reasons: (1) four Mercator Ocean operational systems provide daily forecasts at a horizontal resolution of 1/4, 1/12 and 1/36 • with different physics; (2) glider deployment under the OSMOSIS project provides observation of the changes in mixed-layer depth; (3) the ocean stratifies in May, but mixing events induced by gale force wind are observed and forecast by the systems. Statistical scores and forecast error quantification for each system and for the combined products are presented. Skill scores indicate that forecasts are consistently better than persistence, and temporal correlations between forecast and observations are greater than 0.8 even for the 4-day forecast. The impact of atmospheric forecast error, and for the wind field in particular (miss or time delay of a wind burst forecast), is also quantified in terms of occurrence and intensity of mixing or stratification events.

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Section: Discussionmentioning

confidence: 99%

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

et al. 2014

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“…Linear regression (Method I): The S can be directly estimated through simple or multiple linear regressions [5,8]:…”

Section: A Retrieval Methodsmentioning

confidence: 99%

“…For SMOS mission, for instance, SSS errors potentially stem from instrument observation, brightness temperature reconstruction, salinity retrieval, gridding processing, etc., and differ at different regions [6], which together complicate the practical application of SSS data. Even in the operational system newly developed within the MESCLA [5], the SSS fields used as inputs are still based on in situ observations. This work thus represents the first attempt to apply purely remotely sensed SSS observations, on a global scale and with various resolutions, in retrieval techniques and evaluates their performance.…”

Section: Introductionmentioning

confidence: 99%

“…Among studies on the SSS measurements' role in reconstructing profiles or on the retrieval methods' sensitivity to SSS errors, the SSS measurements were obtained only through drifting/moored buoys [2], Argo floats [3], low-resolution [4] or high-resolution [5] in-situ-observation-based gridded products. It should be emphasized that a simple/ideal error analysis, e.g., adding random white noises to the surface in situ values used as input for the vertical reconstruction seems inadequate, because fully independent remotely sensed SSS data from the SMOS or Aquarius missions could be contaminated by various kind of errors, and there are still many uncertainties about their real accuracy and geographical flavors.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of SMOS sea surface salinity observations in retrieving salinity profiles

Chen¹,

Zhang²,

Wang³

et al. 2013

Proceedings of the 2013 International Conference on Remote Sensing,Environment and Transportation Engineering

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Abstract-The Soil Moisture and Ocean Salinity (SMOS) remotely sensed sea surface salinity (SSS) observations, on a global scale and with various resolutions, have been applied as inputs in three representative retrieval techniques. The purpose is to evaluate the SSS observations' performance, concerning the accuracy of the salinity (S) profiles they retrieve, the spatial scales they can effectively resolve, and the way their errors are projected at depth. The SMOS SSS errors are small in the tropics and large in the extratropics. The spatial resolution, rather than temporal one, is an important factor for the accuracy of retrieved S. In the upper ~ 100 m mixed layer, on average, the root mean square (rms) of retrieved S is larger than, equivalent to, or smaller than the signal rms, with a SSS resolution of 1/4°, 1/2°, 1°, respectively, in the tropics, and always larger elsewhere. This can be explained by the SSS-S regression coefficients, which are uniformly large in the mixed layer and thus project vertically most of the surface error features. Meanwhile, the projection generally reduces the mesoscale structures in the 1/4° SSS fields. Below the mixed layer, mainly influenced by the sea level anomaly instead, the regions with high-accuracy retrieved S displace to higher latitudes. The SSS error patterns disappear, except in the southern oceans where the abnormally large SSS-S regression coefficients project SSS errors down to 400 ~ 500 m.

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“…Buongiorno Nardelli et al (2012) carried out the first attempt to apply the quasigeostrophic (QG) approximation to the observation-based product in order to map 3D mesoscale dynamics. Pascual et al (2015) and Barceló-Llull et al (2016) derive the QG mesoscale vertical velocity from the ARMOR3D fields in order to analyze its distribution in different regions of the global ocean as well as its contribution to nutrient advection and phytoplankton growth.…”

Section: Introductionmentioning

confidence: 99%

Comparing a Multivariate Global Ocean State Estimate With High-Resolution in Situ Data: An Anticyclonic Intrathermocline Eddy Near the Canary Islands

et al. 2018

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The provision of high-resolution in situ oceanographic data is key for the ongoing verification, validation and assessment of operational products, such as those provided by the Copernicus Marine Core Service (CMEMS). Here we analyze the ability of ARMOR3D-a multivariate global ocean state estimate that is available from CMEMS-to reconstruct a mesoscale anticyclonic intrathermocline eddy that was previously sampled with high-resolution independent in situ observations. ARMOR3D is constructed by merging remote sensing observations with in situ vertical profiles of temperature and salinity obtained primarily from the Argo network. In situ data from CTDs and an Acoustic Doppler Current Profiler were obtained during an oceanographic cruise near the Canary Islands (Atlantic ocean). The analysis of the ARMOR3D product using the in situ data is done over (i) a high-resolution meridional transect crossing the eddy center and (ii) a three-dimensional grid centered on the eddy center. An evaluation of the hydrographic eddy signature and derived dynamical variables, namely geostrophic velocity, vertical vorticity and quasi-geostrophic (QG) vertical velocity, demonstrates that the ARMOR3D product is able to reproduce the vertical hydrographic structure of the independently sampled eddy below the seasonal pycnocline, with the caveat that the flow is surface intensified and the seasonal pycnocline remains flat. Maps of ARMOR3D density show the signature of the eddy, and agreement with the elliptical eddy shape seen in the in situ data. The major eddy axes are oriented NW-SE in both data sets. The estimated radius for the in situ eddy is ∼46 km; the ARMOR3D radius is significantly larger at ∼ 92 km and is considered an overestimation that is inherited from an across-track altimetry sampling issue. The ARMOR3D geostrophic flow is underestimated by a factor of 2, with maxima of 0.11 (−0.19) m s −1 at the surface, which implies an underestimation of the local Rossby number by a factor of 3. Both the in situ and ARMOR3D eddies have decelerating flows at their northern edges. The ARMOR3D QG vertical velocity distribution has upwelling/downwelling cells located along the eddy periphery and similar magnitudes to the in situ-derived QG vertical velocity.

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Towards high resolution mapping of 3-D mesoscale dynamics from observations

Cited by 47 publications

References 49 publications

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

Performance evaluation of SMOS sea surface salinity observations in retrieving salinity profiles

Comparing a Multivariate Global Ocean State Estimate With High-Resolution in Situ Data: An Anticyclonic Intrathermocline Eddy Near the Canary Islands

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