Temperature Spatiotemporal Correlation Scales in the Brazil‐Malvinas Confluence from High‐Resolution In Situ and Remote Sensing Data

Orúe‐Echevarría, Dorleta; Castellanos, Paola; Sans, Joel; Emelianov, Mikhail; Vallès‐Casanova, Ignasi; Pelegrí, Josep Lluís

doi:10.1029/2019gl084246

Cited by 6 publications

(5 citation statements)

References 56 publications

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“…18b), although the time lapse and distance between both profiles is larger than in the preceding pair, the thermohaline variations are much smaller: both profiles display similar subantarctic θ-S properties, belonging to the inner MC where the thermohaline variability is low and there are no subtropical intrusions (Fig. 9; Orúe-Echevarría et al, 2019c). Finally, the pair in the far field (Apex22-CTD61, Fig.…”

Section: Bmc Variabilitymentioning

confidence: 82%

“…This situation is most complex when the north-easterly winds over the neighboring continental shelf export river waters. On the other hand, the variability is maximum at the subsurface frontal layers, between 100 and 300 m, particularly in the northern side of the front, due to the presence of both mesoscale and submesoscale intrusions (Orúe-Echevarría et al, 2019c).…”

Section: Bmc Variabilitymentioning

confidence: 99%

See 1 more Smart Citation

A view of the Brazil-Malvinas confluence, March 2015

Orúe‐Echevarría

Pelegrí

Alonso-González

et al. 2021

Deep Sea Research Part I: Oceanographic Research Papers

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Section: Bmc Variabilitymentioning

confidence: 82%

Section: Bmc Variabilitymentioning

confidence: 99%

A view of the Brazil-Malvinas confluence, March 2015

Orúe‐Echevarría

Pelegrí

Alonso-González

et al. 2021

Deep Sea Research Part I: Oceanographic Research Papers

Self Cite

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“…However, in agreement with the observations of Manta et al (2022), mesoscale eddies that approach the outer shelf may create a fine scale structure of relatively intense cross-shore flows (see Supplementary Figure 5). In addition, highly nonlinear submesoscale features of characteristic scale <10 km, not resolved by the GLORYS reanalysis, are ubiquitous in the Brazil/Malvinas Confluence (Orúe-Echevarría et al, 2019), and may further contribute to cross-shelf exchanges.…”

Section: Analysis Of Extreme Transport Eventsmentioning

confidence: 99%

Cross-Shelf Exchange in the Southwestern Atlantic Shelf: Climatology and Extreme Events

2022

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The variability and drivers of the cross-shelf exchanges between the Southwestern Atlantic shelf and the open ocean from 30 to 40°S are analyzed using a high-resolution ocean model reanalysis at daily resolution. The model's performance was first evaluated using altimetry data, and independent mooring and hydrographic data collected in the study area. Model transports are in overall good agreement with all other estimates. The record-mean (1993–2018) cross-shore transport is offshore, 2.09 ± 1.60 Sv. 73% of the shelf-open ocean exchange occurs in the vicinity of Brazil-Malvinas Confluence (~38°S) and 20% near 32°S. This outflow is mostly contributed by northward alongshore transport through 40°S (63%) and the remaining by southward transport through 30°S (37%). The cross-shore flow presents weak seasonal variations, with a maximum in austral summer, and high variability at subannual and weekly time scales. The latter is mainly associated with abrupt wind changes generated by synoptic atmospheric systems. Alongshore wind variations set up sea-level changes in the inner shelf which in turn drive large anomalies in the associated geostrophic alongshore flow. The difference in inner shelf sea-level anomalies at 30 and 40°S is a good indicator of cross-shelf exchange at seasonal and shorter time scales. Episodes of extreme offshore transport that reach up to 9.45 Sv and last about 2 days are driven by convergence of these alongshore flows over the shelf. Large exports of shelf waters lead to freshening of the upper open ocean as revealed by in-situ and satellite observations. In contrast, onshore extreme events drive open ocean water intrusions of up to 6.53 Sv and last <4 days. These inflows, particularly the subtropical waters from the Brazil Current, induce a substantial salinification of the outer shelf.

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“…This is a special spatial-temporal function of RS-STBD which is different from general image processing system. For the analysis and mining of RS-STBD, the main methods and theories include: spatial-temporal classification [39], spatial-temporal clustering [40], spatial-temporal anomaly [41], change detection [42], spatial-temporal correlation analysis [43], spatial-temporal evolution analysis [44], spatial-temporal prediction [19], and other analysis and data mining methods of spatial-temporal information.…”

Section: Intelligent Computing Model and Data Mining Theory Of Rs-stbdmentioning

confidence: 99%

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

Liu

Dang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the rapid development of high-resolution earth observation system, the data processing, algorithm design and system development of Remote Sensing Spatial-Temporal Big Data (RS-STBD) have gradually become the bottleneck problems in the application and development of earth observation system. The research on the model, algorithm and system of RS-STBD processing involves complex scientific problems, technical bottlenecks and inconstant requirements of engineering applications. This paper summarizes the data type and processing theory model of RS-STBD, the high performance algorithm design based on cloud service and intelligent computing, and the architecture design and engineering development methods of the complex remote sensing application system. Furthermore, the existing problems in the current research are analyzed, and the related solutions are given. Finally, the future development trend of scientific exploration, technical research and application development of RS-STBD is prospected.

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Temperature Spatiotemporal Correlation Scales in the Brazil‐Malvinas Confluence from High‐Resolution In Situ and Remote Sensing Data

Cited by 6 publications

References 56 publications

A view of the Brazil-Malvinas confluence, March 2015

A view of the Brazil-Malvinas confluence, March 2015

Cross-Shelf Exchange in the Southwestern Atlantic Shelf: Climatology and Extreme Events

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

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