Dynamic and thermohaline properties of the Mindanao Undercurrent, part II: Thermohaline structure

1998 ◽  
Vol 16 (3) ◽  
pp. 206-213 ◽  
Author(s):  
Wang Fan ◽  
Hu Dun-xin
Keyword(s):  
Thermohaline Structure ◽  
Mindanao Undercurrent

scholarly journals Numerical study of circulation and thermohaline structure in the São Sebastião channel

2005 ◽  
Vol 23 (4) ◽  
Author(s):  
Lourval dos Santos Silva ◽  
Luiz Bruner de Miranda ◽  
Belmiro Mendes de Castro Filho
Keyword(s):  
Numerical Study ◽  
Thermohaline Structure

Fine Thermohaline Structure and Gas-exchange in the Near-Surface Layer of the Ocean DuringGasEx-98

2013 ◽  
pp. 181-185 ◽  
Author(s):  
Alexander Soloviev ◽  
Jim Edson ◽  
Wade Mcgillis ◽  
Peter Schluessel ◽  
Rik Wanninkof
Keyword(s):  
Surface Layer ◽  
Gas Exchange ◽  
Near Surface ◽  
Thermohaline Structure

scholarly journals Transformation of Levantine Intermediate Water tracked by MedArgo floats in Western Mediterranean

2006 ◽  
Vol 3 (3) ◽  
pp. 569-584 ◽  
Author(s):  
M. Emelianov ◽  
J. Font ◽  
A. Turiel ◽  
J. Solé ◽  
P. Poulain ◽  
...  
Keyword(s):  
Mediterranean Basin ◽  
General Circulation ◽  
Western Mediterranean ◽  
Intermediate Water ◽  
Thermohaline Structure ◽  
Western Mediterranean Basin ◽  
Intermediate Waters ◽  
Good Agreement

Abstract. A clustering methodology is applied to investigate the thermohaline structure of Levantine Intermediate Water (LIW) in the western Mediterranean basin. 16 free-drifting hydrographic profilers were deployed in the framework of the MFSTEP project (MedArgo component) from September 2003. A total of 925 CTD profiles data collected until the beginning of February 2006 have been used in the analysis. The results are in good agreement with the general circulation scheme for intermediate waters in the basin and confirm the hypothesis about a "discrete-continuous" thermohaline structure of LIW.

scholarly journals EXPERIMENTAL STUDIES OF THE THERMOHALINE STRUCTURE AT THE CONTINENTAL SLOPE IN THE NORTH-WESTERN JAPAN SEA

2017 ◽  
Vol 45 (1) ◽  
pp. 34-52 ◽  
Author(s):  
A.Yu. Lazaryuk ◽  
◽  
D.D. Kaplunenko ◽  
A.G. Ostrovskii ◽  
V.B. Lobanov ◽  
...  
Keyword(s):  
Continental Slope ◽  
Experimental Studies ◽  
Japan Sea ◽  
Thermohaline Structure ◽  
The North ◽  
Western Japan ◽  
North Western

scholarly journals Estimating Subsurface Thermohaline Structure of the Global Ocean Using Surface Remote Sensing Observations

2019 ◽  
Vol 11 (13) ◽  
pp. 1598 ◽  
Author(s):  
Hua Su ◽  
Xin Yang ◽  
Wenfang Lu ◽  
Xiao-Hai Yan
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Estimation Error ◽  
Satellite Observations ◽  
The Other ◽  
Global Ocean ◽  
Gradient Boosting ◽  
Mean Square ◽  
Thermohaline Structure

Retrieving multi-temporal and large-scale thermohaline structure information of the interior of the global ocean based on surface satellite observations is important for understanding the complex and multidimensional dynamic processes within the ocean. This study proposes a new ensemble learning algorithm, extreme gradient boosting (XGBoost), for retrieving subsurface thermohaline anomalies, including the subsurface temperature anomaly (STA) and the subsurface salinity anomaly (SSA), in the upper 2000 m of the global ocean. The model combines surface satellite observations and in situ Argo data for estimation, and uses root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), and R2 as accuracy evaluations. The results show that the proposed XGBoost model can easily retrieve subsurface thermohaline anomalies and outperforms the gradient boosting decision tree (GBDT) model. The XGBoost model had good performance with average R2 values of 0.69 and 0.54, and average NRMSE values of 0.035 and 0.042, for STA and SSA estimations, respectively. The thermohaline anomaly patterns presented obvious seasonal variation signals in the upper layers (the upper 500 m); however, these signals became weaker as the depth increased. The model performance fluctuated, with the best performance in October (autumn) for both STA and SSA, and the lowest accuracy occurred in January (winter) for STA and April (spring) for SSA. The STA estimation error mainly occurred in the El Niño-Southern Oscillation (ENSO) region in the upper ocean and the boundary of the ocean basins in the deeper ocean; meanwhile, the SSA estimation error presented a relatively even distribution. The wind speed anomalies, including the u and v components, contributed more to the XGBoost model for both STA and SSA estimations than the other surface parameters; however, its importance at deeper layers decreased and the contributions of the other parameters increased. This study provides an effective remote sensing technique for subsurface thermohaline estimations and further promotes long-term remote sensing reconstructions of internal ocean parameters.

scholarly journals Upper layer thermohaline structure of the Bay of Bengal during the 2013 northeast monsoon

2020 ◽  
Vol 172 ◽  
pp. 104630 ◽  
Author(s):  
Emily L. Shroyer ◽  
Arnold L. Gordon ◽  
Gualtiero Spiro Jaeger ◽  
Mara Freilich ◽  
Amy F. Waterhouse ◽  
...  
Keyword(s):  
Bay Of Bengal ◽  
Northeast Monsoon ◽  
Thermohaline Structure
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