scholarly journals A comparison of Multiple Non-linear regression and neural network techniques for sea surface salinity estimation in the tropical Atlantic ocean based on satellite data

2015 ◽  
Vol 49 ◽  
pp. 65-77 ◽  
Author(s):  
H. Moussa ◽  
M. A. Benallal ◽  
C. Goyet ◽  
N. Lefevre ◽  
M. C. EL Jai ◽  
...  
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Atlantic Ocean ◽  
Satellite Data ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Tropical Atlantic ◽  
Surface Salinity ◽  
Tropical Atlantic Ocean ◽  
Non Linear

scholarly journals Impact of intraseasonal wind bursts on sea surface temperature variability in the far eastern tropical Atlantic Ocean during boreal spring 2005 and 2006: focus on the mid-May 2005 event

Ocean Science ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 849-869 ◽  
Author(s):  
Gaëlle Herbert ◽  
Bernard Bourlès
Keyword(s):  
Atlantic Ocean ◽  
Surface Temperature ◽  
Kelvin Waves ◽  
Sea Surface ◽  
Tropical Atlantic ◽  
Gulf Of Guinea ◽  
Atmospheric Conditions ◽  
Tropical Atlantic Ocean ◽  
Boreal Spring ◽  
Wind Bursts

Abstract. The impact of boreal spring intraseasonal wind bursts on sea surface temperature variability in the eastern tropical Atlantic Ocean in 2005 and 2006 is investigated using numerical simulation and observations. We especially focus on the coastal region east of 5° E and between the Equator and 7° S that has not been studied in detail so far. For both years, the southerly wind anomalies induced cooling episodes through (i) upwelling processes, (ii) vertical mixing due to the vertical shear of the current, and for some particular events (iii) a decrease in incoming surface shortwave radiation. The strength of the cooling episodes was modulated by subsurface conditions affected by the arrival of Kelvin waves from the west influencing the depth of the thermocline. Once impinging the eastern boundary, the Kelvin waves excited westward-propagating Rossby waves, which combined with the effect of enhanced westward surface currents contributed to the westward extension of the cold water. A particularly strong wind event occurred in mid-May 2005 and caused an anomalous strong cooling off Cape Lopez and in the whole eastern tropical Atlantic Ocean. From the analysis of oceanic and atmospheric conditions during this particular event, it appears that anomalously strong boreal spring wind strengthening associated with anomalously strong Hadley cell activity prematurely triggered the onset of coastal rainfall in the northern Gulf of Guinea, making it the earliest over the 1998–2008 period. No similar atmospheric conditions were observed in May over the 1998–2008 period. It is also found that the anomalous oceanic and atmospheric conditions associated with the event exerted a strong influence on rainfall off northeast Brazil. This study highlights the different processes through which the wind power from the South Atlantic is brought to the ocean in the Gulf of Guinea and emphasizes the need to further document and monitor the South Atlantic region.

High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

<p>The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.</p>

scholarly journals Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a self-organizing map neural network technique

2013 ◽  
Vol 10 (9) ◽  
pp. 6093-6106 ◽  
Author(s):  
S. Nakaoka ◽  
M. Telszewski ◽  
Y. Nojiri ◽  
S. Yasunaka ◽  
C. Miyazaki ◽  
...  
Keyword(s):  
Neural Network ◽  
North Pacific ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
The North ◽  
The North Pacific ◽  
Self Organizing

Abstract. This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) – are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

scholarly journals Origin of Weakened Interannual Sea Surface Temperature Variability in the Southeastern Tropical Atlantic Ocean

2020 ◽  
Vol 47 (20) ◽  
Author(s):  
Arthur Prigent ◽  
Rodrigue Anicet Imbol Koungue ◽  
Joke F. Lübbecke ◽  
Peter Brandt ◽  
Mojib Latif
Keyword(s):  
Atlantic Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Variability ◽  
Sea Surface ◽  
Tropical Atlantic ◽  
Tropical Atlantic Ocean

scholarly journals Community review of Southern Ocean satellite data needs

2016 ◽  
Vol 29 (2) ◽  
pp. 97-138 ◽  
Author(s):  
A. Pope ◽  
P. Wagner ◽  
R. Johnson ◽  
J.D. Shutler ◽  
J. Baeseman ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Satellite Data ◽  
Marine Biology ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Starting Point ◽  
Data Continuity ◽  
Derived Data

AbstractThis review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea ice properties, sea surface temperature, sea surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea surface salinity, and a discussion of coincident andin situdata collection. Recommendations include commitment to data continuity, increases in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.

A Novel Dual Path Gated Recurrent Unit Model for Sea Surface Salinity Prediction

2020 ◽  
Vol 37 (2) ◽  
pp. 317-325 ◽  
Author(s):  
Tao Song ◽  
Zihe Wang ◽  
Pengfei Xie ◽  
Nisheng Han ◽  
Jingyu Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
South China Sea ◽  
South China ◽  
Prediction Accuracy ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Intrinsic Correlation ◽  
Gated Recurrent Unit

AbstractAccurate and real-time sea surface salinity (SSS) prediction is an elemental part of marine environmental monitoring. It is believed that the intrinsic correlation and patterns of historical SSS data can improve prediction accuracy, but they have been not fully considered in statistical methods. In recent years, deep-learning methods have been successfully applied for time series prediction and achieved excellent results by mining intrinsic correlation of time series data. In this work, we propose a dual path gated recurrent unit (GRU) network (DPG) to address the SSS prediction accuracy challenge. Specifically, DPG uses a convolutional neural network (CNN) to extract the overall long-term pattern of time series, and then a recurrent neural network (RNN) is used to track the local short-term pattern of time series. The CNN module is composed of a 1D CNN without pooling, and the RNN part is composed of two parallel but different GRU layers. Experiments conducted on the South China Sea SSS dataset from the Reanalysis Dataset of the South China Sea (REDOS) show the feasibility and effectiveness of DPG in predicting SSS values. It achieved accuracies of 99.29%, 98.44%, and 96.85% in predicting the coming 1, 5, and 14 days, respectively. As well, DPG achieves better performance on prediction accuracy and stability than autoregressive integrated moving averages, support vector regression, and artificial neural networks. To the best of our knowledge, this is the first time that data intrinsic correlation has been applied to predict SSS values.

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