scholarly journals Mapping sea surface observations to spectra of underwater ambient noise through self-organizing map method

2019 ◽  
Vol 146 (2) ◽  
pp. EL111-EL116 ◽  
Author(s):  
Ying Zhang ◽  
Kunde Yang ◽  
Qiulong Yang ◽  
Cheng Chen
Keyword(s):  
Ambient Noise ◽  
Sea Surface ◽  
Self Organizing Map ◽  
Surface Observations ◽  
Self Organizing

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.

Clustering of Loop Current patterns based on the satellite-observed sea surface height and self-organizing map

2015 ◽  
Vol 6 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Xiangming Zeng ◽  
Yizhen Li ◽  
Ruoying He ◽  
Yuqi Yin
Keyword(s):  
Sea Surface Height ◽  
Sea Surface ◽  
Self Organizing Map ◽  
Loop Current ◽  
Self Organizing

scholarly journals Variations in the summer oceanic <i>p</i>CO<sub>2</sub> and carbon sink in Prydz Bay using the self-organizing map analysis approach

2019 ◽  
Vol 16 (3) ◽  
pp. 797-810 ◽  
Author(s):  
Suqing Xu ◽  
Keyhong Park ◽  
Yanmin Wang ◽  
Liqi Chen ◽  
Di Qi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Ocean ◽  
Sea Surface ◽  
Prydz Bay ◽  
Self Organizing Map ◽  
Ocean Region ◽  
Co2 Sink ◽  
Shelf Region ◽  
Self Organizing

Abstract. This study applies a neural network technique to produce maps of oceanic surface pCO2 in Prydz Bay in the Southern Ocean on a weekly 0.1∘ longitude × 0.1∘ latitude grid based on in situ measurements obtained during the 31st CHINARE cruise from February to early March 2015. This study area was divided into three regions, namely, the “open-ocean” region, “sea-ice” region and “shelf” region. The distribution of oceanic pCO2 was mainly affected by physical processes in the open-ocean region, where mixing and upwelling were the main controls. In the sea-ice region, oceanic pCO2 changed sharply due to the strong change in seasonal ice. In the shelf region, biological factors were the main control. The weekly oceanic pCO2 was estimated using a self-organizing map (SOM) with four proxy parameters (sea surface temperature, chlorophyll a concentration, mixed Layer Depth and sea surface salinity) to overcome the complex relationship between the biogeochemical and physical conditions in the Prydz Bay region. The reconstructed oceanic pCO2 data coincide well with the in situ pCO2 data from SOCAT, with a root mean square error of 22.14 µatm. Prydz Bay was mainly a strong CO2 sink in February 2015, with a monthly averaged uptake of 23.57±6.36 TgC. The oceanic CO2 sink is pronounced in the shelf region due to its low oceanic pCO2 values and peak biological production.

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 (3) ◽  
pp. 4575-4610 ◽  
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 produced 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 values were estimated by using a self-organizing map neural network technique to explain the non-linear relationships between observed pCO2sea data and four oceanic parameters: sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS). The observed pCO2sea data was obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies. The reconstructed pCO2sea values agreed rather well with the pCO2sea measurements, the root mean square error being 17.6 μatm. The pCO2sea estimates were 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 stations in the North Pacific. The distributions of pCO2sea revealed by seven-year averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology and more precisely reflected oceanic 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 Sea Surface Temperature Patterns on the West Florida Shelf Using Growing Hierarchical Self-Organizing Maps

2006 ◽  
Vol 23 (2) ◽  
pp. 325-338 ◽  
Author(s):  
Yonggang Liu ◽  
Robert H. Weisberg ◽  
Ruoying He
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Self Organizing Map ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
Network Analyses ◽  
Sst Anomaly ◽  
Self Organizing

Abstract Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.

Self-organizing map and its application in the analysis of ambient noise characteristics

2017 ◽  
Author(s):  
Chunxia Meng ◽  
Guijuan Li ◽  
Shuwei Che ◽  
Jin Bai
Keyword(s):  
Ambient Noise ◽  
Self Organizing Map ◽  
Noise Characteristics ◽  
Self Organizing
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