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Título

A global monthly climatology of total alkalinity: a neural network approach (Discussions version) [Dataset]

AutorBroullón, Daniel CSIC ORCID; Pérez, Fiz F. CSIC ORCID ; Velo, A. CSIC ORCID ; Hoppema, Mario; Olsen, Are; Takahashi, Taro; Key, Robert M.; González-Dávila, Melchor; Tanhua, Toste; Jeansson, Emil; Kozyr, Alex; van Heuven, Steven
Palabras claveTotal alkalinity
Monthly climatology
Neural networks
Ocean acidification
Tesauro AGROVOCalkalinity 
acidification
climatic data
Fecha de publicación2018
CitaciónBroullón, Daniel; Pérez, Fiz F.; Velo, A.; Hoppema, M.; Olsen, Are; Takahashi, Taro; Key, Robert M.; González-Dávila, Melchor; Tanhua, T.; Jeansson, Emil; Kozyr, Alex; Van Heuven, S.; 2018; “A global monthly climatology of total alkalinity: a neural network approach (Discussions version) [Dataset]”; Digital.CSIC; http://dx.doi.org/10.20350/digitalCSIC/8564
DescripciónThe item is made of 6 files: 1) Readme_Global_monthly_dataset.txt; 2) ATNNWOA13.nc is the climatological data of total alkalinity computed with NNGv2; 3) NNGv2 is the neural network object used to create the climatology; 4) NNw3RMSE is a neural network object used to evaluate the error of the network when it is trained without data beyond +-3RMSE; 5)ATNNWOA13.mp4 is a video of the surface climatology, 3 vertical sections in the Pacific Ocean, Atlantic Ocean and Indean Ocean and, the variation in depth of one month (April); 6) Example.rar contains an example matrix of inputs to the neural network, the NNGv2.mat and a MATLAB script to compute AT with NNGv2.-- The final version is in http://dx.doi.org/10.20350/digitalCSIC/8644
URIhttp://hdl.handle.net/10261/169529
DOI10.20350/digitalCSIC/8564
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