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

• Main Authors: Broullón, Daniel, Pérez, Fiz F., Velo, A., Hoppema, Mario, Olsen, Are, Takahashi, Taro, Key, Robert M., González-Dávila, Melchor, Tanhua, Toste, Jeansson, Emil, Kozyr, Alex, Van Heuven, S.
• Other Authors: Ministerio de Economía y Competitividad (España)
• Format: Dataset
• Language: English
• Published: 2018
• Subjects: Total alkalinity; Monthly climatology; Neural network; Ocean acidification; http://aims.fao.org/aos/agrovoc/c_8721; http://aims.fao.org/aos/agrovoc/c_90; http://aims.fao.org/aos/agrovoc/c_29553; alkalinity; acidification; climatic data; Pacific
• Online Access: http://hdl.handle.net/10261/169529; https://doi.org/10.20350/digitalCSIC/8564; https://doi.org/10.13039/501100003329

The 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 This research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant FPU15/06026), Ministerio de Economía y Competitividad through the ARIOS (CTM2016-76146-C3-1-R) project co-funded by the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER) and EU Horizon 2020 through the AtlantOS project (grant agreement 633211) No