A global monthly climatology of total alkalinity: a neural network approach (Discussions version) [Dataset]
2018
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, Steven | Ministerio de Economía y Competitividad (España)
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
Показать больше [+] Меньше [-]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)
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Эту запись предоставил Instituto de Investigaciones Marinas