Supporting Data for "A novel machine learning method for accelerated modeling of the downwelling irradiance field in the upper ocean"
2022-04-27
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Title
Supporting Data for "A novel machine learning method for accelerated modeling of the downwelling irradiance field in the upper ocean"
Published Date
2022-04-27
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Hao, Xuanting
haoxx081@umn.edu
haoxx081@umn.edu
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Simulation Data
Simulation Data
Abstract
The training data are generated from the Monte Carlo simulation of oceanic irradiance field. They can be used for training a neural network that significantly accelerates the prediction of irradiance in the upper ocean.
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Referenced by
Hao, X., & Shen, L. (2022). A novel machine learning method for accelerated modeling of the downwelling irradiance field in the upper ocean. Geophysical Research Letters, 49, e2022GL097769.
https://doi.org/10.1029/2022GL097769
https://doi.org/10.1029/2022GL097769
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Hao, Xuanting; Shen, Lian. (2022). Supporting Data for "A novel machine learning method for accelerated modeling of the downwelling irradiance field in the upper ocean". Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/742c-c863.
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| File View/Open | Description | Size |
|---|---|---|
| mldat3d_clear.tar.gz | Training data in clear case | 68.95 MB |
| mldat3d_turbid.tar.gz | Training data in turbid case | 206.97 MB |
| wavelight_6to5.png | A broadband wave field and the irradiance field beneath the ocean surface | 528.39 KB |
| trainingcode.zip | Training code, pretrained neural network, and record of loss function | 225.88 KB |
| Readme.txt | Documentation of the data files | 13.47 KB |
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