Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence
Abstract The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use data sets that are temporally subsampled relative to th...
Main Authors: | Timothy A. Smith, Stephen G. Penny, Jason A. Platt, Tse‐Chun Chen |
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Format: | Article |
Language: | English |
Published: |
American Geophysical Union (AGU)
2023-12-01
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Series: | Journal of Advances in Modeling Earth Systems |
Subjects: | |
Online Access: | https://doi.org/10.1029/2023MS003792 |
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