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Status: Unchecked ×Keyword: atmospheric parameterization ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision

   Yuval, O'Gorman and Hill · Geophysical Research Letters · 2021

   The authors trained a neural-network parameterization from a high-resolution atmospheric simulation in an idealized domain and coupled it to a coarser model, finding stable, accurate simulations at reduced precision.

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   1. UncheckedA neural-network subgrid scheme built to respect physical constraints gave stable climate simulations as accurate as a random-forest scheme, using far less memory.“The NN parameterization has a structure that ensures physical constraints are respected, and it leads to stable simulations that replicate the climate of the high-resolution simulation with similar accuracy to a successful random-forest parameterization while needing far less memory.”
   2. Unchecked“We find that the simulations are stable for a variety of NN architectures and horizontal resolutions, and that an NN with substantially reduced numerical precision could decrease computational costs without affecting the quality of simulations.”