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Status: Unchecked ×Keyword: subgrid-scale turbulence models ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?

   Qu and Shi · Artificial Intelligence for the Earth Systems · 2022

   The authors trained a machine-learning model of unresolved small-scale turbulence with a differentiable solver in the loop, and it lengthened the effective forecast lead time of a low-resolution fluid model.

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   1. Unchecked“However, given initial conditions long after the training dataset time, the trained SGS model can still significantly increase the effective lead time of the BVE model running at the low resolution by up to 50% relative to the BVE simulation without an SGS m…
   2. UncheckedIn this study, normalising the training loss with a covariance matrix notably improved the machine-learning subgrid-scale model's performance.“We also tested using a covariance matrix to normalize the loss function and found it can notably boost the performance of the ML parameterization.”