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Status: Unchecked ×Keyword: unresolved scales ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Using neural networks to improve simulations in the gray zone

   Kriegmair, Ruckstuhl, Rasp and Craig · Nonlinear processes in geophysics · 2022

   Using a modified shallow water model, the authors trained a neural network to learn errors from unresolved scales, which improved forecasts for up to about a day until mass conservation errors took over.

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   1. UncheckedA neural network learned a large share of the error from unresolved small scales in single-step predictions on held-out validation data, in a simplified model.“The neural network is able to learn a large portion of the difference when evaluated on single time step predictions on a validation dataset.”
   2. UncheckedCoupled to the low-resolution model, the neural network improves forecasts by up to about one day on average, in a modified shallow water model.“When coupled to the low-resolution model, we find large forecast improvements up to 1 d on average.”
   3. UncheckedIn this shallow water model study, a mass-conservation penalty in the neural network's training loss delayed the forecast getting worse.“This deterioration can effectively be delayed by adding a penalty term to the loss function used to train the ANN to conserve mass in a weak sense.”