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Status: Unchecked ×Keyword: atmospheric radiative transfer ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Exploring Pathways to More Accurate Machine Learning Emulation of Atmospheric Radiative Transfer

   Ukkonen · Journal of Advances in Modeling Earth Systems · 2022

   The study trained neural networks to emulate a shortwave radiation scheme and its components, and developed a recurrent network that builds in the vertical, sequential nature of radiation calculations.

   Unchecked3 claims
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   1. UncheckedA bidirectional RNN with far fewer parameters than a feed-forward network was found more accurate at emulating a radiation scheme, and about 4 times faster on CPUs.“It is found that a bidirectional RNN with an order of magnitude fewer model parameters than FNN is considerably more accurate, while offering a smaller but still significant 4‐fold speedup over the original code on CPUs, and a larger speedup on GPUs.”
   2. UncheckedA recurrent neural network emulating shortwave radiation predicted fluxes with under 1% error and heating rates with 0.16 K per day RMSE in offline tests.“The RNN predicts fluxes with less than 1% error, and heating rates computed from fluxes have a root‐mean‐square‐error of 0.16 K day −1 in offline tests using a year of global data.”
   3. UncheckedFeedforward neural networks that emulate the gas optics part of a radiation scheme are very accurate and several times faster than the original code.“Finally, FNNs emulating gas optics are very accurate while being several times faster.”