Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: Journal of Advances in Modeling Earth Systems
The use of machine learning to represent sub-grid processes is increasingly being explored as a way of reducing the uncertainty and computational expense of large-scale models. To shed light on the best approaches, Ukkonen [2022] evaluates different ways of emulating a radiation scheme using machine learning.
Radiation differs from some other sub-grid processes in that it’s well-understood, but its computational expense has motivated attempts to replace the entire radiation code with a neural network. Past studies on emulating radiation, as well as other sub-grid physics, have traditionally taken the vertical profiles of relevant variables and concatenated them into one long input or output vector of a dense neural network. In this approach, the number of inputs and outputs, and hence the vertical resolution, must be fixed. A recurrent neural network (RNN), in contrast, can be used to traverse through an atmospheric column sequentially layer by layer. When applied to shortwave radiation, a method based on bidirectional RNNs, whose structure was inspired by physical radiative transfer equations, improved the accuracy by an order of magnitude compared to a dense network that used an order of magnitude more parameters. If RNNs prove effective for other processes, the smaller dimensionality may be crucial in allowing machine-learned parameterizations to generalize.
Another way of using machine learning for radiation is to keep the radiative transfer equations but replace the gas optics – a more data-driven component of radiation schemes – with a neural network. This approach did not sacrifice accuracy, and still gave a meaningful speedup. The author’s research presents a clear example of how machine learning can be combined with physical modeling and domain knowledge to improve the prediction of sub-grid processes.
Citation: Ukkonen, P. (2022). Exploring pathways to more accurate machine learning emulation of atmospheric radiative transfer. Journal of Advances in Modeling Earth Systems, 14, e2021MS002875. https://doi.org/10.1029/2021MS002875
—Jiwen Fan, Editor, Journal of Advances in Modeling Earth Systems
Text © 2022. The authors. CC BY-NC-ND 3.0
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