Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3241-2023
https://doi.org/10.5194/gmd-16-3241-2023
Development and technical paper
 | 
09 Jun 2023
Development and technical paper |  | 09 Jun 2023

Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0

Peter Ukkonen and Robin J. Hogan

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Cited articles

Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q.: JAX: composable transformations of Python+NumPy programs, http://github.com/google/jax (last access: 8 June 2023), 2018. a
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural network unified physics parameterization, Geophys. Res. Lett., 45, 6289–6298, https://doi.org/10.1029/2018gl078510, 2018. a
Brenowitz, N. D., Beucler, T., Pritchard, M., and Bretherton, C. S.: Interpreting and stabilizing machine-learning parametrizations of convection, J. Atmos. Sci., 77, 4357–4375, 2020. a
Chevallier, F., Chéruy, F., Scott, N., and Chédin, A.: A neural network approach for a fast and accurate computation of a longwave radiative budget, J. Appl. Meteorol., 37, 1385–1397, 1998. a
Cotronei, A. and Slawig, T.: Single-precision arithmetic in ECHAM radiation reduces runtime and energy consumption, Geosci. Model Dev., 13, 2783–2804, https://doi.org/10.5194/gmd-13-2783-2020, 2020. a, b
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Short summary
Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
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