Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System

Peter Ukkonen, Robin James Hogan

Geoscientific model development · 30 May 2022 · ECMWF, University of Reading

In one sentence. Implements a neural-network gas optics scheme in ECMWF's IFS, speeding radiation by about one third with a similar model climate in 1-year coupled runs.

Abstract

Radiative transfer parameterizations are physically important but computationally expensive components of weather and climate models. In previous work, it was demonstrated that the gas optics module of a radiation scheme, which traditionally rely on look-up-tables, can be replaced with neural networks (NN) to improve speed while retaining a high degree of accuracy. However, the evaluation of the NN version of the RRTMGP gas optics scheme (RRTMGP-NN) was based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). This was carried out by incorporating the gas optics scheme into ecRad, the modular radiation scheme used in the IFS. New NN models were trained on RRTMGP k-distributions with reduced spectral resolution. A hybrid loss function helped reduce radiative forcing errors. Four 1-year coupled ocean-atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing gas optics schemes, and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up the radiation scheme by roughly a third compared to RRTMGP, and is also faster than the older and less accurate RRTMG which is used in the current operational cycle of the IFS.

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