Radiation schemes are fundamental components of weather and climate models that need to be both efficient and accurate. In this work we refactor ecRad, a flexible radiation scheme developed at the European Centre for Medium-Range Weather Forecasts (ECMWF). The goal was to improve performance especially with ecCKD, a new gas optics scheme that requires only 32 spectral intervals in the longwave and shortwave to be accurate. This speeds up ecRad considerably, but also reduces performance due to short inner loops. We therefore carry out both higher-level code restructuring and kernel-level optimizations for the radiative transfer solvers TripleClouds and SPARTACUS. SPARTACUS computes cloud 3D radiative effects, which have so far been neglected in large-scale models. We exploit the lack of vertical loop dependencies in key computations by merging the spectral and vertical dimensions, improving vectorization and instruction-level parallelism. On the new AMD Rome-based ECMWF supercomputer, we obtain a 3-fold speedup for both solvers when using 32-term ecCKD models. Combining ecCKD with optimized code results in very fast yet accurate radiation computations: with TripleClouds we achieve 1.7 TFLOPs and a throughput of 621 columns/ms on a 128-core node. This is 11.5 times faster than ecRad in Integrated Forecasting System cycle 47r3, which uses a more noisy solver (McICA) and less accurate gas optics (RRTMG). SPARTACUS with ecCKD is now 2.4 times faster than CY47r3-ecRad, making cloud 3D radiative effects affordable to compute within large-scale models. Preliminary results show that SPARTACUS slightly improves forecasts of 2-metre temperature and low clouds in the tropics.
Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NN), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach which is slower but more robust than full emulation is to use NNs to predict optical properties, without abandoning the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP gas optics scheme, and shown to be accurate while improving speed.However, the evaluations were 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). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using a hybrid loss function designed to reduce radiative forcing errors, and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark, new NN models were trained on RRTMGP k-distributions with reduced spectral resolutions. Offline evaluation shows a very high level of accuracy for clear-sky fluxes and heating rates; for instance the RMSE in shortwave surface downwelling flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for 5 important greenhouse gases across a wide range of concentrations such as 8x CO2. To assess the impact of different gas optics schemes in the IFS, 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 schemes, and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster), and is also faster than the older and less accurate RRTMG which is used in the current operational cycle of the IFS