Evaluation of Neural Network Emulations for Radiation Parameterization in Cloud Resolving Model

This study evaluated the forecast performance of neural network (NN)‐based radiation emulators with 300 to 56 neurons developed under the cloud‐resolving simulation. These emulators are 20–100 times faster than the original parameterization and express evolutionary features well for 6 hr. The results suggest that the frequent use of an NN emulator can improve not only computational speed but also forecasting accuracy in comparison to the infrequent use of original radiation parameterization, which is commonly used for speedup but can induce numerical instability as a result of imbalance with other processes. The forecast error of the emulator results was much improved in comparison with that for infrequent radiation runs with similar computational cost. The 56‐neuron emulator results were even more accurate than the infrequent runs, which had a computational cost five times higher. The speed and accuracy advantages of radiation emulators can be utilized for weather forecasting. Plain Language Summary Radiative transfer calculations in weather and climate models often impose computational challenges because of the complexity of radiation processes. Empirical emulators based on NN have been developed to mimic radiation parameterization while reducing computational cost. The accuracy in those studies has not been strictly evaluated because the emulator cannot outpace the original radiation parameterization in terms of accuracy. However, the emulators developed in this study showed advantages both the computational cost and forecast accuracy. These advantages of radiation emulator make them useful for weather forecasting. Key Points Neural network radiation emulators are developed and evaluated at a cloud‐resolving scale Radiation emulators with 300 to 56 neurons are 20–100 times faster than the original scheme Frequent emulator results can be more accurate than infrequent calculations of the original radiation scheme