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

Soonyoung Roh, Hwan‐Jin Song

Geophysical Research Letters · 17 October 2020 · Korea Meteorological Administration (KMA)

In one sentence. Neural network radiation emulators in a cloud-resolving model run 20–100 times faster and, run frequently, improve 6 h forecast accuracy over infrequent original radiation calls.

Abstract

Abstract 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.

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