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Evaluation of Neural Network Emulations for Radiation Parameterization in Cloud Resolving Model
Author(s) -
Roh Soonyoung,
Song HwanJin
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl089444
Subject(s) - speedup , computer science , artificial neural network , cloud computing , radiation , artificial intelligence , parallel computing , physics , quantum mechanics , operating system
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.