Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-199-2023
https://doi.org/10.5194/gmd-16-199-2023
Model description paper
 | 
06 Jan 2023
Model description paper |  | 06 Jan 2023

WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer

Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang

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Cited articles

Belochitski, A. and Krasnopolsky, V.: Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model, Geosci. Model Dev., 14, 7425–7437, https://doi.org/10.5194/gmd-14-7425-2021, 2021. a
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Chantry, M., Hatfield, S., Dueben, P., Polichtchouk, I., and Palmer, T.: Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting, J. Adv. Model. Earth Sy., 13, e2021MS002477, https://doi.org/10.1029/2021MS002477, 2021. a
Chevallier, F., Chéruy, F., Scott, N., and Chédin, A.: A neural network approach for a fast and accurate computation of a longwave radiative budget, J. Appl. Meteorol., 37, 1385–1397, 1998. a
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More and more researchers use deep learning models to replace physics-based parameterizations to accelerate weather simulations. However, embedding the ML models within the weather models is difficult as they are implemented in different languages. This work proposes a coupling framework to allow ML-based parameterizations to be coupled with the Weather Research and Forecasting (WRF) model. We also demonstrate using the coupler to couple the ML-based radiation schemes with the WRF model.