Wrf-ml v1.0: a bridge between wrf v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer

HIGHLIGHTS

• who: Xiaohui Zhong and colleagues from the Damo Academy, Alibaba Group, Hangzhou, China have published the article: WRF-ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer, in the Journal: (JOURNAL)
• what: Lastly, the authors compare the accuracy and computational costs of the WRF simulations coupled with ML-based radiation emulators with the WRF simulations using original RRTMG schemes. This study has trained and tested both FC networks and Bi-LSTM models.
• how: The authors developed a coupler to allow the ML . . .
  

   

  

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