Deep learning for downward longwave radiative flux forecasts in the Arctic
作者：
Highlights：
• Downward longwave radiative flux (LWD) simulation in the Arctic has uncertainties.
• Deep learning was used to improve LWD simulations in the Arctic.
• After deep learning, the root mean square errors of LWD simulations were reduced.
• Deep learning reduces uncertainties in LWD simulations in the Arctic.
• A deep learning model trained with a model data can be applied to other model data.
摘要
•Downward longwave radiative flux (LWD) simulation in the Arctic has uncertainties.•Deep learning was used to improve LWD simulations in the Arctic.•After deep learning, the root mean square errors of LWD simulations were reduced.•Deep learning reduces uncertainties in LWD simulations in the Arctic.•A deep learning model trained with a model data can be applied to other model data.
论文关键词：Deep learning,Downward longwave radiative flux,The Arctic,Reduction of forecast uncertainties
论文评审过程：Received 25 January 2022, Revised 18 July 2022, Accepted 12 August 2022, Available online 17 August 2022, Version of Record 27 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118547
