Numerical weather prediction revisions using the locally trained differential polynomial network
作者：
Highlights：
• Differential polynomial neural network (D-PNN) extends the GMDH network structure.
• D-PNN constructs and solves the general partial differential equation with sum series.
• D-PNN is trained with historical time-series for actual local weather data relations.
• The correction model can apply NWP outputs to revise one target 24-hour forecast.
• NWP model revisions of the temperature, relative humidity and dew point were done.
摘要
•Differential polynomial neural network (D-PNN) extends the GMDH network structure.•D-PNN constructs and solves the general partial differential equation with sum series.•D-PNN is trained with historical time-series for actual local weather data relations.•The correction model can apply NWP outputs to revise one target 24-hour forecast.•NWP model revisions of the temperature, relative humidity and dew point were done.
论文关键词：Polynomial neural network,Differential equation composition,Relative substitution derivative term,Multi-variable function approximation
论文评审过程：Received 28 May 2014, Revised 30 August 2015, Accepted 31 August 2015, Available online 21 September 2015, Version of Record 10 November 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.08.057
