A Deep Learning-Based Wind Field Nowcasting Method With Extra Attention on Highly Variable Events

Highly variable wind fields (HWFs), which usually have drastically changing velocities over time, can seriously impact aviation safety, wind energy assessment, and so on. A recently proposed deep learning method can well predict the wind fields in ordinary cases, but its performance deteriorates when HWFs are involved. In this letter, a nowcasting method taking into account the impact of HWFs is proposed. First, standard deviations (SDs) of the lidar observations within a time interval are used to identify highly variable events. Second, a loss function weighted by the SDs is adopted to train the nowcasting neural network. Additionally, a new dimensionless metric is introduced to quantitatively measure the nowcasting performance with emphasis on HWFs. Experimental results demonstrate that the weighted loss function can efficiently improve the nowcasting performance on HWFs such as the initiation and dissipation processes of wind shear. Compared with the original nowcasting method, the network with the weighted loss function can reduce the nowcasting errors by an improvement of more than 15.2% in terms of the new metric.

Automated summary

This study proposes a nowcasting method that takes into account the impact of highly variable wind fields (HWFs). The method identifies highly variable events using standard deviations and trains the neural network with a weighted loss function. Experimental results show that this method improves the nowcasting performance on HWFs.