Very short-range lightning forecasting with NWP and observation data: A deep learning approach

KeyWord:Convective weather;Short-term forecast;Deep learning;Observation data;Numerical weather prediction

Abstract:

      The very short-range (VSR, 2—6 h) convective weather forecasting is still a great challenge. On the one hand, the extrapolation of observation data is no longer available. On the other hand, the High-resolution Numerical Weather Prediction (HNWP) performance needs to be further improved. To address the above issues, a semantic segmentation deep learning network named LightningNet-NWP is implemented to merge the multi-source observation data with HNWP data to get better VSR lightning forecasts. The predictors of the LightningNet-NWP include lightning density, radar reflectivity, 6 infrared bands of Himawari-8 and the radar composite reflectivity from GRAPES_3km. Because the observations and HNWP data differ a lot, two encode-decode symmetry sub-networks were designed to extract future information from the above two data sources. The pooling index is shared in upsampling process, so that the details of shallow feature maps are transmitted and fully used. Three dimensional convolutional layers are utilized to extract spatial and temporal features. The experimental results show that the LightningNet-NWP can effectively combine observations and HNWP data and yield a good lightning prediction for the next 0—6 hours. The performance of the LightningNet-NWP combined with observations and HNWP data is much better than that solely using observations or HNWP data. The longer the prediction period, the more advantageous the combinational use of observations and HNWP data.