利用深度学习融合NWP和多源观测数据的闪电落区短时预报方法

强对流短时预报(2—6 h)具有较大难度.一方面,基于观测数据的外推已基本不可用;另一方面,高分辨率数值模式(High-resolution Numerical Weather Prediction,HNWP)的预报性能有待提升.利用深度学习方法,将卫星、雷达、云-地闪电(简称闪电)等观测数据和高分辨率数值模式预测数据...

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

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.