水利水电技术（中英文）2024，Vol.55Issue(11)：1-14,14.DOI:10.13928/j.cnki.wrahe.2024.11.001
结合Causal-LSTM单元的CrevNet深度学习模型在对流降水临近预报中的试验研究
Experimental study on convective precipitation nowcasting based on crevnet deep learning model combined with causal-lstm unit
摘要
Abstract
[Objective]Mesoscale convective precipitation prediction is one of the key and difficult objects of weather forecasting.Precipitation data detected by weather radar has high spatiotemporal resolution,which is the main data source for nowcasting in 0～2 hours.It is of great significance for high-resolution rainfall forecasting in small and medium-sized watersheds.The use of ra-dar data to carry out nowcasting of convective precipitation can facilitate peoples travel,agricultural production guidance,disas-ter prevention and mitigation,and has practical application value in the field of meteorology and hydrology.[Methods]Based on the volumetric scan data of the new generation S-band Doppler radar in Guangzhou,this study will explore the prediction perform-ance of the conditional reversible network CrevNet based on the Causal-LSTM memory module in convective precipitation nowcast-ing,and then compare the prediction effect with the model based on ordinary ST-LSTM to verify its superiority.To improve the memory ability of strong echoes,the model was trained with weighted Huber loss function.In this study,the CSI(Critical Suc-cess Index),POD(Probability of Detection,or Hit Ratio)and FAR(False Alarm Rate)were used to evaluate the result of the test dataset under different prediction time and test thresholds,and PSNR(Peak signal-to-noise ratio),SSIM(image structure similarity)and BIAS(bias scores)were used to test the predictive ability of the convective event.[Results]The result show that the CrevNet model based on Causal-LSTM memory unit has higher CSI and POD,and lower FAR during the forecast period.In the prediction of two convection cases,the model has a higher PSNR,SSIM and a BIAS closer to 1 under multiple prediction timeliness.[Conclusion]The CrevNet deep learning model of conditional reversible network has a strong ability to extract spatio-temporal features for spatiotemporal sequences,and the prediction effect will be different with different convolutional recurrent neural units.Therefore,the CrevNet model based on the Causal-LSTM memory unit can better preserve the convective echo mor-phology and is more suitable for convective precipitation nowcasting.关键词
对流临近预报/深度学习/CrevNet/Huber损失/雷达反射率/降水/气候变化/防灾减灾Key words
convection nowcasting/deep learning/CrevNet/Huber loss function/radar reflectivity/precipitation/climate change/disaster prevention and reduction分类
天文与地球科学引用本文复制引用
张永轩,黄兴友,王雪婧,于华英,楚志刚..结合Causal-LSTM单元的CrevNet深度学习模型在对流降水临近预报中的试验研究[J].水利水电技术（中英文）,2024,55(11):1-14,14.基金项目
国家重点研发计划项目"重大自然灾害监测预警"课题(2018YFC1506102) （2018YFC1506102）
山东省自然科学基金双偏振雷达资料在强对流天气中的应用研究(ZR2020MD053) （ZR2020MD053）
