| 基于深度学习和雷达观测的华南短临预报精度评估 |
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| 引用本文： | 梁振清,陈生.基于深度学习和雷达观测的华南短临预报精度评估[J].气象研究与应用,2020(1):41-47. |
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| 作者姓名： | 梁振清 陈生 |
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| 作者单位： | 南宁师范大学地理科学与规划学院;中山大学大气科学学院;广东省气候变化与自然灾害研究重点实验室 |
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| 基金项目： | 国家自然科学基金项目(41875182);广州科技局计划项目(201904010162);中山大学“百人计划”项目(74110-18841203);广西自然科学基金项目(2018JJA150110);南宁师范大学-高校高层次人才和教师素质提升(6020303890216);广西自然科学基金项目(2018GXNSFAA050130)。 |
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| 摘 要： | 利用最新的深度学习算法,即卷积长短期记忆(Convolution Long-Short Term Memory)神经网络,构建基于深度学习的人工智能短临预报系统,以广州地区2019年3-5月雷达观测的数据为输入进行训练,然后进行短期1h内的降水预报。利用常用的统计评分指标(探测率POD、误报率FAR、临界成功指数CSI,相关系数CC)检验模型。结果表明,预报结果与实际观测的相关系数在1h内预报均保持在0.6以上,在1h内预报探测率均保持在80%以上,临界成功指数在降水强度为10mm·h^-1时,基本保持在60%,误报率均小于40%。
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| 关 键 词： | 深度学习 神经网络 降水短临预报 精度评估 |
Accuracy evaluation of nowcasting in South China based on deep learning and radar observation |
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| Liang Zhenqing,Chen Sheng.Accuracy evaluation of nowcasting in South China based on deep learning and radar observation[J].Journal of Guangxi Meteorology,2020(1):41-47. |
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| Authors: | Liang Zhenqing Chen Sheng |
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| Institution: | (School of Geography and Planning,Nanning Normal University,Nanning 53001;School of Atmospheric Sciences,Sun Yat-sen University,Zhuhai Guangdong 519082;Guangdong Province Key Laboratory of Climate Change and Natural Disasters,Guangzhou 510275) |
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| Abstract: | The latest deep learning algorithm, namely convolution long-term short-term memory neural network, is used to construct an artificial intelligence short-time prediction forecast system. The radar observation data from March to May 2019 in Guangzhou is used as input for training, and then the short-time precipitation forecast within 1 hour is carried out. The commonly used statistical scoring indicators(POD, FAR, CSI, CC)were used to test the model. The results show that: 1) the CC between the prediction results and the actual observation are kept above 0.6;2) All the POD are above 80%;3) the CSI are basically kept at 60% when the precipitation intensity is 10 mm/h;4) All FAR are less than 40%. |
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| Keywords: | deep learning neural network short-time precipitation forecast accuracy assessment |
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