| 基于循环神经网络的单站能见度短临预报试验 |
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| 引用本文： | 李昕蓓,张苏平,衣立,韩美,潘宁.基于循环神经网络的单站能见度短临预报试验[J].山东气象,2019,39(2):76-83. |
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| 作者姓名： | 李昕蓓 张苏平 衣立 韩美 潘宁 |
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| 作者单位： | 1.中国海洋大学海洋与大气学院，山东 青岛 266101； 2.福建省气象台，福建 福州 350000 |
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| 基金项目： | 国家自然科学基金项目（41576108，41605006）；山东省优秀中青年科学家奖励基金项目（ZR2016DB26）；福建省气象灾害监测预警系统项目 |
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| 摘 要： | 由于能见度具有局地性和复杂的非线性变化特征，一直是精细化预报的难点。人工神经网络对复杂变化过程的模拟能力较高，为解决这一难题提供了可能性。本文采用循环神经网络，利用福州气象观测站地面观测数据，建立了福州单站能见度短临预报模型，并就预报能力进行了评估。随机检验结果表明,在1 h、3 h、6 h时效上,循环神经网络的预报与观测的变化趋势一致性较好；均方根误差比基于实况的预报分别减小15.75%、31.66%、41.26%，说明具备较好的预报能力；平均绝对值误差比传统BP神经网络分别减小12.90%、24.45%、 38.99%，表明循环神经网络对能见度预报具有优势，为能见度的精细化短临预报提供了新途径。
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| 关 键 词： | 能见度 神经网络 短临预报 |
| 收稿时间： | 2019/3/2 0:00:00 |
| 修稿时间： | 2019/4/17 0:00:00 |
Short-time forecasting and nowcasting of visibility at a single station based on Recurrent Neural Network |
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| LI Xinbei,ZHANG Suping,YI Li,HAN Mei,PAN Ning.Short-time forecasting and nowcasting of visibility at a single station based on Recurrent Neural Network[J].Journal of Shandong Meteorology,2019,39(2):76-83. |
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| Authors: | LI Xinbei ZHANG Suping YI Li HAN Mei PAN Ning |
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| Institution: | 1. College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China; 2. Fujian Meteorological Observatory, Fuzhou 350000, China |
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| Abstract: | The fined forecasting of visibility is difficult due to its local scale and complicated nonlinear variations. ANN (Artificial Neural Network) performs well in simulating complicated variation processes and thus is feasible for solving this problem. The study employs RNN (Recurrent Neural Network) to build a short-time forecasting and nowcasting model of visibility at a single station using surface observations of Fuzhou Meteorological Observation Station and its forecasting skill is evaluated. The results from random test samples show that the variation tendency forecast of visibility by RNN is basically in conformity with the observed data in the 1-h, 3-h, and 6-h forecast; compared with the forecast based on the actual situation, the RMSE (root mean square error) decreases by 15.75%, 31.66%, and 41.26%, respectively; compared with the forecast based on the traditional BPNN (Back Propagation Neural Network), the MAE (mean absolute error) decreases by 12.90%, 24.45%, and 38.99%, respectively. The results indicate that RNN has advantages in the forecasting of visibility, providing a new method for the refined short-time forecasting and nowcasting of visibility. |
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| Keywords: | visibility neural network short time forecasting and nowcasting |
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