Forecasting Different Types of Convective Weather: A Deep Learning Approach
Forecasting Different Types of Convective Weather: A Deep Learning Approach
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DOI：
10.1007/s13351-019-8162-6
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发表时间：
2019-10-01
影响因子：
3.2
通讯作者：
Zhang, Xiaoling
中科院分区：
文献类型：
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作者：
Zhou, Kanghui;Zheng, Yongguang;Zhang, Xiaoling
针对短时暴雨、冰雹、对流性阵风和雷暴等强对流天气，提出了一种基于数值天气预报（NWP）数据的深度学习客观预报方案。我们首先建立了如下的训练数据集。五年的恶劣天气观测被用来标记NCEP最终（FNL）分析数据。然后选择每种天气类型的大量标记样本进行模型训练。在我们的模式中，当地的温度，压力，湿度，风从1000到200 hPa，以及几十个对流物理参数，作为预测因子。然后构建并训练六层卷积神经网络（CNN）模型以获得最佳模型权重。最后，以全球天气预报系统（GFS）的预报数据为输入，利用训练好的模型对北京夏季风进行预测。比较了CNN模型与其他传统方法的性能。实验结果表明，与支持向量机、随机森林等传统机器学习算法相比，深度学习算法对HR和冰雹的分类精度更高。使用深度学习算法的客观预测也比预测人员的主观预测表现出更好的预测技巧。雷暴、HR、冰雹和CG的威胁得分（TS）分别增加了16.1%、33.2%、178%和55.7%。深度学习预报模型目前在中国国家气象中心使用，为中国的业务SCW预报提供指导。
A deep learning objective forecasting solution for severe convective weather (SCW) including short-duration heavy rain (HR), hail, convective gusts (CG), and thunderstorms based on numerical weather prediction (NWP) data was developed. We first established the training datasets as follows. Five years of severe weather observations were utilized to label the NCEP final (FNL) analysis data. A large number of labeled samples for each type of weather were then selected for model training. The local temperature, pressure, humidity, and winds from 1000 to 200 hPa, as well as dozens of convective physical parameters, were taken as predictors in our model. A six-layer convolutional neural network (CNN) model was then built and trained to obtain optimal model weights. After that, the trained model was used to predict SCW based on the Global Forecast System (GFS) forecast data as input. The performances of the CNN model and other traditional methods were compared. The results show that the deep learning algorithm had a higher classification accuracy on HR and hail than support vector machine, random forests, and other traditional machine learning algorithms. The objective forecasts by use of the deep learning algorithm also showed better forecasting skills than the subjective forecasts by the forecasters. The threat scores (TSs) of thunderstorm, HR, hail, and CG were increased by 16.1%, 33.2%, 178%, and 55.7%, respectively. The deep learning forecast model is currently used in the National Meteorological Center of China to provide guidance for the operational SCW forecasting over China.