# Feature Construction and Identification of Convective Wind from Doppler Radar Data

## 基于多普勒雷达数据和机器学习模型的对流大风的特征构建与识别

• Convective wind is one of the common types of severe convective weather. Identification and Forecasting of convective wind are essential. In this paper, five kinds of features are firstly constructed from characteristics of typical convective wind-related echo phenomena based on Doppler radar data. The features include storm motion, high-value reflectivity, high-value velocity, velocity shear, and velocity texture. A severe convective wind (SCW) identification model is then built by applying the above features to the random forest model. With convective wind samples collected over 13 cities of China in June–August 2016, it is found that the probability of detection (POD) of SCW is 78.9%, the false alarm ratio (FAR) is 26.4%, and the critical success index (CSI) is 61.5%. For the convective wind samples that carry typical echo features, the POD, FAR, and CSI range from 89.4% to 99.3%, 4.2% to 16.0%, and 76.4% to 95.1%, respectively. Meanwhile, the POD and negative-case POD of samples without typical echo features are 66.8% and 85.4%, respectively. The experimental results demonstrate that the SCW identification model can classify non-SCW effectively, and performs better with SCW samples carrying typical echo features than without.
对流大风是常见的强对流天气之一，对对流大风的智能识别十分重要。本文基于与对流大风相关的典型多普勒雷达回波现象的特征，构建了包括风暴运动特征、反射率高值特征、速度高值特征、速度切变特征和速度纹理特征等五种特征。在随机森林模型的基础上，利用上述特征建立了强对流大风（SCW）识别模型。以2016年6月、7月和8月中国十三个城市的对流大风事件作为样本，强对流大风的击中率（POD）达到为78.9%，误报率（FAR）为26.4%，临界成功指数（CSI）为61.5%。对于携带典型回波现象的对流风样本，其POD、FAR和CSI分别在89.4%–99.3%、4.2%–16.0%和76.4%–95.1%之间。同时，对无典型回波现象的样品的POD和负例击中率（NPOD）分别达到66.8% 和85.4%。实验结果表明本文强对流大风识别模型可以有效地对强对流大风和非强对流风进行分类，对携带典型回波现象的强对流大风样本的识别能力比对无典型回波现象的强对流大风样本更好。
• Fig. 1.  The parameter r and the positional relationship between points p and i.

Fig. 2.  Illustration of association points for circular ${\rm{LBP}}_q^R$ codes: (a) R = 1, q = 8; (b) R = 2, q = 16; and (c) R = 2, q = 8.

Fig. 3.  Frequency distribution histogram of each feature on SCW (red) and NSCW (blue) samples: (a) vCMS, (b) vCDS, (c) Ref99, (d) Rref, (e) Vel99, (f) Rvel, (g) shear features’ first principal component (Shearpca_1), and (h) texture features’ first principal component (VLBPpca_1).

Fig. 4.  Frequency distribution histograms of radial velocity high-value features in the sample set of SCW samples with SWAs (black) and without SWAs (red): (a) Rvel and (b) Vel99.

Fig. 5.  Performance of (a) ${\text{Shea}}{{\text{r}}_{{\text{pca\_1}}}}$ and (b) ${\text{VLB}}{{\text{P}}_{{\text{pca\_1}}}}$ in classifying SCW samples with/without MARC.

Fig. 6.  Performance of (a) ${\text{Shea}}{{\text{r}}_{{\text{pca\_1}}}}$ and (b) ${\text{VLB}}{{\text{P}}_{{\text{pca\_1}}}}$ on SCW samples with/without mesocyclones.

Fig. 7.  Performance of the moving speed feature in SCW sample sets with/without squall lines.

Fig. 8.  Detailed timing information for three cases, including the maximum wind speed under each individual body scan, the occurrence of typical echo phenomena, and the model’s prediction results.

Fig. 9.  Scatter diagrams for the three cases: (a) reflectivity high-value features, (b) radial velocity high-value and shear, and (c) shear and texture.

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Feature Construction and Identification of Convective Wind from Doppler Radar Data

###### Corresponding author: Di WANG, wangdi2015@tju.edu.cn;
• School of Electrical and Information Engineering, Tianjin University, Tianjin 300072
Funds: Supported by the Applied Foundation and Frontier Technology Research Program (Youth Project) of Tianjin, China (16JQNJC07500)

Abstract: Convective wind is one of the common types of severe convective weather. Identification and Forecasting of convective wind are essential. In this paper, five kinds of features are firstly constructed from characteristics of typical convective wind-related echo phenomena based on Doppler radar data. The features include storm motion, high-value reflectivity, high-value velocity, velocity shear, and velocity texture. A severe convective wind (SCW) identification model is then built by applying the above features to the random forest model. With convective wind samples collected over 13 cities of China in June–August 2016, it is found that the probability of detection (POD) of SCW is 78.9%, the false alarm ratio (FAR) is 26.4%, and the critical success index (CSI) is 61.5%. For the convective wind samples that carry typical echo features, the POD, FAR, and CSI range from 89.4% to 99.3%, 4.2% to 16.0%, and 76.4% to 95.1%, respectively. Meanwhile, the POD and negative-case POD of samples without typical echo features are 66.8% and 85.4%, respectively. The experimental results demonstrate that the SCW identification model can classify non-SCW effectively, and performs better with SCW samples carrying typical echo features than without.

Reference (27)

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