基于随机森林的组网雷达龙卷检测算法

近年来我国极端灾害性天气频发，造成了重大人员伤亡和财产损失，随着防灾减灾工作的推进，龙卷等中小尺度强对流灾害性天气的预警预报工作的关注度正逐步提升。现有龙卷检测算法基于对新一代天气雷达基数据在多个仰角和体积扫描中进行阈值判断得到龙卷涡旋特征TVS，在自适应协同观测背景下表现为自适应策略同步较慢，预警预报准确率不高，提前预警时间短。使用机器学习算法结合龙卷在雷达反射率、径向速度和速度谱宽的多重特征能有效提高龙卷识别的准确率和预警时间，能提高组网雷达的协同观测能力。基于随机森林的龙卷检测算法（TDA-RF），使用CINRAD雷达历史龙卷数据作为训练集，通过随机森林算法对训练集进行分类学习得到龙卷预测模型，使用预测模型对实时雷达数据进行龙卷检测。试验结果表明，TDA-RF算法能有效识别不同强度的龙卷，较TVS龙卷检测算法能给出龙卷区域的分类概率值，无需对龙卷特征时空连续性进行判断；TDA-RF算法对多个特征进行综合判断具有较好的抗干扰能力，使基于组网雷达的龙卷预警时间最高可达18分钟。

TORNADO DETECTION ALGORITHM BASED ON RANDOM FOREST IN RADAR NETWORK

In recent years, extreme severe weather has frequently occurred in China, causing heavy casualties and property losses. As a result, a growing focus has been on the early warning and forecasting of tornadoes and other small to medium-scale severe convective weather events as part of national disaster prevention and mitigation efforts. However, the current tornado detection algorithm exhibits limitations in terms of accuracy and warning time. This algorithm relies on the threshold judgment of the new generation weather radar base data in multiple elevation angles and volume scans to obtain the tornado vortex signature (TVS). The TVS algorithm''s tornado warning forecasts are characterized by low accuracy, and the early warning time is short due to the sudden onset of tornadoes and their short generation and lysis duration. The integration of a machine learning algorithm with the multiple features of the tornado, including radar reflectivity, radial velocity, and velocity spectrum width, can effectively improve the accuracy and warning time of tornado recognition. In the present study, the tornado detection algorithm based on random forest (TDA-RF) utilizes CINRAD radar historical tornado data as the training set, classifies the training set through the random forest algorithm to generate a tornado prediction model, and employs the prediction model to detect tornadoes in real-time radar data. The test results show that the TDA-RF algorithm can effectively identify tornadoes of varying intensities. Compared with the TVS tornado detection algorithm, it can calculate the classification probability value of the tornado area. TDA-RF does not require the judgment of the spatiotemporal continuity of tornado features. It is less affected by false and invalid echoes, and the early warning time can reach up to 18 minutes.