基于ROSE2.0的普洱地区CINRAD/CC雷达冰雹探测算法评估及参数本地化

为提高冰雹探测算法（Hail Detection Algorithm，HDA）产品的可用性，针对2015—2020年普洱地区监测到的22次冰雹个例，利用新一代雷达业务应用软件ROSE2.0对相关雷达基数据进行回放及产品分析，以命中率、虚警率、临界成功指数为指标对HDA算法在普洱地区的识别效果进行评估并给出本地化参数配置方案。结果表明:HDA算法在普洱地区命中率接近100%，但虚警现象非常普遍，使用强冰雹概率（Probability of Severe Hail，POSH）的预警效果优于任意大小冰雹概率（Probability of Hail，POH），且冰雹尺寸越大POSH虚警的概率越低。进一步使用模拟测评法对POSH算法的适配参数进行分析，发现正确输入降雹日当天的0 ℃层和-20 ℃层高度能有效减少POSH的虚警率及提高临界成功指数；同时使算法预测的最大冰雹直径普遍偏大的情况得到控制，其中，中小冰雹直径偏离百分比减小76.07%，改善效果显著高于大冰雹。此外，增大反射率因子及POSH阈值能有效控制虚警，但也导致漏报次数快速增加，当阈值太大时命中率明显降低，为了保证较高的命中率和临界成功指数，选择Z=50 dBz或POSH=70%为阈值能明显改善HDA算法的识别效果。

Evaluation of CINRAD/CC Radar Hail Detection Algorithm and Parameter Localization in Pu’er on ROSE2.0

In order to apply the Hail Detection Algorithm (HDA) related products more extensively and correctly, for the 22 hail cases monitored in Pu’er area from 2015 to 2020, the new Radar Operational Software Engineering (ROSE2.0) is used to replay radar-based data and analyse the relevant products. The recognition effect of the HDA algorithm in the Pu’er area is evaluated with the probability of detection (POD), false alarm rate (FAR), and critical success index (CSI), and a localised parameter configuration scheme is provided after that, which is useful to improve the local hail warning ability. The results show that although the POD of the HDA algorithm in Pu’er area is close to 100%, there are also many ordinary storm cells that are identified as hail cells mistakenly. The number of false alarms is very huge, and the low CSI cannot meet the requirement of the weather forecasting operation. The warning effect of using Probability of Severe Hail (POSH) is better than that of Probability of Hail (POH) for any size of hail, and the larger the size of hail, the lower the probability of false alarm of POSH. Further analysis of the adaptation parameters of the POSH algorithm by a simulation test method shows that the height of the 0 ℃ and -20 ℃ layers has a significant impact on the recognition ability of POSH, the original default value is significantly lower in Pu’er area, correctly inputting the height of 0 ℃ and -20 ℃ layers on the day of hail can effectively reduce the FAR and improve the CSI of POSH; at the same time, it can control the situation that the maximum hail diameter predicted by the algorithm is generally too large, and the maximum expected hail size (MEHS) is closer to the observation value; the deviation percentage of small and medium-sized hail diameter decreases by 76.07%, with a significantly higher improvement effect than large hail, but the diameter prediction error of MEHS for large hail is smaller. In addition, increasing the reflectivity factor and POSH threshold can effectively control FAR, but it also leads to a rapid increase in the number of missed alarms. When the threshold is too large, the POD significantly decreases. In order to achieve a higher POD and CSI, selecting Z=50 dBz or POSH=70% as the threshold can improve the recognition effect of the HDA algorithm. Setting the optimal threshold of multiple parameters at the same time can effectively improve the recognition ability of the HDA algorithm in Pu’er.