Abstract: Concerning the uneven density distributed lightning location data, a lightning nowcasting model based on Ordering Points To Identify the Clustering Structure (OPTICS) algorithm was proposed. The model analyzed continuous period of lightning location data with OPTICS. It effectively filtered out the sparse points that would affect the lightning clouds distribution. Based on the lightning clusters produced by OPTICS, the model used dilate-corrode algorithm to restore real distribution of lightning clouds. Then future lightning location area was predicted according to the moving trend of lightning clouds. Furthermore, to overcome the traditional algorithm's drawback of consuming longer time, adjacent list and improved seed-list updating strategy were introduced into the OPTICS algorithm. The experimental results show that OPTICS based model is more applicable for lightning nowcasting, and achieves higher accuracy and lower time consumption.

Key words: lightning nowcasting, location data, clustering analysis, Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, moving trend

摘要： 针对密度分布不均的雷电定位资料,提出了一种基于OPTICS聚类算法的雷电临近预警模型。该模型运用OPTICS算法对雷暴天气连续时段的雷电定位资料进行聚类分析,有效剔除了影响雷暴云分布的稀疏点。在聚类分析结果基础上,利用“膨胀〖CD*2〗侵蚀”算法还原雷暴云真实分布,根据雷暴云的移动趋势进行雷电落区预报。此外,针对传统预测算法运行时间长的缺陷,运用邻接表改进了OPTICS算法,且优化了可达队列更新策略。实验结果表明,基于改进的OPTICS算法所构建的雷电临近预报模型降低了算法运行时间,同时提高了雷电预报模型适应能力及预测的准确率。

关键词: 雷电临近预报, 定位资料, 聚类分析, OPTICS算法, 移动趋势