摘要
利用2009一2012年前汛期广东和香港五个探空站资料计算得到的物理量,选取各个探空站与强对流天气相关性好的物理量作为预报因子,通过对各指数的空间分布特征和数值进行二值Logistic回归分析,得到各物理量的参数估算值,分别建立五个探空站的强对流诊断预报方程,得到前汛期强对流潜势预报因子P,从而制作广东省未来12 h强对流天气潜势预报。并用此法回报了2009—2012年前汛期的强对流天气,对于P的值进行预报质量评定,以CSI评分为标准,选取五个探空站的P值的阈值,并以各个站的阈值对2013年前汛期的强对流天气进行预报质量评定,结果表明,进行拟合后的潜势预报因子P比单个物理量的CSI评分有显著提高。
Based on the radiosonde data of 5 stations in Guangdong and Hong Kong in the annually first flooding season from 2009 to 2012, some indices and parameters with good relevancy were selected as predictors. Through analyzing the spatial distributions and the binary logistic regressions of the indices, estimated values of the predictors and severe convective weather diagnostic prediction equations were established to get a severe weather predictor P for forecasting severe convection for the next 12 hours in Guangdong. The equations were tested and verified with historical data in the annually first flooding seasons from 2009 to 2012. Then 5 thresholds of 5 sites were selected by CSI to evaluate the quality of severe weather potential forecasts by each threshold of the each site. The results indicated that the CSI of severe weather forecasts by the predictor P was obviously higher than that of any other single index.
出处
《热带气象学报》
CSCD
北大核心
2016年第2期265-272,共8页
Journal of Tropical Meteorology
基金
基于GRAPES12KM的0~24小时逐时强对流落区预报技术研究(CMAYBY2014-046)
华南雷暴大风线状对流系统的数值天气预报关键技术(GYHY201406009)
我国华南春季对流性大风监测预报关键技术研究(GYHY201406002)
多源探测资料在强对流预警中综合应用技术集成(CMAGJ2014M39)共同资助
关键词
物理量
逻辑回归
强对流潜势预报
indexes
Binary Logistic Regression
severe convective weather potential