陈估鑫. L波段雷达资料在保山市强降水预报中的应用[J]. 云南大学学报(自然科学版), 2015, 37(6): 878-883. doi: 10.7540/j.ynu.20150299
引用本文: 陈估鑫. L波段雷达资料在保山市强降水预报中的应用[J]. 云南大学学报(自然科学版), 2015, 37(6): 878-883. doi: 10.7540/j.ynu.20150299
CHEN Gu-xin. The application of L band radar data in the heavy rainfall forecast of Baoshan City,Yunnan[J]. Journal of Yunnan University: Natural Sciences Edition, 2015, 37(6): 878-883. DOI: 10.7540/j.ynu.20150299
Citation: CHEN Gu-xin. The application of L band radar data in the heavy rainfall forecast of Baoshan City,Yunnan[J]. Journal of Yunnan University: Natural Sciences Edition, 2015, 37(6): 878-883. DOI: 10.7540/j.ynu.20150299

L波段雷达资料在保山市强降水预报中的应用

The application of L band radar data in the heavy rainfall forecast of Baoshan City,Yunnan

  • 摘要: 为加强基层短时强降水预报能力,结合前人研究,利用20082009年腾冲L波段雷达探测资料和保山市昼(夜)降水数据,统计分析了该市昼(夜)出现强降水时当日(前一日)沙氏指数、抬升指数、不同高度层的温度露点差情况.结果表明,保山市强降水与沙氏指数、温度露点差呈显著负相关,与抬升指数值呈显著正相关.采用相关性和显著性假设检验确定了6项强降水预报因子和它们各自对应预报值域后,将6种预报因子自由组合成8种预报判据.经预报质量检验从8种组合中挑选出了保山市昼(夜)间出现强降水的预报判据.以20102014年保山市强降水实况进行预报能力检验,该方法对夜雨和昼雨强降水的预报准确率分别为56.00%和60.00%,具有一定参考使用价值.

     

    Abstract: In order to strengthen the forecasting ability of short time heavy rainfall, this paper uses L band radar detection data in Tengchong of 20082009 and precipitation data in Baoshan City to examine the relationship between precipitation and SI, LI, t-td during the day and night.It has been found that the intensity of precipitation was negatively correlated with t-td and SI, and positively correlated with LI.Using correlation and significance test method applied to determine the numerical value of 6 kinds of prediction factors and combination of forecasting factors into 8 kinds of forecasting methods.After comparison, in light of the forecast method of Baoshan City during the day or at night, the forecasting ability of the method of heavy rainfall data in Baoshan City during 20102014 has been tested.The correct rate during the day and night is 56.00% and 60.00%.Inspection certificates, this method is of a gtreat value as for the disaster weather forecast.

     

/

返回文章
返回