【作者】 刘京华；

【导师】 韩雷；

【作者基本信息】 中国海洋大学 ， 通信与信息系统， 2011， 硕士

【摘要】 强对流灾害天气是我国的主要灾害性天气之一,常对社会的生产、生活构成严重的威胁。近年来,针对强对流初生(Convective Initiation,CI)的监测和预警成为新的热点研究问题。此处,CI的定义为:多普勒天气雷达第一次检测到由对流云产生的反射率因子大于等于35dBZ。可以认为,CI的发生是强对流天气活动开始的标志,因而,对CI进行成功的预警,也就成为了灾害性天气监测预警的重要环节。本文主要包括以下两部分。第一部分是CI预警算法。具体步骤为:(1)利用CL多小波融合方法将卫星红外数据插值成与可见光等同的分辨率,增加红外通道数据的物理信息量;(2)结合交叉相关算法,追踪15分钟间隔的多幅卫星图像中同一像素的运动趋势,用于估计云顶亮温的时间变化趋势;(3)采用包括了IR云顶亮温,IR多通道差和IR云顶亮温/多光谱时间变化趋势的八个独立的CI判别指标来预警CI,具体为若积雨云像素满足八个CI判别指标中的七个或七个以上,则被标识为具有高度的CI可能性,也就是本文采用的计分统计方法。结果表明通过对对流云关键的IR亮温值和时间变化趋势的监测,可以提前30~45分钟预警出CI。第二部分是预警算法的评价技术。在这一部分我们用四个评价技术指标—预警成功率(POD)、虚假警报率(FAR)、风险得分(TS)、Heidke技巧得分(HSS),和主成分分析(PCA)方法对本文应用的预警CI算法进行评价,从统计分析的角度评价本文使用的八个指标计分统计方法预警CI的准确性,以及确定每一个指标对于预警CI的相对重要性。通过对统计结果的分析,验证了本文所用的预警CI算法的可靠性。更多还原

【Abstract】 Convective weather is one of the severe weathers in China and often poses serious threats on the production and life of human beings. Recently, it became a hot topic on monitoring and forecasting convective initiation(CI). CI is defined as the first occurrence of a≥35dBZ radar echo from a cumuliform cloud. CI is the beginning symbol of the strong convection weather event. Thus, the accurate forecasting of CI is important for convective weather warning. This study mainly contains two parts.The first is the CI forecasting algorithm, which is as follows: (1)Fusing visible imagery with high spatial resolution and rich texture detail to infrared imagery, based on the theory of CL multiple wavelet fusion, which can improve the spatial resolution of infrared imagery, and increase the physical information. (2)Tracking the moving trend of the same pixel in different satellite imagery of 15-min time period by the cross correlation algorithm. The goal is to assess the temporal trend in cloud-top temperature. (3)Eight predictors are used to forecast CI which include IR cloud-top brightness temperatures, IR multispectral channel differences, and IR cloud-top temperature/multispectral temporal trend. Cumulus cloud pixels for which≥7 of the 8 CI indicators are satisfied are labeled as having high CI potential. The result shows that CI may be forecasted 30-45 min in advance through this method.The second is a statistical evaluation of forecasting CI. Four measures of foresting skill which are probability of detection(POD), false-alarm ratio(FAR), threat score(TS), and Heidke skill score(HSS), and principal component analysis(PCA) are used to evaluate the CI forecast products. The goal of the statistical analysis is to confirm the accuracy of the algorithm, and that whether each CI indicator is important to forecast CI. The results show that it is reliable to use this method.更多还原