基于卫星资料观测和反演降水是当前监测全球尺度降水的主要方式,而其中一大难题就是如何将降水云与非降水云在像素尺度进行有效分离,这也是准确反演地表降水量的基本前提。为建立一套适用于常见星载可见光/红外探测仪器的降水云识别方法...基于卫星资料观测和反演降水是当前监测全球尺度降水的主要方式,而其中一大难题就是如何将降水云与非降水云在像素尺度进行有效分离,这也是准确反演地表降水量的基本前提。为建立一套适用于常见星载可见光/红外探测仪器的降水云识别方法,本文利用热带测雨卫星(TRMM)可见光/红外辐射计(VIRS)和测雨雷达(PR)的融合观测资料,针对选定的代表性区域,统计分析了较长时间尺度上降水云与非降水云的典型云属性差异。在此基础上,提出了一种基于云光学厚度和云滴有效半径的白天降水云识别方案(IPCτRe)。由于用来获取上述云参数的可见光/红外信号无法透过降水性云层,此方案不受下垫面条件的影响,适用于陆地和海洋区域。为验证IPCτRe方案的降水云识别效果,本文以PR瞬时降水探测结果为真值,采用三种二元预报评价因子对识别结果进行了定量评估,并与Inoue and Aonashi(2000)和Nauss and Kokhanovsky(2006)提出的降水云识别方案进行了比较。研究表明,IPCτRe方案的降水云识别性能均高于其它两种方案。特别是在洋面上,降水云识别比例达到84%,而对非降水云的误判率只有6%,到达了降水卫星监测和预报业务所要求的精度。展开更多
Visible and infrared(VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, ...Visible and infrared(VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal resolutions. Combined measurements from visible/infrared scanner(VIRS) and precipitation radar(PR) aboard the Tropical Rainfall Measuring Mission(TRMM) satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness(COT), effective radius(Re), and brightness temperature of VIRS channel 4(BT4), are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Parameters information(PISCP). It is essentially a lookup table(LUT) approach that is deduced from the optimal equitable threat score(ETS) statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the performance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic areas. Especially, over ocean, precipitating clouds(PCs) and non-precipitating clouds(N-PCs) are well distinguished by PISCP, with the probability of detection(POD) near 0.80, the probability of false detection(POFD) about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.展开更多
文摘基于卫星资料观测和反演降水是当前监测全球尺度降水的主要方式,而其中一大难题就是如何将降水云与非降水云在像素尺度进行有效分离,这也是准确反演地表降水量的基本前提。为建立一套适用于常见星载可见光/红外探测仪器的降水云识别方法,本文利用热带测雨卫星(TRMM)可见光/红外辐射计(VIRS)和测雨雷达(PR)的融合观测资料,针对选定的代表性区域,统计分析了较长时间尺度上降水云与非降水云的典型云属性差异。在此基础上,提出了一种基于云光学厚度和云滴有效半径的白天降水云识别方案(IPCτRe)。由于用来获取上述云参数的可见光/红外信号无法透过降水性云层,此方案不受下垫面条件的影响,适用于陆地和海洋区域。为验证IPCτRe方案的降水云识别效果,本文以PR瞬时降水探测结果为真值,采用三种二元预报评价因子对识别结果进行了定量评估,并与Inoue and Aonashi(2000)和Nauss and Kokhanovsky(2006)提出的降水云识别方案进行了比较。研究表明,IPCτRe方案的降水云识别性能均高于其它两种方案。特别是在洋面上,降水云识别比例达到84%,而对非降水云的误判率只有6%,到达了降水卫星监测和预报业务所要求的精度。
基金supported by the National Basic Research Program of China (Grant No. 2010CB428601)the Strategic Priority Research Program-Climate Change (Carbon Budget and Relevant Issues of the Chinese Academy of Sciences) (Grant No. XDA05100303)+2 种基金the Fundamental Research Funds for the Central Universities (Grant No. WK2080000024)the National Natural Science Foundation of China (Grant Nos. 41230419, 41175032 and 41075041)the Guangdong Science and Technology Plan Project (2012A061400012, 2011A032100006)
文摘Visible and infrared(VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal resolutions. Combined measurements from visible/infrared scanner(VIRS) and precipitation radar(PR) aboard the Tropical Rainfall Measuring Mission(TRMM) satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness(COT), effective radius(Re), and brightness temperature of VIRS channel 4(BT4), are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Parameters information(PISCP). It is essentially a lookup table(LUT) approach that is deduced from the optimal equitable threat score(ETS) statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the performance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic areas. Especially, over ocean, precipitating clouds(PCs) and non-precipitating clouds(N-PCs) are well distinguished by PISCP, with the probability of detection(POD) near 0.80, the probability of false detection(POFD) about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.
