基于热带测雨卫星光谱观测的云参数反演及降水云识别研究
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摘要
随着更多更先进的气象卫星投入使用以及卫星遥感技术的不断发展,云和降水的宏微观特征以及它们在整个气候系统中所起的作用被逐渐揭示,而基于多仪器多通道的联合观测来研究云和降水已然成为当前大气遥感和气候变化领域的研究热点。本研究基于热带测雨卫星(TRMM)搭载的测雨雷达(PR)与可见光/红外扫描仪(VIRS)的融合资料,凭借PR高信度的降水探测和VIRS高分辨率的云顶辐射信息,并借助于可靠的云检测和云参数反演技术,提出了三套基于云参数信息的白天降水云识别方案,研究了云参数与降水之间的关系,并在此基础上重点分析和比较了全球热带和副热带地区降水云与非降水云的空间分布特征以及两者在云属性上的气候差异,初步取得的结果包括以下三个方面：
1、基于云参数信息的白天降水云识别方案
基于时空匹配的降水与云辐射信号资料,将云参数与降水信息相结合,统计不同云参数组合与降水发生概率之间的关系,在此基础上初步构建了三套基于云参数信息的白天降水云识别方案：IPCτzRe、IPCTC和PISCP,实现对降水云和非降水云像素快速而又准确的判别。
研究结果显示,无论是陆地还是海洋区域,三种方法中,PISCP法对降水云的识别效果最佳,IPCTC法次之,IPCτRe法识别性能最弱。其中PISCP、IPCTC和IPCτRe三种方法在陆地区域的ETS评估因子值分别为：0.37、0.36和0.34,而在洋面上的识别精度要好于陆地,相应ETS评估因子值分别为：0.44、0.42和0.40。
与现有同类降水云识别方案进行对比,结果显示,所提出的三种方案的降水云识别精度均优于现有识别方法,尤其是PISCP方案,其识别精度已接近被动微波仪器的探测精度。研究结果还表明,这三种方案的适用范围较广,可移植至全球其它区域。
IPCτRe、IPCTC和PISCP方案均适用于常见星载可见光/红外探测仪器,为日常降水卫星监测和预报业务提供了新的方法,同时也为深入研究云参数与降水的关系做了很好的铺垫。
2、暴雨云团云参数与降水的关系
针对夏季中国东部的暴雨云团个例,建立时空匹配的降水廓线与云参数融合资料,考察了云参数与降水概率之间的联系,并深入分析了云参数与地表降水强度及降水垂直结构的关系。
研究结果显示,在暴雨云团中,降水云的冰云比例(-87%)远高于非降水云(-40%)。降水云的光学厚度和云水路径都远大于非降水云,云顶温度也明显低于非降水云,而两者在云滴有效半径上的差异较小。随着光学厚度和云水路径的增大及云顶温度的降低,降水概率均显著增加。这其中,云水路径和云顶温度对降水云有较好的指示能力,而有效半径信息的指示能力较弱。
无论是层云降水还是对流降水,随着地表降水强度的增大,相应降水云的光学厚度、有效半径和云水路径均随之增大,而云顶温度也随之降低。对于给定的地表降水强度,对流降水的光学厚度和云水路径要低于层云降水,而两类降水的有效半径变化完全一致。
层云降水和对流降水的降水廓线对各云参数变化的响应特性相似,即随着云光学厚度、云滴有效半径和云水路径的增大以及云顶温度的降低,都对应有雨顶高度的升高以及5km以下各高度上降水率的一致增大。有所不同的是,有效半径仅在小于15μm时,降水廓线才对其变化有明显的响应。当其云水路径较低时,对流降水在4km至2km的高度层存在显著的雨滴蒸发现象。
该部分研究工作增进了对云参数与降水之间关系的认识,并为后续深入分析降水云与非降水云在云属性上的差异奠定了基础。
3、降水云与非降水云的云属性差异
在气候尺度上分析了降水云与非降水云的空间分布特征,并结合NCEP再分析资料,考察了决定降水云空间分布的关键气象要素因子,着重比较了降水云与非降水云的云参数属性在均值及空间分布上的差异。
研究结果显示,在全球(热带)范围内,降水频率高值区对应的云量一般都在80%以上,然而云量高值区相应的降水频率不一定高,有些高云量区甚至几乎不发生降水。降水类型中,层云降水出现比例最高(79%),对流降水次之(21%),其它类型降水比例最低(-0.5%)。全球(热带)地区的日均降水量约为2.6mm/d,这其中层云降水和对流降水的贡献率较为接近,分别为53%和47%,而其它类型降水的贡献极小(<0.1%)。
结合NCEP再分析资料发现,中层600hPa等压面的相对湿度高/低值中心与总降水频率的高/低值区有较好的对应,两者的纬向相关系数为0.90,经向相关系数为0.60。而高层200hPa等压面的辐散/辐合中心与总降水频率的高/低值区的对应更为一致,两者的纬向相关系数高达0.98,而经向相关系数也有0.88。可见行星尺度水汽输送和大气环流形势,是决定全球总降水频率的分布形式的主导因素,其中大尺度的气流上升／下沉运动更是决定性因素。
研究结果表明,大部分低云降水是浅薄孤立降水,比例在60%以上,局部地区的比例接近100%,这些浅薄孤立降水的降水强度和空间尺度都很小。研究结果还显示,中层600hPa与高层200hPa等压面的垂直速度差分布与中云降水频率分布有较好的对应,相应的纬向相关系数为0.61,经向相关系数为0.51。高层200hPa等压面的垂直速度分布与高云降水频率分布更为一致,其纬向相关系数高达0.94,经向相关系数为0.83。这表明,中层(高层)的气流上升/下沉运动是决定中层(高层)降水的主导因素。
在全球(热带)范围内,非降水云的云量要远远高于降水云,两者云量比值在5倍以上。非降水云与降水云的云量高值区分布并不完全一致,但非降水云的云量低值区相应的降水云云量也非常低。统计结果还显示,随着云顶高度升高(云顶气压下降),云量比值整体下降,即云顶越高,越容易产生降水。
研究结果显示,降水云与非降水云在光学厚度上的差异非常显著,两者差值在60以上。此外,两者光学厚度差值的空间分布与总降水频率及日均降水量较为相似,即降水频率/日均降水量越大,两者光学厚度差值越大。虽然在洋面上降水云的有效半径明显大于非降水云(差值为6～8μm),但在陆地上两者的差异不明显(差值为0～2μm)。分段统计结果显示,在不同光学厚度段,降水云的有效半径都要大于非降水云,导致两者有效半径在陆地上差异不明显的原因,是在于两者的光学厚度谱分布不同。
研究结果还表明,降水云与非降水云在云水路径的差异要明显大于两者在光学厚度和有效半径上的差异。降水云与非降水云的云顶温度差值空间分布呈现明显的空间不均匀性,整体而言,降水云的云顶温度(245K)要明显低于非降水云(272K)。统计还表明,降水云的冰云比例(-76%)要远高于非降水云(-23%)。此外,降水云和非降水云在云顶气压和云顶高度的全球分布形态与云顶温度较为相似。
该部分研究工作一方面在气候尺度上分析了降水云与非降水云在空间分布特征,并结合NCEP再分析资料,首次揭示了决定全球总降水、中层和高层降水空间分布的主导气象要素因子,还分析了低云降水与浅薄孤立降水之间的关系。另一方面,定量给出了降水云与非降水云在云量、云光学厚度、冰水云比例、云滴有效半径、云顶温度、云顶气压等参数的气候均值和海陆差异,并首次给出了两者上述云属性差异的空间分布特征。
Along with the applications of various advanced meteoroligcal satelliate platforms and advancements of remote sensing techniques, the macro and micro characteristics of clouds and precipitation, as well as the roles played by them in the climate system are revealed step by step. Analyses of clouds and precipitation based on the joint observations of multi-instrument and multi-channel observations has been an active research field in atmospheric remote sensing and climate change. In this study, based on the TRMM PR and VIRS measurements, which provide high-quality precipitation measurements and refined radiative information from cloud top, together with reliable cloud detection and cloud parameters retrieval techniques, the climatological characteristics of precipitating clouds (PCs) and non-precipitating clouds (NPCs) was investigated within a long time and a global scale. Three daytime precipitating clouds identification schemes from cloud parameters information have been proposed, and the relationships between cloud parameters and precipitation have been studied. Particularly, the differences of cloud parameters between PCs and NPCs were emphasized. The preliminary results on three aspects are presented as following.
(1) Daytime PCs identification scheme from cloud parameters information
Based on synchronous precipitation and radiative measurements, the precipitation information derived from PR and the cloud parameters retrieved from VIRS were compared and the relationships between cloud parameters and precipitation probability were analyzed. Three frameworks for determining the occurrence of precipitation in daytime, called Identification of Precipitating Clouds from Optical Thickness and Effective Radius (IPC rRe), Identification of Precipitating Clouds from Thermal Infrared Brightness Temperature and Cloud Parameters (IPCTC), Precipitation Identification Scheme from Cloud Parameters information (PISCP), respectively, were developed.
It was proved that PISCP performed better than IPCTC and IPC τRe both in land and oceanic areas. The performance of IPCTC was followed by PISCP, and IPC τRe a performed worse than PISCP and IPCTC. Over land area, the equitable threat score (ETS) values of PISCP, IPCTC and IPC τRe were0.37,0.36and0.34, respectively. The performances of the three schemes in oceanic areas were all better than land areas, and the ETS values were0.44,0.42and0.40.
Compared with existing similar precipitation identification schemes, the accuracies of the three proposed schemes were better, especially of PISCP, which was close to the accuracy of passive microwave instruments. The results also showed that the three proposed schemes cloud widely applied in other regions of the world.
(2) Relationships between cloud parameters and precipitation in rainstorm areas
Focusing on a set of summer rainstorm samples in eastern China, the synchronous precipitation profiles and cloud parameters datasets were developed, and the relationships between cloud parameters and precipitation were analyzed, especially the relationships between cloud parameters and precipitation vertical structures.
Results indicated that the ratio of ice-cloud in PCs (-87%) was much higher than in NPCs (-40%). Both optical thickness (r) and cloud water path (CWP) of PCs were obviously larger than that of NPCs, and the cloud top temperature (Tc) were lower but the differences in effective radius (Re) were very small. As r and CWP increased (Tc decreased), the probability of precipitation increased. Among these, the CWP and Tc have obvious effects in discriminating precipitation, and the effects of Re was weak.
Whether stratiform precipitation or convective precipitation, the r, CWP and Re increased with increasing of near surface rainrate, meanwhile the Tc decreased. For a given near surface rainrate, the rand CWP of convective precipitation were smaller than those of stratiform precipitation, but the Re of them were exactly the same.
As cloud parameters changing, the response characteristics of precipitation profiles to were similar for stratiform precipitation and convective precipitation. Specifically, with r, Re and CWP increasing and Tc decreasing, the rain top heights decreased and the rainrates lower than5km consistently decreased. Only Re less than15μm, the precipitation profiles have notable changes. For convective precipitation, raindrops of4km to2km levels evaporated significantly when CWP were low.
(3) Differences of cloud parameters between PCs and NPCs
The spatial distribution characteristics of PCs and NPCs were analyzed on climate scale. Combined with NCEP reanalysis datasets, the key meteorological elements of the decision to spatial distribution of PCs were studied. We Focused on comparison the differences of cloud parameters mean value and spatial distribution between PCs and NPCs.
Results showed that the cloud amounts in the high precipitation frequency area were generally higher than80%over global scale. However, the precipitation frequencies in the high high cloud amounts areas were not necessarily high, some of these areas even hardly occurred precipitation. The proportion of stratiform precipitation was highest (～79%), convective precipitation followed by stratiform precipitation (～21%), and the proportion of other-type precipitation was lowest (～0.5%). The global average daily rainfall amount was about2.6mm/d. The rainfall contributions of stratiform precipitation and convective precipitation were near, which were53%and47%, respectively. While the contribution of other-type precipitation was very small, which was less than0.1%.
Compared with NCEP reanalysis datasets, it was found that the high/low value areas of relative humidity at middle level (600hPa) were highly corresponded to the high/low value areas of total precipitation frequency. The latitudinal correlation coefficient between them was0.90, and longitudinal correlation coefficient was0.60. The divergence at high level (200hPa) was more consistent corresponding with total precipitation frequency, and the latitudinal (longitudinal) correlation coefficient between them was up to0.98(0.88). These showed that the planetary-scale water vapor transports and atmospheric circulations were the dominant factors of global total precipitation frequency distributions, in which large-scale atmospheric rising/sinking was the decisive factor.
Results indicated that most of the low cloud precipitation was shallow, isolated precipitation, and the proportion was more than60%. The precipitation intensity and spatial scales of these shallow, isolated precipitation were small. Results also showed that the difference distribution of vertical velocity between middle (600hPa) and high (200hPa) levels corresponded with the precipitation frequency distribution of middle cloud precipitation, and the latitudinal (longitudinal) correlation coefficient between them was0.61(0.51). The vertical velocity distribution of high (200hPa) levels corresponded well with the precipitation frequency distribution of high cloud precipitation, and the latitudinal (longitudinal) correlation coefficient between them was up to0.94(0.83). These results indicated that atmospheric rising/sinking at middle (high) level was the dominant factor of middle (high) cloud precipitation frequency distribution.
On a global scale, the cloud amounts of NPCs were much higher than PCs, and the ratio between them was more than5times. The high cloud amount areas of NPCs and PCs were not entirely consistent, but the low cloud amount areas of them were the same. The statistical results also showed that with the cloud top height increased, the cloud amount ratios between NPCs and PCs decreased.
Results showed that the differences of τ between PCs and NPCs were very significant, which were larger than60, and the spatial distribution of τ differences between PCs and NPCs was similar to the spatial distributions of precipitation frequency and averaged daily rainfall amount. The Re of PCs were significantly larger than NPCs in oceanic areas, but no obvious difference in land areas. The segment statistics showed that the Re of PCs were larger than NPCs in different τ segments. The no obvious Re difference in land areas was due to the different τspectrums of PCs and NPCs.
Analysis also showed that the differences in CWP between PCs and NPCs were obviously larger than the differences of both τ and Re. The Tc of PCs were significantly lower than NPCs, and the spatial distributions of cloud top pressure and cloud top height were similar to Tc. The statistics also showed that the proportion of ice cloud in PCs (～76%) was much higher than that in NPCs (～23%).
1、基于云参数信息的白天降水云识别方案
基于时空匹配的降水与云辐射信号资料,将云参数与降水信息相结合,统计不同云参数组合与降水发生概率之间的关系,在此基础上初步构建了三套基于云参数信息的白天降水云识别方案：IPCτzRe、IPCTC和PISCP,实现对降水云和非降水云像素快速而又准确的判别。
研究结果显示,无论是陆地还是海洋区域,三种方法中,PISCP法对降水云的识别效果最佳,IPCTC法次之,IPCτRe法识别性能最弱。其中PISCP、IPCTC和IPCτRe三种方法在陆地区域的ETS评估因子值分别为：0.37、0.36和0.34,而在洋面上的识别精度要好于陆地,相应ETS评估因子值分别为：0.44、0.42和0.40。
与现有同类降水云识别方案进行对比,结果显示,所提出的三种方案的降水云识别精度均优于现有识别方法,尤其是PISCP方案,其识别精度已接近被动微波仪器的探测精度。研究结果还表明,这三种方案的适用范围较广,可移植至全球其它区域。
IPCτRe、IPCTC和PISCP方案均适用于常见星载可见光/红外探测仪器,为日常降水卫星监测和预报业务提供了新的方法,同时也为深入研究云参数与降水的关系做了很好的铺垫。
2、暴雨云团云参数与降水的关系
针对夏季中国东部的暴雨云团个例,建立时空匹配的降水廓线与云参数融合资料,考察了云参数与降水概率之间的联系,并深入分析了云参数与地表降水强度及降水垂直结构的关系。
研究结果显示,在暴雨云团中,降水云的冰云比例(-87%)远高于非降水云(-40%)。降水云的光学厚度和云水路径都远大于非降水云,云顶温度也明显低于非降水云,而两者在云滴有效半径上的差异较小。随着光学厚度和云水路径的增大及云顶温度的降低,降水概率均显著增加。这其中,云水路径和云顶温度对降水云有较好的指示能力,而有效半径信息的指示能力较弱。
无论是层云降水还是对流降水,随着地表降水强度的增大,相应降水云的光学厚度、有效半径和云水路径均随之增大,而云顶温度也随之降低。对于给定的地表降水强度,对流降水的光学厚度和云水路径要低于层云降水,而两类降水的有效半径变化完全一致。
层云降水和对流降水的降水廓线对各云参数变化的响应特性相似,即随着云光学厚度、云滴有效半径和云水路径的增大以及云顶温度的降低,都对应有雨顶高度的升高以及5km以下各高度上降水率的一致增大。有所不同的是,有效半径仅在小于15μm时,降水廓线才对其变化有明显的响应。当其云水路径较低时,对流降水在4km至2km的高度层存在显著的雨滴蒸发现象。
该部分研究工作增进了对云参数与降水之间关系的认识,并为后续深入分析降水云与非降水云在云属性上的差异奠定了基础。
3、降水云与非降水云的云属性差异
在气候尺度上分析了降水云与非降水云的空间分布特征,并结合NCEP再分析资料,考察了决定降水云空间分布的关键气象要素因子,着重比较了降水云与非降水云的云参数属性在均值及空间分布上的差异。
研究结果显示,在全球(热带)范围内,降水频率高值区对应的云量一般都在80%以上,然而云量高值区相应的降水频率不一定高,有些高云量区甚至几乎不发生降水。降水类型中,层云降水出现比例最高(79%),对流降水次之(21%),其它类型降水比例最低(-0.5%)。全球(热带)地区的日均降水量约为2.6mm/d,这其中层云降水和对流降水的贡献率较为接近,分别为53%和47%,而其它类型降水的贡献极小(<0.1%)。
结合NCEP再分析资料发现,中层600hPa等压面的相对湿度高/低值中心与总降水频率的高/低值区有较好的对应,两者的纬向相关系数为0.90,经向相关系数为0.60。而高层200hPa等压面的辐散/辐合中心与总降水频率的高/低值区的对应更为一致,两者的纬向相关系数高达0.98,而经向相关系数也有0.88。可见行星尺度水汽输送和大气环流形势,是决定全球总降水频率的分布形式的主导因素,其中大尺度的气流上升／下沉运动更是决定性因素。
研究结果表明,大部分低云降水是浅薄孤立降水,比例在60%以上,局部地区的比例接近100%,这些浅薄孤立降水的降水强度和空间尺度都很小。研究结果还显示,中层600hPa与高层200hPa等压面的垂直速度差分布与中云降水频率分布有较好的对应,相应的纬向相关系数为0.61,经向相关系数为0.51。高层200hPa等压面的垂直速度分布与高云降水频率分布更为一致,其纬向相关系数高达0.94,经向相关系数为0.83。这表明,中层(高层)的气流上升/下沉运动是决定中层(高层)降水的主导因素。
在全球(热带)范围内,非降水云的云量要远远高于降水云,两者云量比值在5倍以上。非降水云与降水云的云量高值区分布并不完全一致,但非降水云的云量低值区相应的降水云云量也非常低。统计结果还显示,随着云顶高度升高(云顶气压下降),云量比值整体下降,即云顶越高,越容易产生降水。
研究结果显示,降水云与非降水云在光学厚度上的差异非常显著,两者差值在60以上。此外,两者光学厚度差值的空间分布与总降水频率及日均降水量较为相似,即降水频率/日均降水量越大,两者光学厚度差值越大。虽然在洋面上降水云的有效半径明显大于非降水云(差值为6～8μm),但在陆地上两者的差异不明显(差值为0～2μm)。分段统计结果显示,在不同光学厚度段,降水云的有效半径都要大于非降水云,导致两者有效半径在陆地上差异不明显的原因,是在于两者的光学厚度谱分布不同。
研究结果还表明,降水云与非降水云在云水路径的差异要明显大于两者在光学厚度和有效半径上的差异。降水云与非降水云的云顶温度差值空间分布呈现明显的空间不均匀性,整体而言,降水云的云顶温度(245K)要明显低于非降水云(272K)。统计还表明,降水云的冰云比例(-76%)要远高于非降水云(-23%)。此外,降水云和非降水云在云顶气压和云顶高度的全球分布形态与云顶温度较为相似。
该部分研究工作一方面在气候尺度上分析了降水云与非降水云在空间分布特征,并结合NCEP再分析资料,首次揭示了决定全球总降水、中层和高层降水空间分布的主导气象要素因子,还分析了低云降水与浅薄孤立降水之间的关系。另一方面,定量给出了降水云与非降水云在云量、云光学厚度、冰水云比例、云滴有效半径、云顶温度、云顶气压等参数的气候均值和海陆差异,并首次给出了两者上述云属性差异的空间分布特征。
Along with the applications of various advanced meteoroligcal satelliate platforms and advancements of remote sensing techniques, the macro and micro characteristics of clouds and precipitation, as well as the roles played by them in the climate system are revealed step by step. Analyses of clouds and precipitation based on the joint observations of multi-instrument and multi-channel observations has been an active research field in atmospheric remote sensing and climate change. In this study, based on the TRMM PR and VIRS measurements, which provide high-quality precipitation measurements and refined radiative information from cloud top, together with reliable cloud detection and cloud parameters retrieval techniques, the climatological characteristics of precipitating clouds (PCs) and non-precipitating clouds (NPCs) was investigated within a long time and a global scale. Three daytime precipitating clouds identification schemes from cloud parameters information have been proposed, and the relationships between cloud parameters and precipitation have been studied. Particularly, the differences of cloud parameters between PCs and NPCs were emphasized. The preliminary results on three aspects are presented as following.
(1) Daytime PCs identification scheme from cloud parameters information
Based on synchronous precipitation and radiative measurements, the precipitation information derived from PR and the cloud parameters retrieved from VIRS were compared and the relationships between cloud parameters and precipitation probability were analyzed. Three frameworks for determining the occurrence of precipitation in daytime, called Identification of Precipitating Clouds from Optical Thickness and Effective Radius (IPC rRe), Identification of Precipitating Clouds from Thermal Infrared Brightness Temperature and Cloud Parameters (IPCTC), Precipitation Identification Scheme from Cloud Parameters information (PISCP), respectively, were developed.
It was proved that PISCP performed better than IPCTC and IPC τRe both in land and oceanic areas. The performance of IPCTC was followed by PISCP, and IPC τRe a performed worse than PISCP and IPCTC. Over land area, the equitable threat score (ETS) values of PISCP, IPCTC and IPC τRe were0.37,0.36and0.34, respectively. The performances of the three schemes in oceanic areas were all better than land areas, and the ETS values were0.44,0.42and0.40.
Compared with existing similar precipitation identification schemes, the accuracies of the three proposed schemes were better, especially of PISCP, which was close to the accuracy of passive microwave instruments. The results also showed that the three proposed schemes cloud widely applied in other regions of the world.
(2) Relationships between cloud parameters and precipitation in rainstorm areas
Focusing on a set of summer rainstorm samples in eastern China, the synchronous precipitation profiles and cloud parameters datasets were developed, and the relationships between cloud parameters and precipitation were analyzed, especially the relationships between cloud parameters and precipitation vertical structures.
Results indicated that the ratio of ice-cloud in PCs (-87%) was much higher than in NPCs (-40%). Both optical thickness (r) and cloud water path (CWP) of PCs were obviously larger than that of NPCs, and the cloud top temperature (Tc) were lower but the differences in effective radius (Re) were very small. As r and CWP increased (Tc decreased), the probability of precipitation increased. Among these, the CWP and Tc have obvious effects in discriminating precipitation, and the effects of Re was weak.
Whether stratiform precipitation or convective precipitation, the r, CWP and Re increased with increasing of near surface rainrate, meanwhile the Tc decreased. For a given near surface rainrate, the rand CWP of convective precipitation were smaller than those of stratiform precipitation, but the Re of them were exactly the same.
As cloud parameters changing, the response characteristics of precipitation profiles to were similar for stratiform precipitation and convective precipitation. Specifically, with r, Re and CWP increasing and Tc decreasing, the rain top heights decreased and the rainrates lower than5km consistently decreased. Only Re less than15μm, the precipitation profiles have notable changes. For convective precipitation, raindrops of4km to2km levels evaporated significantly when CWP were low.
(3) Differences of cloud parameters between PCs and NPCs
The spatial distribution characteristics of PCs and NPCs were analyzed on climate scale. Combined with NCEP reanalysis datasets, the key meteorological elements of the decision to spatial distribution of PCs were studied. We Focused on comparison the differences of cloud parameters mean value and spatial distribution between PCs and NPCs.
Results showed that the cloud amounts in the high precipitation frequency area were generally higher than80%over global scale. However, the precipitation frequencies in the high high cloud amounts areas were not necessarily high, some of these areas even hardly occurred precipitation. The proportion of stratiform precipitation was highest (～79%), convective precipitation followed by stratiform precipitation (～21%), and the proportion of other-type precipitation was lowest (～0.5%). The global average daily rainfall amount was about2.6mm/d. The rainfall contributions of stratiform precipitation and convective precipitation were near, which were53%and47%, respectively. While the contribution of other-type precipitation was very small, which was less than0.1%.
Compared with NCEP reanalysis datasets, it was found that the high/low value areas of relative humidity at middle level (600hPa) were highly corresponded to the high/low value areas of total precipitation frequency. The latitudinal correlation coefficient between them was0.90, and longitudinal correlation coefficient was0.60. The divergence at high level (200hPa) was more consistent corresponding with total precipitation frequency, and the latitudinal (longitudinal) correlation coefficient between them was up to0.98(0.88). These showed that the planetary-scale water vapor transports and atmospheric circulations were the dominant factors of global total precipitation frequency distributions, in which large-scale atmospheric rising/sinking was the decisive factor.
Results indicated that most of the low cloud precipitation was shallow, isolated precipitation, and the proportion was more than60%. The precipitation intensity and spatial scales of these shallow, isolated precipitation were small. Results also showed that the difference distribution of vertical velocity between middle (600hPa) and high (200hPa) levels corresponded with the precipitation frequency distribution of middle cloud precipitation, and the latitudinal (longitudinal) correlation coefficient between them was0.61(0.51). The vertical velocity distribution of high (200hPa) levels corresponded well with the precipitation frequency distribution of high cloud precipitation, and the latitudinal (longitudinal) correlation coefficient between them was up to0.94(0.83). These results indicated that atmospheric rising/sinking at middle (high) level was the dominant factor of middle (high) cloud precipitation frequency distribution.
On a global scale, the cloud amounts of NPCs were much higher than PCs, and the ratio between them was more than5times. The high cloud amount areas of NPCs and PCs were not entirely consistent, but the low cloud amount areas of them were the same. The statistical results also showed that with the cloud top height increased, the cloud amount ratios between NPCs and PCs decreased.
Results showed that the differences of τ between PCs and NPCs were very significant, which were larger than60, and the spatial distribution of τ differences between PCs and NPCs was similar to the spatial distributions of precipitation frequency and averaged daily rainfall amount. The Re of PCs were significantly larger than NPCs in oceanic areas, but no obvious difference in land areas. The segment statistics showed that the Re of PCs were larger than NPCs in different τ segments. The no obvious Re difference in land areas was due to the different τspectrums of PCs and NPCs.
Analysis also showed that the differences in CWP between PCs and NPCs were obviously larger than the differences of both τ and Re. The Tc of PCs were significantly lower than NPCs, and the spatial distributions of cloud top pressure and cloud top height were similar to Tc. The statistics also showed that the proportion of ice cloud in PCs (～76%) was much higher than that in NPCs (～23%).
引文
Acarreta J R, Stammes P, Knap W H.2004. First retrieval of cloud phase from SCIAMACHY spectra around 1.6μm[J]. Atmospheric Research,72(1-4):89-105.
Ackerman S A, Strabala K I, Menzel W P, et al.1998. Discriminating Clear-sky from Clouds with MODIS[J]. Journal of Geophysical Research,103(D24):32141-32158.
Adler R F, Negri A J, Keehn P R, et al.1993. Estimation of Monthly Rainfall over Japan and Surrounding Waters from a Combination of Low-Orbit Microwave and Geosynchronous IR Data[J]. Journal of Applied Meteorology,32(2):335-356.
Alley R B, Coauthors.2007. Summary for policymakers[J]. Climate Change 2007:The Physical Science Basis:1-18.
Arkin P A.1979. The Relationship between Fractional Coverage of High Cloud and Rainfall Accumulations during GATE over the B-scale Array[J]. Monthly Weather Review, 107:1382-1387.
Arkin P A, Ardanuy P E.1989. Estimating Climatic-Scale Precipitation from Space:A Review[J]. Journal of Climate,2(11):1229-1238.
Arkin P A, Meisner B N.1987. The Relationship between Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere during 1982-84[J]. Monthly Weather Review, 115(1):51-74.
Arkin P A, Xie P.1994. The Global Precipitation Climatology Project:First Algorithm Intercomparison Project[J]. Bulletin of the American Meteorological Society,75(3):401-419.
Arking A, Childs J D.1985. Retrieval of Cloud Cover Parameters from Multispectral Satellite Images[J]. Journal of Applied Meteorology,24(4):322-334.
Awaka J, Iguchi T, Okamoto K.1998. Early results on rain type classification by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar[J]. Proc.8th URSI Commission F Open Symp., Aveiro, Portugal:143-146.
Awaka J, Iguchi T, Okamoto K I 2007. Rain Type Classification Algorithm [M] //V. LEVIZZANI, P. BAUER, F. J. TURK, Measuring Precipitation from Space:EURAINSAT and the Future. Springer Netherlands; Heidelberg:213-224.
Baum B A, Soulen P F, Strabala K I, et al.2000. Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS 2. Cloud thermodynamic phase[J]. Journal of Geophysical Research,105(D9):11781-11792.
Behrangi A, Hsu K, Imam B, et al.2009. PERSIANN-MSA:A Precipitation Estimation Method from Satellite-Based Multispectral Analysis[J]. Journal of Hydrometeorology,10(6): 1414-1429.
Bellerby T, Todd M, Kniveton D, et al.2000. Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network[J]. Journal of Applied Meteorology,39(12):2115-2128.
Berk A, Bernstein L W, Robertson D C.1983. MODTRAN:A moderate resolution model for LOWTRAN 7[J]. Rep. AFGL-TR-83-0187:261pp.
Bernstein L S, Berk A, Robertson D C, et al.1996. Addition of a correlated-k capability to MODTRAN[J]. Proc.19th Annual Conf. on Atmospheric Transmission Models:261pp.
Boukabara S A, Hoffman R N, Grassotti C, et al.2002. Physically based modeling of QuikSCAT SeaWinds passive microwave measurements for rain detection[J]. Journal of Geophysical Research,107(D24):4786.
Cess R D, Potter G L, Blanchet J P, et al.1989. Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models[J]. Science,245(4917):513-516.
Cess R D, Potter G L, Blanchet J P, et al.1990. Intercomparison and Interpretation of Climate Feedback Processes in 19 Atmospheric General Circulation Models[J]. Journal of Geophysical Research,95(D10):16601-16615.
Cess R D, Zhang M H, Minnis P, et al.1995. Absorption of Solar Radiation by Clouds: Observations Versus Models[J]. Science,267(5197):496-499.
Cess R D, Zhang M H, Zhou Y, et al.1996. Absorption of solar radiation by clouds:Interpretations of satellite, surface, and aircraft measurements[J]. Journal of Geophysical Research, 101(D18):23299-23309.
Chang F L, Li Z Q.2003. Retrieving vertical profiles of water-cloud droplet effective radius: Algorithm modification and preliminary application[J]. Journal of Geophysical Research, 108(D24):4763.
Chen R, Chang F L, Li Z Q, et al.2007. Impact of the Vertical Variation of Cloud Droplet Size on the Estimation of Cloud Liquid Water Path and Rain Detection[J]. Journal of the Atmospheric Sciences,64(11):3843-3853.
Chen S S, Houze R A, Mapes B E.1996. Multiscale Variability of Deep Convection In Realation to Large-Scale Circulation in TOGA COARE[J]. Journal of Atmospheric Sciences,53(10): 1380-1409.
Desbois M, Seze G, Szejwach G.1982. Automatic Classification of Clouds on METEOSAT Imagery:Application to High-Level Clouds[J], Journal of Applied Meteorology,21(3): 401-412.
Di Vittorio A V, Emery W J.2002. An Automated, Dynamic Threshold Cloud-masking Algorithm for Daytime AVHRR Images over Land[J]. Ieee Transactions on Geoscience and Remote Sensing,40(8):1682-1694.
Ebert E E, Manton M J.1998. Performance of Satellite Rainfall Estimation Algorithms during TOGA COARE[J]. Journal of the Atmospheric Sciences,55(9):1537-1557.
Feidas H, Giannakos A.2011. Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data[J]. Theoretical and Applied Climatology, 104(1):25-42.
Fu Y F, Liu G S.2001. The Variability of Tropical Precipitation Profiles and Its Impact on Microwave Brightness Temperatures as Inferred from TRMM Data[J]. Journal of Applied Meteorology,40(12):2130-2143.
Fu Y F, Lin Y H, Liu G S, et al.2003. Seasonal characteristics of precipitation in 1998 over East Asia as derived from TRMM PR[J]. Advances in Atmospheric Sciences,20(4):511-529.
Fu Y F, Liu G S.2007. Possible Misidentification of Rain Type by TRMM PR over Tibetan Plateau[J]. Journal of Applied Meteorology and Climatology,46(5):667-672.
Gao B C, Kaufman Y J.1995. Selection of the 1.375-μm MODIS Channel for Remote Sensing of Cirrus Clouds and Stratospheric Aerosols from Space[J]. Journal of Atmospheric Sciences, 52(23):4231-4237.
Gao B Cai, Goetz A F H, Wiscombe W J.1993. Cirrus cloud detection from Airborne Imaging Spectrometer data using the 1.38μm water vapor band[J]. Geophys. Res. Lett.,20(4): 301-304.
Hall D K, Riggs G A, Salomonson V V, et al.2002. MODIS snow-cover products[J]. Remote Sensing of Environment,83(1-2):181-194.
Han Q, Rossow W B, Lacis A A.1994, Near-Global Survey of Effective Droplet Radii in Liquid Water Clouds Using ISCCP Data[J]. Journal of Climate,7(4):465-497.
Hansen J E, Travis L D.1974. Light scattering in planetary atmospheres[J]. Space Science Reviews,16(4):527-610.
Huffman G J, Adler R F, Arkin P A, et al.1997. The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset[J]. Bulletin of the American Meteorological Society, 78(1):5-20.
Iguchi T, Kozu T, Meneghini R, et al.2000. Rain-Profiling Algorithm for the TRMM Precipitation Radar[J]. Journal of Applied Meteorology,39(12):2038-2052.
Inoue T, Aonashi K.2000. A comparison of cloud and rainfall information from instantaneous visible and infrared scanner and precipitation radar observations over a frontal zone in east Asia during June 1998[J]. Journal of Applied Meteorology,39(12):2292-2301.
Kalnay E, Kanamitsu M, Kistler R, et al.1996. The NCEP/NCAR 40-year reanalysis project[J]. Bulletin of the American Meteorological Society,77(3):437-471.
Key J R, Intrieri J M.2000. Cloud Particle Phase Determination with the AVHRR[J]. Journal of Applied Meteorology,39(10):1797-1804.
Kidd C, Kniveton D R, Todd M C, et al.2003. Satellite Rainfall Estimation Using Combined Passive Microwave and Infrared Algorithms[J]. Journal of Hydrometeorology,4(6): 1088-1104.
King M D.1981. A Method for Determining the Single Scattering Albedo of Clouds Through Observation of the Internal Scattered Radiation Field[J]. Journal of Atmospheric Sciences, 38(9):2031-2044.
King M D.1987. Determination of the scaled optical thickness of clouds from reflected solar radiation measurements[J]. Journal of the Atmospheric Sciences,44(13):1734-1751.
King M D, Kaufman Y J, Menzel W P, et al.1992. Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS)[J]. Geoscience and Remote Sensing, IEEE Transactions on,30(1):2-27.
Knap W H, Stammes P, Koelemeijer R B A.2002. Cloud Thermodynamic-Phase Determination From Near-Infrared Spectra of Reflected Sunlight[J]. Journal of the Atmospheric Sciences, 59(1):83-96.
Kneizys F X, Shettle E P, Gallery W O, et al.1983. Atmospheric transmittance/radiance: Computer code LOWTRAN 6[J]. Rep. AFGL-TR-83-0187:200pp.
Kohonen T.1982. Self-organized formation of topologically correct feature maps[J]. Biological Cybernetics,43(1):59-69.
Kokhanovsky A A, Jourdan O, Burrows J P.2006. The cloud phase discrimination from a satellite[J]. Geoscience and Remote Sensing Letters, IEEE,3(1):103-106.
Kozu T, Kawanishi T, Kuroiwa H, et al.2001. Development of precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite[J]. leee Transactions on Geoscience and Remote Sensing,39(1):102-116.
Kriebel K T, Gesell G, Ka'Stner M, et al.2003. The cloud analysis tool APOLLO:Improvements and validations[J]. International Journal of Remote Sensing,24(12):2389-2408.
Krishnamurti T N, Surendran S, Shin D W, et al.2001. Real-Time Multianalysis-Multimodel Superensemble Forecasts of Precipitation Using TRMM and SSM/I Products[J]. Monthly Weather Review,129(12):2861-2883.
Kummerow C, Barnes W, Kozu T, et al.1998. The Tropical Rainfall Measuring Mission (TRMM) Sensor Package[J]. Journal of Atmospheric and Oceanic Technology,15(3):809-817.
Kummerow C, Simpson J, Thiele O, et al.2000. The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit[J]. Journal of Applied Meteorology,39(12): 1965-1982.
Lensky I M, Rosenfeld D.2003a. A Night-Rain Delineation Algorithm for Infrared Satellite Data Based on Microphysical Considerations[J]. Journal of Applied Meteorology,42(9): 1218-1226.
Lensky I M, Rosenfeld D.2003b. Satellite-Based Insights into Precipitation Formation Processes in Continental and Maritime Convective Clouds at Nighttime[J]. Journal of Applied Meteorology,42(9):1227-1233.
Liu C, Zipser E J, Cecil D J, et al.2008. A Cloud and Precipitation Feature Database from Nine Years of TRMM Observations[J]. Journal of Applied Meteorology and Climatology,47(10): 2712-2728.
Liu G S, Curry J A.1998. An Investigation of the Relationship between Emission and Scattering Signals in SSM/I Data[J]. Journal of the Atmospheric Sciences,55(9):1628-1643.
Liu G S, Fu Y F.2001. The Characteristics of Tropical Precipitation Profiles As Inferred From Satellite Radar Measurements[J]. Journal of the Meteorological Society of Japan,79(1): 131-143.
Liu Q, Fu Y F, Yu R C, et al.2008. A new satellite-based census of precipitating and nonprecipitating clouds over the tropics and subtropics[J]. Geophysical Research Letters, 35(7):L07816.
Liu Q, Fu Y F.2010. Comparison of radiative signals between precipitating and non-precipitating clouds in frontal and typhoon domains over East Asia[J]. Atmospheric Research,96(2-3): 436-446.
Li R, Fu Y F.2005. Tropical precipitation estimated by GPCP and TRMM PR observations[J]. Advances in Atmospheric Sciences,22(6):852-864.
Machado L a T, Rossow W B, Guedes R L, et al.1998. Life Cycle Variations of Mesoscale Convective Systems over the Americas[J]. Monthly Weather Review,126(6):1630-1654.
Mapes B E, Houze R A.1993. Cloud Clusters and Superclusters over the Oceanic Warm Pool[J]. Monthly Weather Review,121(5):1398-1415.
Nakajima T, King M D.1990. Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. I-Theory[J]. Journal of the Atmospheric Sciences,47(15):1878-1893.
Nakajima T, King M D, Spinhirne J D, et al.1991. Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part Ⅱ: Marine Stratocumulus Observations[J]. Journal of the Atmospheric Sciences,48(5):728-751.
Nakajima T Y, Nakajima T.1995. Wide-area determination of cloud microphysical properties from NOA AVHRR measurements for FIRE and ASTEX regions[J]. Journal of the Atmospheric Sciences,52(23):4043-4059.
Nauss T, Kokhanovsky A A.2006. Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data[J]. Atmospheric Chemistry and Physics,6: 5031-5036.
Nauss T, Kokhanovsky A A.2011. Retrieval of warm cloud optical properties using simple approximations[J]. Remote Sensing of Environment,115(6):1317-1325.
Olson W S, Kummerow C D, Heymsfield G M, et al.1996. A Method for Combined Passive-Active Microwave Retrievals of Cloud and Precipitation Profiles[J]. Journal of Applied Meteorology and Climatology,35(10):1763-1789.
Ou S C, Liou K N, Gooch W M, et al.1993. Remote sensing of cirrus cloud parameters using advanced very-high-resolution radiometer 3.7-and 10.9-μm channels[J]. Appl. Opt.,32(12): 2171-2180.
Pavolonis M J, Heidinger A K, Uttal T.2005. Daytime Global Cloud Typing from AVHRR and VIIRS:Algorithm Description, Validation, and Comparisons[J]. Journal of Applied Meteorology,44(6):804-826.
Peak J E, Tag P M.1994. Segmentation of Satellite Imagery using Hierarchical Thresholding and Neural Networks[J]. Journal of Applied Meteorology,33(5):605-616.
Pierluissi J H, Peng G S.1985. New molecular transmission band models for LOWTRAN[J]. Opt. Eng.,24(3):541-547.
Pilewskie P, Twomey S.1987. Discrimination of ice from water in clouds by optical remote sensing[J]. Atmospheric Research,21(2):113-122.
Platnick S, King M D, Ackerman S A, et al.2003a. The MODIS Cloud Products:Algorithms and Examples From Terra[J]. Ieee Transactions on Geoscience and Remote Sensing,44(2): 459-473.
Platnick S, King M D, Ackerman S A, et al.2003b. The MODIS cloud products:algorithms and examples from Terra[J]. Geoscience and Remote Sensing, IEEE Transactions on,41(2): 459-473.
Platnick S, Valero F P J.1995. A Validation of a Satellite Cloud Retrieval during ASTEX[J]. Journal of Atmospheric Sciences,52(16):2985-3001.
Ramanathan V.1987. The Role of Earth Radiation Budget Studies in Climate and General Circulation Research[J]. Journal of Geophysical Research,92(D4):4075-4095.
Ramanathan V, Cess R D, Harrison E F, et al.1989. Cloud-Radiative Forcing and Climate:Results from the Earth Radiation Budget Experiment[J]. Science,243(4887):57-63.
Ramanathan V, Crutzen P J, Kiehl J T, et al.2001a. Aerosols, Climate, and the Hydrological Cycle[J]. Science,294(5549):2119-2124.
Ramanathan V, Crutzen P J, Kiehl J T, et al.2001b. Atmosphere-Aerosols, climate, and the hydrological cycle[J]. Science,294(5549):2119-2124.
Reynolds R W, Rayner N A, Smith T M, et al.2002. An Improved In Situ and Satellite SST Analysis for Climate[J]. Journal of Climate,15:1609-1625.
Reynolds R W, Smith T M.1994. Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation[J]. Journal of Climate,7(6):929-948.
Ricchiazzi P, Yang S, Gautier C, et al.1998. SBDART:A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's Atmosphere[J]. Bulletin of the American Meteorological Society,79(10):2101-2114.
Rosenfeld D.1999. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall[J]. Geophysical Research Letters,26(20):3105-3108.
Rosenfeld D.2000. Suppression of Rain and Snow by Urban and Industrial Air Pollution[J]. Science,287(5459):1793-1796.
Rosenfeld D, Cattani E, Melani S, et al.2004. Considerations on Daylight Operation of 1.6-VERSUS 3.7-μm Channel on NOAA and Metop Satellites[J]. Bulletin of the American Meteorological Society,85(6):873-881.
Rosenfeld D, Gutman G.1994. Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data[J]. Atmospheric Research,34(1-4):259-283.
Rosenfeld D, Lensky I M.1998. Satellite-based insights into precipitation formation processes in continental and maritime convective clouds[J]. Bulletin of the American Meteorological Society,79(11):2457-2476.
Rosenfeld D, Lohmann U, Raga G B, et al.2008. Flood or Drought:How Do Aerosols Affect Precipitation?[J]. Science,321(5894):1309-1313.
Rossow W B, Schiffer R A.1991. ISCCP Cloud Data Products[J]. Bulletin of the American Meteorological Society,72(1):2-20.
Rossow W B, Garder L C.1993a. Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for 1SCCP[J]. J Climate,6(12):2341-2369.
Rossow W B, Garder L C.1993b. Validation of ISCCP cloud detections[J]. J Climate,6(12): 2370-2393.
Rossow W B, Schiffer R A.1999. Advances in Understanding Clouds from ISCCPfJ]. Bulletin of the American Meteorological Society,80(11):2261-2287.
Saunders R W, Kriebel K T.1988. An Improved Method of Detecting Clear Sky and Cloudy Radiances from AVHRR Data[J]. International Journal of Remote Sensing,9(1):123-150.
Schaefer J T.1990. The critical success index as an indicator of warning skill[J]. Weather forecast, 5:570-575.
Schiffer R A, Rossow W B.1983. The International Satellite Cloud Climatology Project (ISCCP)-The first project of the World Climate Research Programme [J]. Bulletin of the American Meteorological Society,64(7):779-784.
Schiffer R A, Rossow W B.1985. ISCCP global radiance data set:a new resource for climate research[J]. Bulletin of the American Meteorological Society,66(12):1498-1505.
Schumacher C, Houze R A.2003. The TRMM Precipitation Radar's View of Shallow, Isolated Rain[J]. Journal of Applied Meteorology,42(10):1519-1524.
Senior C A, Mitchell J F B.1993. Carbon Dioxide and Climate:The Impact of Cloud Parameterization [J]. Journal of Climate,6(3):393-418.
Senior C A.1999. Comparison of Mechanisms of Cloud-Climate Feedbacks in GCMs[J]. Journal of Climate,12(5):1480-1489.
Simpson J, Adler R F, North G R.1988. A proposed Tropical Rainfall Measuring Mission (TRMM) satellite[J]. Bulletin of the American Meteorological Society,69(3):278-295.
Simpson J, Kummerow C, Tao W K, et al.1996. On the Tropical Rainfall Measuring Mission (TRMM)[J]. Meteorology and Atmospheric Physics,60(1):19-36.
Spencer R W.1986. A Satellite Passive 37-GHz Scattering-based Method for Measuring Oceanic Rain Rates[J]. Journal of Climate and Applied Meteorology,25(6):754-766.
Spencer R W, Goodman H M, Hood R E.1989. Precipitation Retrieval over Land and Ocean with the SSM/I:Identification and Characteristics of the Scattering Signal[J]. Journal of Atmospheric and Oceanic Technology,6(2):254-273.
Stamnes K, Tsay S C, Wiscombe W, et al.1988. Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media[J]. Appl. Opt.,27(12):2502-2509.
Stephens G L, Kummerow C D.2007. The Remote Sensing of Clouds and Precipitation from Space:A Review[J]. Journal of the Atmospheric Sciences,64(11):3742-3765.
Stowe L L, Davis P, Mcclain E P.1995. Evaluating the CLAVR (clouds from AVHRR) phase Ⅰ-cloud cover experimental product[J]. Advances in Space Research,16(10):21-24.
Stowe L L, Davis P A, Mcclain E P.1999. Scientific Basis and Initial Evaluation of the CLAVR-1 Global Clear/Cloud Classification Algorithm for the Advanced Very High Resolution Radiometer[J]. Journal of Atmospheric and Oceanic Technology,16(6):656-681.
Strabala K I, Ackerman S A, Menzel W P.1994. Cloud properties inferred from 8-12μm data[J]. Journal of Applied Meteorology,33(2):212-229.
Thies B, Nauss T, Bendix J.2008a. Discriminating raining from non-raining cloud areas at mid-latitudes using meteosat second generation SEVIRI night-time data[J]. Meteorological Applications,15(2):219-230.
Thies B, Nauss T, Bendix J.2008b. Discriminating raining from non-raining clouds at mid-latitudes using meteosat second generation daytime data[J]. Atmospheric Chemistry and Physics,8(9):2341-2349.
Tian B, Soden B J, Wu X.2004. Diurnal cycle of convection, clouds, and water vapor in the tropical upper troposphere:Satellites versus a general circulation model[J]. Journal of Geophysical Research,109(D10):D10101.
Vemury S, Stowe L L, Anne V R.2001. AVHRR Pixel Level Clear-Sky Classification Using Dynamic Thresholds (CLAVR-3)[J]. Journal of Atmospheric and Oceanic Technology,18(2): 169-186.
Wetherald R T, Manabe S.1988. Cloud Feedback Processes in a General Circulation Model [J]. Journal of Atmospheric Sciences,45(8):1397-1416.
Williams E, Rosenfeld D, Madden N, et al.2002. Contrasting convective regimes over the Amazon:Implications for cloud electrification[J]. Journal of Geophysical Research, 107(D20):8082.
Woodley W L, Rosenfeld D, Strautins A.2000. Identification of a seeding in Texas using multi-spectral satellite imagery[J]. Journal of Weather Modification,32:37-52.
Xie P, Arkin P A.1996. Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions[J]. Journal of Climate,9(4):840-858.
Zipser E J, Lutz K R.1994. The vertical profile of radar reflectivity of convective cells:A strong indicator of storm intensity and lightning probability?[J]. Monthly Weather Review,122(8): 1751-1759.
Zuidema P.2003. Convective Clouds over the Bay of Bengal[J]. Monthly Weather Review,131(5): 780-798.
陈英英,唐仁茂,周毓荃等.2009FY-2C/D卫星微物理特性参数产品在地面降水分析中的应用[J].气象,35(2)：15-18.
陈英英,周毓荃,毛节泰等.2007.利用FY-2C静止卫星资料反演云粒子有效半径的试验研究[J].气象,33(4)：29-34.
戴进,余兴,刘贵华等.2010.一次暴雨过程中云微物理特征的卫星反演分析[J].气象学报,68(3)：387-397.
丁伟钰,林爱兰.2003.GMS5多通道数据与TRMM资料估测华南地区热带气旋降水[J].热带气象学报,19(增刊)：74-80.
方宗义,许健民,赵凤生.2004.中国气象卫星和卫星气象研究的回顾和发展[J].气象学报,62(5)：550-560.
傅云飞,冯静夷,朱红芳等.2005.西太平洋副热带高压下热对流降水结构特征的个例分析[J].气象学报,63(5)：750-761.
傅云飞,宇如聪,徐幼平等.2003TRMM测雨雷达和微波成像仪对两个中尺度特大暴雨降水结构的观测分析研究[J].气象学报,61(4)：421-431.
傅云飞,刘栋,王雨等.2007.热带测雨卫星综合探测结果之“云娜”台风降水云与非降水云特征[J].气象学报,65(3)：316-328.
傅云飞,刘奇,自勇等.2008a.基于TRMM卫星探测的夏季青藏高原降水和潜热分析[J].高原山地气象研究,28(1)：8-18.
傅云飞,张爱民,刘勇等.2008b.基于星载测雨雷达探测的亚洲对流和层云降水季尺度特征分析[J].气象学报,66(5)：730-746.
傅云飞,冯沙,刘鹏等.2010.热带测雨卫星测雨雷达探测的亚洲夏季积雨云云砧[J].气象学报,68(2)：195-206.
傅云飞,刘鹏,刘奇等.2011.夏季热带及副热带降水云可见光/红外信号气候分布特征[J].大气与环境光学学报,6(2)：407-426.
李锐,傅云飞.2005.利用热带测雨卫星的测雨雷达资料对1997/1998年El Nino后期热带太平洋降水结构的研究[J].大气科学,29(2)：225-235.
刘诚,Gerry B, Hiroaki K等.2007.基于NOAA16-AVHRR数据反演中纬度陆地上空云类型及云顶高度信息[J].大气与环境光学学报,2(4)：301-305.
刘健,许健民,方宗义.1999.利用NOAA卫星的AVHRR资料试分析云和雾顶部粒子的尺度特征[J].应用气象学报,10(1)：28-33.
刘健,董超华.2002.卫星资料在云顶粒子尺度特征分析中的应用[J].红外与毫米波学报,21(2)：124-128.
刘健,董超华,张文建.2003a.利用FY-1C资料反演水云的光学厚度和粒子有效半径[J].红外与毫米波学报,22(6)：436-440.
刘健,董超华,朱元竞等.2003b.FY-1C资料在云顶粒子热力学相态分析中的应用研究[J].大气科学,27(5)：901-908.
刘健,李云.2011.风云二号静止气象卫星的云相态识别算法[J].红外与毫米波学报,30(4)：322-327.
刘鹏,傅云飞.2010.利用星载测雨雷达探测结果对夏季中国南方对流和层云降水气候特征的分析[J].大气科学,34(4)：802-814.
刘奇.2007.基于ISCCP及TRMM观测的热带降水云与非降水云差异的研究[J].博士毕业论文.
刘奇,傅云飞,冯沙.2010.基于ISCCP观测的云量全球分布及其在NCEP再分析场中的指示[J].气象学报,68(5)：689-704.
刘希,许健民,杜秉玉.2005.用双通道动态阈值对GMS-5图像进行自动云检测[J].应用气象学报,16(4)：434-444.
刘显通,刘奇,傅云飞.2011.基于光学厚度和有效半径的白天降水云识别方案[J].大气科学,35(5)：903-911.
闵爱荣,游然,卢乃锰等.2008TRMM卫星微波成像仪资料的陆面降水反演[J].热带气象学报,24(3)：265-272.
师春香,吴荣璋,项续康.2001.多阈值和神经网络卫星云图云系自动分割试验[J].应用气象学报,12(1)：70-78.
王雨,傅云飞,刘国胜.2006.热带测雨卫星TMI探测结果对非降水云液态水路径的反演方案研究[J].气象学报,64(4)：443-452.
徐海明,何金海,谢尚平.2007.卫星资料揭示的中尺度地形对南海夏季气候的影响[J].大气科学,31(5)：1021-1031.
叶晶,李万彪,严卫.2009.利用MODIS数据反演多层云光学厚度和有效半径[J].气象学报,67(4)：613-622.
赵凤生,丁强,孙同明等.2002.利用NOAA-AVHRR观测数据反演云辐射特性的一种迭代方法[J].气象学报,60(5)：594-601.
周青,赵凤生,高文华.2010.利用FY-2C卫星数据反演云辐射特性[J].大气科学,34(4)：827-842.
周毓荃,陈英英,李娟等.2008.用FY-2C/D卫星等综合观测资料反演云物理特性产品及检验[J].气象,34(12)：27-35.
周毓荃,蔡淼,欧建军等.2011.云特征参数与降水相关性的研究[J].大气科学学报,34(6)：641-652.
周著华,白洁,刘健文等.2005MODIS多光谱云相态识别技术的应用研究[J].应用气象学报,16(5)：678-683.
朱素行,徐海明,徐蜜蜜.2010.亚洲夏季风区中尺度地形降水结构及分布特征[J].大气科学,34(1)：71-82.
Ackerman S A, Strabala K I, Menzel W P, et al.1998. Discriminating Clear-sky from Clouds with MODIS[J]. Journal of Geophysical Research,103(D24):32141-32158.
Adler R F, Negri A J, Keehn P R, et al.1993. Estimation of Monthly Rainfall over Japan and Surrounding Waters from a Combination of Low-Orbit Microwave and Geosynchronous IR Data[J]. Journal of Applied Meteorology,32(2):335-356.
Alley R B, Coauthors.2007. Summary for policymakers[J]. Climate Change 2007:The Physical Science Basis:1-18.
Arkin P A.1979. The Relationship between Fractional Coverage of High Cloud and Rainfall Accumulations during GATE over the B-scale Array[J]. Monthly Weather Review, 107:1382-1387.
Arkin P A, Ardanuy P E.1989. Estimating Climatic-Scale Precipitation from Space:A Review[J]. Journal of Climate,2(11):1229-1238.
Arkin P A, Meisner B N.1987. The Relationship between Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere during 1982-84[J]. Monthly Weather Review, 115(1):51-74.
Arkin P A, Xie P.1994. The Global Precipitation Climatology Project:First Algorithm Intercomparison Project[J]. Bulletin of the American Meteorological Society,75(3):401-419.
Arking A, Childs J D.1985. Retrieval of Cloud Cover Parameters from Multispectral Satellite Images[J]. Journal of Applied Meteorology,24(4):322-334.
Awaka J, Iguchi T, Okamoto K.1998. Early results on rain type classification by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar[J]. Proc.8th URSI Commission F Open Symp., Aveiro, Portugal:143-146.
Awaka J, Iguchi T, Okamoto K I 2007. Rain Type Classification Algorithm [M] //V. LEVIZZANI, P. BAUER, F. J. TURK, Measuring Precipitation from Space:EURAINSAT and the Future. Springer Netherlands; Heidelberg:213-224.
Baum B A, Soulen P F, Strabala K I, et al.2000. Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS 2. Cloud thermodynamic phase[J]. Journal of Geophysical Research,105(D9):11781-11792.
Behrangi A, Hsu K, Imam B, et al.2009. PERSIANN-MSA:A Precipitation Estimation Method from Satellite-Based Multispectral Analysis[J]. Journal of Hydrometeorology,10(6): 1414-1429.
Bellerby T, Todd M, Kniveton D, et al.2000. Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network[J]. Journal of Applied Meteorology,39(12):2115-2128.
Berk A, Bernstein L W, Robertson D C.1983. MODTRAN:A moderate resolution model for LOWTRAN 7[J]. Rep. AFGL-TR-83-0187:261pp.
Bernstein L S, Berk A, Robertson D C, et al.1996. Addition of a correlated-k capability to MODTRAN[J]. Proc.19th Annual Conf. on Atmospheric Transmission Models:261pp.
Boukabara S A, Hoffman R N, Grassotti C, et al.2002. Physically based modeling of QuikSCAT SeaWinds passive microwave measurements for rain detection[J]. Journal of Geophysical Research,107(D24):4786.
Cess R D, Potter G L, Blanchet J P, et al.1989. Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models[J]. Science,245(4917):513-516.
Cess R D, Potter G L, Blanchet J P, et al.1990. Intercomparison and Interpretation of Climate Feedback Processes in 19 Atmospheric General Circulation Models[J]. Journal of Geophysical Research,95(D10):16601-16615.
Cess R D, Zhang M H, Minnis P, et al.1995. Absorption of Solar Radiation by Clouds: Observations Versus Models[J]. Science,267(5197):496-499.
Cess R D, Zhang M H, Zhou Y, et al.1996. Absorption of solar radiation by clouds:Interpretations of satellite, surface, and aircraft measurements[J]. Journal of Geophysical Research, 101(D18):23299-23309.
Chang F L, Li Z Q.2003. Retrieving vertical profiles of water-cloud droplet effective radius: Algorithm modification and preliminary application[J]. Journal of Geophysical Research, 108(D24):4763.
Chen R, Chang F L, Li Z Q, et al.2007. Impact of the Vertical Variation of Cloud Droplet Size on the Estimation of Cloud Liquid Water Path and Rain Detection[J]. Journal of the Atmospheric Sciences,64(11):3843-3853.
Chen S S, Houze R A, Mapes B E.1996. Multiscale Variability of Deep Convection In Realation to Large-Scale Circulation in TOGA COARE[J]. Journal of Atmospheric Sciences,53(10): 1380-1409.
Desbois M, Seze G, Szejwach G.1982. Automatic Classification of Clouds on METEOSAT Imagery:Application to High-Level Clouds[J], Journal of Applied Meteorology,21(3): 401-412.
Di Vittorio A V, Emery W J.2002. An Automated, Dynamic Threshold Cloud-masking Algorithm for Daytime AVHRR Images over Land[J]. Ieee Transactions on Geoscience and Remote Sensing,40(8):1682-1694.
Ebert E E, Manton M J.1998. Performance of Satellite Rainfall Estimation Algorithms during TOGA COARE[J]. Journal of the Atmospheric Sciences,55(9):1537-1557.
Feidas H, Giannakos A.2011. Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data[J]. Theoretical and Applied Climatology, 104(1):25-42.
Fu Y F, Liu G S.2001. The Variability of Tropical Precipitation Profiles and Its Impact on Microwave Brightness Temperatures as Inferred from TRMM Data[J]. Journal of Applied Meteorology,40(12):2130-2143.
Fu Y F, Lin Y H, Liu G S, et al.2003. Seasonal characteristics of precipitation in 1998 over East Asia as derived from TRMM PR[J]. Advances in Atmospheric Sciences,20(4):511-529.
Fu Y F, Liu G S.2007. Possible Misidentification of Rain Type by TRMM PR over Tibetan Plateau[J]. Journal of Applied Meteorology and Climatology,46(5):667-672.
Gao B C, Kaufman Y J.1995. Selection of the 1.375-μm MODIS Channel for Remote Sensing of Cirrus Clouds and Stratospheric Aerosols from Space[J]. Journal of Atmospheric Sciences, 52(23):4231-4237.
Gao B Cai, Goetz A F H, Wiscombe W J.1993. Cirrus cloud detection from Airborne Imaging Spectrometer data using the 1.38μm water vapor band[J]. Geophys. Res. Lett.,20(4): 301-304.
Hall D K, Riggs G A, Salomonson V V, et al.2002. MODIS snow-cover products[J]. Remote Sensing of Environment,83(1-2):181-194.
Han Q, Rossow W B, Lacis A A.1994, Near-Global Survey of Effective Droplet Radii in Liquid Water Clouds Using ISCCP Data[J]. Journal of Climate,7(4):465-497.
Hansen J E, Travis L D.1974. Light scattering in planetary atmospheres[J]. Space Science Reviews,16(4):527-610.
Huffman G J, Adler R F, Arkin P A, et al.1997. The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset[J]. Bulletin of the American Meteorological Society, 78(1):5-20.
Iguchi T, Kozu T, Meneghini R, et al.2000. Rain-Profiling Algorithm for the TRMM Precipitation Radar[J]. Journal of Applied Meteorology,39(12):2038-2052.
Inoue T, Aonashi K.2000. A comparison of cloud and rainfall information from instantaneous visible and infrared scanner and precipitation radar observations over a frontal zone in east Asia during June 1998[J]. Journal of Applied Meteorology,39(12):2292-2301.
Kalnay E, Kanamitsu M, Kistler R, et al.1996. The NCEP/NCAR 40-year reanalysis project[J]. Bulletin of the American Meteorological Society,77(3):437-471.
Key J R, Intrieri J M.2000. Cloud Particle Phase Determination with the AVHRR[J]. Journal of Applied Meteorology,39(10):1797-1804.
Kidd C, Kniveton D R, Todd M C, et al.2003. Satellite Rainfall Estimation Using Combined Passive Microwave and Infrared Algorithms[J]. Journal of Hydrometeorology,4(6): 1088-1104.
King M D.1981. A Method for Determining the Single Scattering Albedo of Clouds Through Observation of the Internal Scattered Radiation Field[J]. Journal of Atmospheric Sciences, 38(9):2031-2044.
King M D.1987. Determination of the scaled optical thickness of clouds from reflected solar radiation measurements[J]. Journal of the Atmospheric Sciences,44(13):1734-1751.
King M D, Kaufman Y J, Menzel W P, et al.1992. Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS)[J]. Geoscience and Remote Sensing, IEEE Transactions on,30(1):2-27.
Knap W H, Stammes P, Koelemeijer R B A.2002. Cloud Thermodynamic-Phase Determination From Near-Infrared Spectra of Reflected Sunlight[J]. Journal of the Atmospheric Sciences, 59(1):83-96.
Kneizys F X, Shettle E P, Gallery W O, et al.1983. Atmospheric transmittance/radiance: Computer code LOWTRAN 6[J]. Rep. AFGL-TR-83-0187:200pp.
Kohonen T.1982. Self-organized formation of topologically correct feature maps[J]. Biological Cybernetics,43(1):59-69.
Kokhanovsky A A, Jourdan O, Burrows J P.2006. The cloud phase discrimination from a satellite[J]. Geoscience and Remote Sensing Letters, IEEE,3(1):103-106.
Kozu T, Kawanishi T, Kuroiwa H, et al.2001. Development of precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite[J]. leee Transactions on Geoscience and Remote Sensing,39(1):102-116.
Kriebel K T, Gesell G, Ka'Stner M, et al.2003. The cloud analysis tool APOLLO:Improvements and validations[J]. International Journal of Remote Sensing,24(12):2389-2408.
Krishnamurti T N, Surendran S, Shin D W, et al.2001. Real-Time Multianalysis-Multimodel Superensemble Forecasts of Precipitation Using TRMM and SSM/I Products[J]. Monthly Weather Review,129(12):2861-2883.
Kummerow C, Barnes W, Kozu T, et al.1998. The Tropical Rainfall Measuring Mission (TRMM) Sensor Package[J]. Journal of Atmospheric and Oceanic Technology,15(3):809-817.
Kummerow C, Simpson J, Thiele O, et al.2000. The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit[J]. Journal of Applied Meteorology,39(12): 1965-1982.
Lensky I M, Rosenfeld D.2003a. A Night-Rain Delineation Algorithm for Infrared Satellite Data Based on Microphysical Considerations[J]. Journal of Applied Meteorology,42(9): 1218-1226.
Lensky I M, Rosenfeld D.2003b. Satellite-Based Insights into Precipitation Formation Processes in Continental and Maritime Convective Clouds at Nighttime[J]. Journal of Applied Meteorology,42(9):1227-1233.
Liu C, Zipser E J, Cecil D J, et al.2008. A Cloud and Precipitation Feature Database from Nine Years of TRMM Observations[J]. Journal of Applied Meteorology and Climatology,47(10): 2712-2728.
Liu G S, Curry J A.1998. An Investigation of the Relationship between Emission and Scattering Signals in SSM/I Data[J]. Journal of the Atmospheric Sciences,55(9):1628-1643.
Liu G S, Fu Y F.2001. The Characteristics of Tropical Precipitation Profiles As Inferred From Satellite Radar Measurements[J]. Journal of the Meteorological Society of Japan,79(1): 131-143.
Liu Q, Fu Y F, Yu R C, et al.2008. A new satellite-based census of precipitating and nonprecipitating clouds over the tropics and subtropics[J]. Geophysical Research Letters, 35(7):L07816.
Liu Q, Fu Y F.2010. Comparison of radiative signals between precipitating and non-precipitating clouds in frontal and typhoon domains over East Asia[J]. Atmospheric Research,96(2-3): 436-446.
Li R, Fu Y F.2005. Tropical precipitation estimated by GPCP and TRMM PR observations[J]. Advances in Atmospheric Sciences,22(6):852-864.
Machado L a T, Rossow W B, Guedes R L, et al.1998. Life Cycle Variations of Mesoscale Convective Systems over the Americas[J]. Monthly Weather Review,126(6):1630-1654.
Mapes B E, Houze R A.1993. Cloud Clusters and Superclusters over the Oceanic Warm Pool[J]. Monthly Weather Review,121(5):1398-1415.
Nakajima T, King M D.1990. Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. I-Theory[J]. Journal of the Atmospheric Sciences,47(15):1878-1893.
Nakajima T, King M D, Spinhirne J D, et al.1991. Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part Ⅱ: Marine Stratocumulus Observations[J]. Journal of the Atmospheric Sciences,48(5):728-751.
Nakajima T Y, Nakajima T.1995. Wide-area determination of cloud microphysical properties from NOA AVHRR measurements for FIRE and ASTEX regions[J]. Journal of the Atmospheric Sciences,52(23):4043-4059.
Nauss T, Kokhanovsky A A.2006. Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data[J]. Atmospheric Chemistry and Physics,6: 5031-5036.
Nauss T, Kokhanovsky A A.2011. Retrieval of warm cloud optical properties using simple approximations[J]. Remote Sensing of Environment,115(6):1317-1325.
Olson W S, Kummerow C D, Heymsfield G M, et al.1996. A Method for Combined Passive-Active Microwave Retrievals of Cloud and Precipitation Profiles[J]. Journal of Applied Meteorology and Climatology,35(10):1763-1789.
Ou S C, Liou K N, Gooch W M, et al.1993. Remote sensing of cirrus cloud parameters using advanced very-high-resolution radiometer 3.7-and 10.9-μm channels[J]. Appl. Opt.,32(12): 2171-2180.
Pavolonis M J, Heidinger A K, Uttal T.2005. Daytime Global Cloud Typing from AVHRR and VIIRS:Algorithm Description, Validation, and Comparisons[J]. Journal of Applied Meteorology,44(6):804-826.
Peak J E, Tag P M.1994. Segmentation of Satellite Imagery using Hierarchical Thresholding and Neural Networks[J]. Journal of Applied Meteorology,33(5):605-616.
Pierluissi J H, Peng G S.1985. New molecular transmission band models for LOWTRAN[J]. Opt. Eng.,24(3):541-547.
Pilewskie P, Twomey S.1987. Discrimination of ice from water in clouds by optical remote sensing[J]. Atmospheric Research,21(2):113-122.
Platnick S, King M D, Ackerman S A, et al.2003a. The MODIS Cloud Products:Algorithms and Examples From Terra[J]. Ieee Transactions on Geoscience and Remote Sensing,44(2): 459-473.
Platnick S, King M D, Ackerman S A, et al.2003b. The MODIS cloud products:algorithms and examples from Terra[J]. Geoscience and Remote Sensing, IEEE Transactions on,41(2): 459-473.
Platnick S, Valero F P J.1995. A Validation of a Satellite Cloud Retrieval during ASTEX[J]. Journal of Atmospheric Sciences,52(16):2985-3001.
Ramanathan V.1987. The Role of Earth Radiation Budget Studies in Climate and General Circulation Research[J]. Journal of Geophysical Research,92(D4):4075-4095.
Ramanathan V, Cess R D, Harrison E F, et al.1989. Cloud-Radiative Forcing and Climate:Results from the Earth Radiation Budget Experiment[J]. Science,243(4887):57-63.
Ramanathan V, Crutzen P J, Kiehl J T, et al.2001a. Aerosols, Climate, and the Hydrological Cycle[J]. Science,294(5549):2119-2124.
Ramanathan V, Crutzen P J, Kiehl J T, et al.2001b. Atmosphere-Aerosols, climate, and the hydrological cycle[J]. Science,294(5549):2119-2124.
Reynolds R W, Rayner N A, Smith T M, et al.2002. An Improved In Situ and Satellite SST Analysis for Climate[J]. Journal of Climate,15:1609-1625.
Reynolds R W, Smith T M.1994. Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation[J]. Journal of Climate,7(6):929-948.
Ricchiazzi P, Yang S, Gautier C, et al.1998. SBDART:A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's Atmosphere[J]. Bulletin of the American Meteorological Society,79(10):2101-2114.
Rosenfeld D.1999. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall[J]. Geophysical Research Letters,26(20):3105-3108.
Rosenfeld D.2000. Suppression of Rain and Snow by Urban and Industrial Air Pollution[J]. Science,287(5459):1793-1796.
Rosenfeld D, Cattani E, Melani S, et al.2004. Considerations on Daylight Operation of 1.6-VERSUS 3.7-μm Channel on NOAA and Metop Satellites[J]. Bulletin of the American Meteorological Society,85(6):873-881.
Rosenfeld D, Gutman G.1994. Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data[J]. Atmospheric Research,34(1-4):259-283.
Rosenfeld D, Lensky I M.1998. Satellite-based insights into precipitation formation processes in continental and maritime convective clouds[J]. Bulletin of the American Meteorological Society,79(11):2457-2476.
Rosenfeld D, Lohmann U, Raga G B, et al.2008. Flood or Drought:How Do Aerosols Affect Precipitation?[J]. Science,321(5894):1309-1313.
Rossow W B, Schiffer R A.1991. ISCCP Cloud Data Products[J]. Bulletin of the American Meteorological Society,72(1):2-20.
Rossow W B, Garder L C.1993a. Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for 1SCCP[J]. J Climate,6(12):2341-2369.
Rossow W B, Garder L C.1993b. Validation of ISCCP cloud detections[J]. J Climate,6(12): 2370-2393.
Rossow W B, Schiffer R A.1999. Advances in Understanding Clouds from ISCCPfJ]. Bulletin of the American Meteorological Society,80(11):2261-2287.
Saunders R W, Kriebel K T.1988. An Improved Method of Detecting Clear Sky and Cloudy Radiances from AVHRR Data[J]. International Journal of Remote Sensing,9(1):123-150.
Schaefer J T.1990. The critical success index as an indicator of warning skill[J]. Weather forecast, 5:570-575.
Schiffer R A, Rossow W B.1983. The International Satellite Cloud Climatology Project (ISCCP)-The first project of the World Climate Research Programme [J]. Bulletin of the American Meteorological Society,64(7):779-784.
Schiffer R A, Rossow W B.1985. ISCCP global radiance data set:a new resource for climate research[J]. Bulletin of the American Meteorological Society,66(12):1498-1505.
Schumacher C, Houze R A.2003. The TRMM Precipitation Radar's View of Shallow, Isolated Rain[J]. Journal of Applied Meteorology,42(10):1519-1524.
Senior C A, Mitchell J F B.1993. Carbon Dioxide and Climate:The Impact of Cloud Parameterization [J]. Journal of Climate,6(3):393-418.
Senior C A.1999. Comparison of Mechanisms of Cloud-Climate Feedbacks in GCMs[J]. Journal of Climate,12(5):1480-1489.
Simpson J, Adler R F, North G R.1988. A proposed Tropical Rainfall Measuring Mission (TRMM) satellite[J]. Bulletin of the American Meteorological Society,69(3):278-295.
Simpson J, Kummerow C, Tao W K, et al.1996. On the Tropical Rainfall Measuring Mission (TRMM)[J]. Meteorology and Atmospheric Physics,60(1):19-36.
Spencer R W.1986. A Satellite Passive 37-GHz Scattering-based Method for Measuring Oceanic Rain Rates[J]. Journal of Climate and Applied Meteorology,25(6):754-766.
Spencer R W, Goodman H M, Hood R E.1989. Precipitation Retrieval over Land and Ocean with the SSM/I:Identification and Characteristics of the Scattering Signal[J]. Journal of Atmospheric and Oceanic Technology,6(2):254-273.
Stamnes K, Tsay S C, Wiscombe W, et al.1988. Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media[J]. Appl. Opt.,27(12):2502-2509.
Stephens G L, Kummerow C D.2007. The Remote Sensing of Clouds and Precipitation from Space:A Review[J]. Journal of the Atmospheric Sciences,64(11):3742-3765.
Stowe L L, Davis P, Mcclain E P.1995. Evaluating the CLAVR (clouds from AVHRR) phase Ⅰ-cloud cover experimental product[J]. Advances in Space Research,16(10):21-24.
Stowe L L, Davis P A, Mcclain E P.1999. Scientific Basis and Initial Evaluation of the CLAVR-1 Global Clear/Cloud Classification Algorithm for the Advanced Very High Resolution Radiometer[J]. Journal of Atmospheric and Oceanic Technology,16(6):656-681.
Strabala K I, Ackerman S A, Menzel W P.1994. Cloud properties inferred from 8-12μm data[J]. Journal of Applied Meteorology,33(2):212-229.
Thies B, Nauss T, Bendix J.2008a. Discriminating raining from non-raining cloud areas at mid-latitudes using meteosat second generation SEVIRI night-time data[J]. Meteorological Applications,15(2):219-230.
Thies B, Nauss T, Bendix J.2008b. Discriminating raining from non-raining clouds at mid-latitudes using meteosat second generation daytime data[J]. Atmospheric Chemistry and Physics,8(9):2341-2349.
Tian B, Soden B J, Wu X.2004. Diurnal cycle of convection, clouds, and water vapor in the tropical upper troposphere:Satellites versus a general circulation model[J]. Journal of Geophysical Research,109(D10):D10101.
Vemury S, Stowe L L, Anne V R.2001. AVHRR Pixel Level Clear-Sky Classification Using Dynamic Thresholds (CLAVR-3)[J]. Journal of Atmospheric and Oceanic Technology,18(2): 169-186.
Wetherald R T, Manabe S.1988. Cloud Feedback Processes in a General Circulation Model [J]. Journal of Atmospheric Sciences,45(8):1397-1416.
Williams E, Rosenfeld D, Madden N, et al.2002. Contrasting convective regimes over the Amazon:Implications for cloud electrification[J]. Journal of Geophysical Research, 107(D20):8082.
Woodley W L, Rosenfeld D, Strautins A.2000. Identification of a seeding in Texas using multi-spectral satellite imagery[J]. Journal of Weather Modification,32:37-52.
Xie P, Arkin P A.1996. Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions[J]. Journal of Climate,9(4):840-858.
Zipser E J, Lutz K R.1994. The vertical profile of radar reflectivity of convective cells:A strong indicator of storm intensity and lightning probability?[J]. Monthly Weather Review,122(8): 1751-1759.
Zuidema P.2003. Convective Clouds over the Bay of Bengal[J]. Monthly Weather Review,131(5): 780-798.
陈英英,唐仁茂,周毓荃等.2009FY-2C/D卫星微物理特性参数产品在地面降水分析中的应用[J].气象,35(2)：15-18.
陈英英,周毓荃,毛节泰等.2007.利用FY-2C静止卫星资料反演云粒子有效半径的试验研究[J].气象,33(4)：29-34.
戴进,余兴,刘贵华等.2010.一次暴雨过程中云微物理特征的卫星反演分析[J].气象学报,68(3)：387-397.
丁伟钰,林爱兰.2003.GMS5多通道数据与TRMM资料估测华南地区热带气旋降水[J].热带气象学报,19(增刊)：74-80.
方宗义,许健民,赵凤生.2004.中国气象卫星和卫星气象研究的回顾和发展[J].气象学报,62(5)：550-560.
傅云飞,冯静夷,朱红芳等.2005.西太平洋副热带高压下热对流降水结构特征的个例分析[J].气象学报,63(5)：750-761.
傅云飞,宇如聪,徐幼平等.2003TRMM测雨雷达和微波成像仪对两个中尺度特大暴雨降水结构的观测分析研究[J].气象学报,61(4)：421-431.
傅云飞,刘栋,王雨等.2007.热带测雨卫星综合探测结果之“云娜”台风降水云与非降水云特征[J].气象学报,65(3)：316-328.
傅云飞,刘奇,自勇等.2008a.基于TRMM卫星探测的夏季青藏高原降水和潜热分析[J].高原山地气象研究,28(1)：8-18.
傅云飞,张爱民,刘勇等.2008b.基于星载测雨雷达探测的亚洲对流和层云降水季尺度特征分析[J].气象学报,66(5)：730-746.
傅云飞,冯沙,刘鹏等.2010.热带测雨卫星测雨雷达探测的亚洲夏季积雨云云砧[J].气象学报,68(2)：195-206.
傅云飞,刘鹏,刘奇等.2011.夏季热带及副热带降水云可见光/红外信号气候分布特征[J].大气与环境光学学报,6(2)：407-426.
李锐,傅云飞.2005.利用热带测雨卫星的测雨雷达资料对1997/1998年El Nino后期热带太平洋降水结构的研究[J].大气科学,29(2)：225-235.
刘诚,Gerry B, Hiroaki K等.2007.基于NOAA16-AVHRR数据反演中纬度陆地上空云类型及云顶高度信息[J].大气与环境光学学报,2(4)：301-305.
刘健,许健民,方宗义.1999.利用NOAA卫星的AVHRR资料试分析云和雾顶部粒子的尺度特征[J].应用气象学报,10(1)：28-33.
刘健,董超华.2002.卫星资料在云顶粒子尺度特征分析中的应用[J].红外与毫米波学报,21(2)：124-128.
刘健,董超华,张文建.2003a.利用FY-1C资料反演水云的光学厚度和粒子有效半径[J].红外与毫米波学报,22(6)：436-440.
刘健,董超华,朱元竞等.2003b.FY-1C资料在云顶粒子热力学相态分析中的应用研究[J].大气科学,27(5)：901-908.
刘健,李云.2011.风云二号静止气象卫星的云相态识别算法[J].红外与毫米波学报,30(4)：322-327.
刘鹏,傅云飞.2010.利用星载测雨雷达探测结果对夏季中国南方对流和层云降水气候特征的分析[J].大气科学,34(4)：802-814.
刘奇.2007.基于ISCCP及TRMM观测的热带降水云与非降水云差异的研究[J].博士毕业论文.
刘奇,傅云飞,冯沙.2010.基于ISCCP观测的云量全球分布及其在NCEP再分析场中的指示[J].气象学报,68(5)：689-704.
刘希,许健民,杜秉玉.2005.用双通道动态阈值对GMS-5图像进行自动云检测[J].应用气象学报,16(4)：434-444.
刘显通,刘奇,傅云飞.2011.基于光学厚度和有效半径的白天降水云识别方案[J].大气科学,35(5)：903-911.
闵爱荣,游然,卢乃锰等.2008TRMM卫星微波成像仪资料的陆面降水反演[J].热带气象学报,24(3)：265-272.
师春香,吴荣璋,项续康.2001.多阈值和神经网络卫星云图云系自动分割试验[J].应用气象学报,12(1)：70-78.
王雨,傅云飞,刘国胜.2006.热带测雨卫星TMI探测结果对非降水云液态水路径的反演方案研究[J].气象学报,64(4)：443-452.
徐海明,何金海,谢尚平.2007.卫星资料揭示的中尺度地形对南海夏季气候的影响[J].大气科学,31(5)：1021-1031.
叶晶,李万彪,严卫.2009.利用MODIS数据反演多层云光学厚度和有效半径[J].气象学报,67(4)：613-622.
赵凤生,丁强,孙同明等.2002.利用NOAA-AVHRR观测数据反演云辐射特性的一种迭代方法[J].气象学报,60(5)：594-601.
周青,赵凤生,高文华.2010.利用FY-2C卫星数据反演云辐射特性[J].大气科学,34(4)：827-842.
周毓荃,陈英英,李娟等.2008.用FY-2C/D卫星等综合观测资料反演云物理特性产品及检验[J].气象,34(12)：27-35.
周毓荃,蔡淼,欧建军等.2011.云特征参数与降水相关性的研究[J].大气科学学报,34(6)：641-652.
周著华,白洁,刘健文等.2005MODIS多光谱云相态识别技术的应用研究[J].应用气象学报,16(5)：678-683.
朱素行,徐海明,徐蜜蜜.2010.亚洲夏季风区中尺度地形降水结构及分布特征[J].大气科学,34(1)：71-82.