期刊检索:
  暴雨灾害   2018, Vol. 37 Issue (6): 493-501.  DOI: 10.3969/j.issn.1004-9045.2018.06.001

综述

DOI

10.3969/j.issn.1004-9045.2018.06.001

资助项目

科技部重点研发计划项目(2018YFC1507200);国家自然科学基金项目(91837310、41230419、91337213)

第一作者

傅云飞, 主要从事大气物理学、大气探测和遥感学的研究。E-mail:fyf@ustc.edu.cn

文章历史

收稿日期:2018-07-16
定稿日期:2018-12-25
云-降水遥感研究现状及夏季东亚云-降水研究思考
傅云飞     
中国科学技术大学地球和空间科学学院, 合肥 230026
摘要:就认知空间大范围的云-降水特征而言,卫星搭载的主被动仪器遥感探测具有独特的优势。本文从卫星多仪器遥感视角出发,就卫星多仪器遥感反演云宏微观参数、云参数与云辐射强迫、云参数和降水关系及其潜热廓线、气溶胶对云-降水作用的研究进行了有限综述,并就东亚夏季风区上述四个方面研究的不足,提出了需要关注的研究问题,最后提出利用多源卫星多仪器遥感结果研究夏季东亚云-降水结构的设想。
关键词云-降水    云参数    云辐射强迫    潜热廓线    卫星多仪器遥感    
Research actuality of remote sensing on cloud precipitation and reflections on summer East Asian cloud precipitation
FU Yunfei    
School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026
Abstract: In terms of the characteristics of cloud precipitation in a wide range of cognitive space, remote sensing by active and passive multi-instruments carried by satellites has unique advantages. From view of multi-source satellite remote sensing in this paper, the first part succinctly reviews satellite remote sensing inversion of macroscopic and micromicroscopic parameters, cloud parameters and cloud radiative forcing, relationship between cloud parameters and precipitation and its latent heat profiles, the effect of aerosol on cloud precipitation through literature review. The shortcomings related to the aforementioned four aspects in East Asian Monsoon are also discussed. In the end, this paper puts forward the idea of using multi-source satellite observations to study the structure of East Asian cloud precipitation in summer.
Key words: cloud precipitation    cloud parameter    cloud radiation forcing    latent heat of profile    satellite multi-instrument remote sensing    
引言

云-降水不仅是全球及区域水循环过程的重要环节,也是气候变化的重要指示剂,为此科学家从不同方面对云-降水进行了广泛的研究。东亚季风区是全球最显著的季风区,季风活动变化及其相应的极端天气,均与云-降水密切相关,也直接影响了人们的社会活动。我国科学家们主要利用地面观测结果,从天气学和大气动力学视角,结合数值模式模拟方法,对夏季季风进退规律与我国雨带位置变化进行了广泛研究[1-7],还通过组织华南地区暴雨试验、淮河水分循环试验、长江中下游梅雨锋暴雨野外观测试验、台湾地区中尺度暴雨试验、南方致洪暴雨监测预测等一系列大型科学试验,揭示东亚季风活动与该地区旱涝时空变化特征的关系、夏季梅雨活动与暴雨事件的关系等[8-11],提升了人们对东亚夏季风活动及降水的天气学和动力学认知。

然而,在云微雾物理学方面,科学家们对东亚夏季风活动与云-降水之间关系的诸多细节尚不清楚。如东亚夏季云-降水结构特征及时空变化、云参数分布特点及其与降水之间关系等,尚有待认知。作者2012年受国家自然科学基金重点项目支持,就东亚云-降水的卫星遥感问题进行研究,本文作为该研究成果的第一部分,将从卫星多仪器遥感视角,分析卫星多仪器遥感云-降水研究现状,探讨东亚夏季风活动期间云-降水研究的相关问题。该项目的其他研究结果部分将另成文叙述。

1 卫星多仪器遥感反演的云宏微观参数

自20世纪60年代TIROS-3卫星开始,卫星云图为认识云宏观特征奠定了基础。20世纪80年代初实施的国际卫星云气候计划(ISCCP,International Satellite Cloud Climatology Project),通过整合能覆盖全球的多颗静止卫星和极轨卫星的可见光与红外通道的遥感数据,反演计算给出了不同云属及其云量等参数,并积累了长时间数据,这为研究全球云宏观特征提供了便利[12-19]。在ISCCP新版云类型划分方案中,依据可见光和红外信号反演的云顶高度及云光学厚度,将云划分为九大类,并参照传统云类型名称进行了命名[16],但ISCCP采用可见光与红外信号遥感,使得它在识别降水云和多层云结构时尚存在不足,如ISCCP云产品仅依据云水含量阈值250 g·m-2,粗略地划分了降水云和非降水云,而这个阈值本身的有效性还值得深究。

在云微观参数研究方面,因探测技术等方面的制约,在很长一段时间里科学家们对云微观属性的认识不足。飞机观测只能提供有限区域云参数,还不能在有限时间里进行如半球尺度的观测;实验室中的一些实验研究,虽可实现云的核化、凝结、碰并等过程,但实验环境无法与实际大气状况完全吻合,因此其代表性也有限;近年来一些工作利用地基测云雷达和激光雷达的探测,并结合某些算法反演,揭示了局地云参数的特性,但目前这类测站分布有限,不足以认识大范围云系云参数特性。但随着卫星平台搭载更先进的可见光和红外传感器、微波成像探测器、测云雷达和激光雷达等仪器,如具有36个通道的中分辨率成像光谱仪(MODIS,Moderate-resolution Imaging Spectroradiometer)、20个通道的FY-3中分辨率光谱成像仪(MERSI,Medium Resolution Spectral Imager),其对云-降水信息遥感更加充分,结合不断进步的云参数反演技术[20-27],使得科学家们对云微观参数的认知得到逐步提升,如认知云顶高度和云水路径等特征、云发展过程中的云参数变化等[28-31]

目前科学家们对于不同类型云系的云参数特征的认识还不到位,特别是对多层云系的云参数认知还十分有限,因为卫星的光谱仪器遥感只能获取云系上层云的云顶附近信息,对云系中层及下层的云信息难以获取。Rosenfeld和Lensky[32]、Rosenfeld等[33]利用卫星搭载的可见光和红外辐射计或成像仪,遥感一定范围内不同高度云顶信息,反演得到云顶附近的云参数,并认为这些不同高度云顶附近的云参数可代表这个范围内云系的云体内部相应高度的云参数,进而得到云系的云参数廓线。可以认为这是一种不得已的近似,还有待观测证实其与真实云参数廓线的差异。

被动微波遥感获得的是微波天线指向的云柱及其下垫面上行辐射信号(积分信号),它难以得到多层云系云参数的垂直结构信息,但利用被动微波遥感,科学家们仍然对洋面上云系的某些云参数(如云水及冰水路径等)有了一定的认知[34-36],这是因为洋面的微波辐射背景冷,其上云-降水发射的微波信号容易被卫星搭载的微波成像仪低频通道探测获取。因此,在未来相当长一段时间里多层云系的云参数垂直结构将主要依靠主动微波(即云廓线雷达)和激光雷达探测来获取,特别是主被动仪器的综合遥感探测,如卫星搭载的MISR (Multi-angle Imaging SpectroRadiometer)、AIRS (Atmospheric Infrared Sounder)、OMI (Ozone Monitoring Instrument)、CALIPSO (Aerosol Lidar and Infrared Pathfinder Satellite Observations)和CPR (Cloud Profiling Radar)等仪器对多层云系具有良好探测能力,可研究揭示云系在垂直高度方向上出现的概率等[37],还可揭示过冷水云的液态水含量、出现频次及分布比例[38],反演得到冰云的冰水含量、冰粒子有效半径及冰粒子消光系数等参数[39]。Wang等[40]则结合MODIS和CPR探测结果,研究给出了全球降雪云中的液态水分布特征。

虽然卫星多仪器遥感反演的产品数据众多,且被用于认知大范围甚至全球范围内云系的宏微观参数的分布特征研究,但利用这些产品来进行东亚夏季风云系特性的研究还不多。科学家们对东亚夏季风云参数多时间尺度的变化规律的认知还有限,如典型天气系统的云参数特征、东亚夏季风活动过程中云参数的变化、云参数在降水过程中的演变、云参数的区域性和季节性特点等,还有待科学家们去认知。而认知东亚季风区的云系特性,可进一步研究云-降水潜热及其对季风活动的反馈作用。

2 云参数与云辐射强迫

云影响地球气候是因为云粒子大小和形状、浓度及相态等云微观参数直接与太阳长短波辐射相互作用,且不同类型云系的云微观参数存在差异,它们时空分布的变化直接改变地-气辐射收支平衡。研究表明这种辐射强迫效应超过目前所知的温室气体和大气溶胶等因素的辐射强迫效应[41]

近20 a来,随着大气辐射测量计划(ARM)、云和辐射观测实验研究(CART)等观测试验和数值模拟研究的开展,科学家们对云辐射效应有了进一步的认识[42-51],如揭示了云-降水粒子的散射和吸收性质[52],又如揭示了次尺度云微观参数及其面积比例的不确定对长短波辐射收支的影响[53],发现卫星仪器遥感和模式模拟的单层且云顶均匀的洋面层状低云辐射通量结果十分相近[54],揭示了卷云云粒子与辐射间的反馈过程等[55-56]。Bony和Emanuel [57]指出云辐射与湿对流之间的反馈是形成热带大气季内振荡的重要原因。

A-TRAIN卫星系列搭载的CERES (Clouds and the Earth's Radiant Energy System)及其它仪器的探测结果,为研究云的辐射效应提供了很好的数据。通过对CERES探测结果的分析,发现云增加了中纬度地区大气对短波的吸收,而长波辐射效应在热带地区增加、极区减少[58];对于同类型的单个云体,虽然其云水含量、冰水含量本身变化较大,但统计结果显示云水和冰水含量的比例相当稳定[59]。对于很薄的卷云(光学厚度小于0.3),被动遥感常常探测失效,Sun等[60]利用CALIPSO与CERES、MODIS和AIRS相结合,对薄卷云及其辐射效应进行了研究,指出这类云平均每天的短波辐射通量约2.5 W·m-2;Li等[61]研究指出,与单层云系相比,多层云系反射的短波辐射少、透过到地面的短波辐射多。

卷云形状复杂、分布广泛,它们既散射太阳短波辐射,同时也吸收短波辐射,因此减少了到达地面的太阳短波辐射,使得地面降温;另一方面,卷云也吸收和发射热红外辐射,使得卷云具有温室气体的增温效应。由于卷云对地气辐射收支平衡的影响很大,学者们也给予了很大的关注[62-64],如Min等[65]利用CALIOP和MODIS探测结果,研究了中国地区上空卷云的辐射强迫效应,指出总的平均净辐射可达(36.5±48.4) W·m-2

如何利用卫星主被动仪器遥感反演得到的云参数来获得云辐射强迫效应?一种方法是将反演得到的云参数带入辐射传输模式中,结合其自带的大气温湿结构或利用探空得到的大气温湿结构,来计算地面得到的下行长短波辐射通量、大气顶得到的上行长短波辐射通量,如Yang等[66]利用这种方法得到热带和副热带云砧的辐射强迫效应。另一种方法是利用卫星搭载的CERES探测结果,来分析云系上行的长短波辐射及总辐射的强迫效应大小。但不论哪种方法,均需要利用地基辐射仪器观测结果来进行检验。

目前科学家们对东亚夏季风雨带云系的云辐射强迫大小认知还不够,如5—6月我国南方阴雨,温度不高,而北方时常晴热,大气低层温度呈现南低北高的分布,就是典型的云辐射强迫所致。云系辐射强迫会造成下垫面热力变化,进而改变大气稳定度等参数的空间分布,并反馈作用于季风系统包括云-降水。这一过程中与云辐射相联系的细节还不甚清楚,如局地或一定空间范围内降水前后地面长短波辐射的变化、该变化对大气稳定度等参数的影响,又如云系随季风推进的过程、云参数变化引起的辐射强迫改变等,均有待于深入研究和探讨。

3 云参数和降水的关系及其潜热结构

成云致雨过程中,影响降水的云微物理过程十分重要,它既是天气模式和云模式中云过程描述的理论基础,也是人工影响天气、气溶胶和云-降水相互作用的理论基础[67-71]。然而,云参数作为云微物理过程的产物,它与降水之间究竟存在何种关系尚不清楚。Ronsenfeld等[72-73]和Lensky等[74-75]采用了多种理想的云物理学假设,提出通过云红外亮温(T)和云滴有效半径(Re)分布来识别积云特征和云-降水形成过程,该方法为科学家们研究云参数与降水关系提供了一种思路,但上述结果还有待于更多的研究来证实。目前,只能通过机载仪器进行有限的穿云观测来获得云体内云参数信息[76-78],更多的云微物理过程中云参数及其与大气参数之间的关系,还要依靠数值模式[79-83]

一定的分辨率(如光谱像元或微波像元)上融合卫星的光谱仪器、微波仪器和测雨雷达等多仪器探测结果,并结合云参数反演算法,如在测雨雷达像元上建立云参数与降水廓线(回波强度廓线)的融合数据,是研究云参数与降水关系的一条有效途径。作者在此方面进行了一些探索,如将热带测雨卫星(TRMM)搭载的测雨雷达(PR)、可见光与红外扫描仪(VIRS)探测结果在PR像元(星下点近5 km)上进行融合[84-90],然后利用VIRS可见光和近红外通道信号结合辐射传输模式计算[91],反演了云顶附近的云参数(云粒子有效半径、云光学厚度及云水路径),由此获得了PR测到的降水回波反射率因子、降水率廓线及降水云顶的云参数的融合数据集[92],据此,可方便地分析近地面降水强度与云顶附近云参数之间的关系。如作者统计了中国江淮、华南及热带西太平洋暖池地区1998—2007年夏季对流降水与层状降水云的云粒子有效半径与地表降水率之间关系,结果表明三个区域的水云(热红外10.8 μm通道温度高于268 K,即暖云)、暖性对流和层状降水云的Re均随降水率的增大而减小,西太平洋暖池尤为如此,这或许是由于这类暖云降水,云中的上升气流不强,因此大粒子均离开了云顶部,下沉到了云体的下部,从而增强降水强度。研究还表明西太平洋暖池云顶热红外通道温度低于268 K的对流降水云的Re似乎不随降水强度的增大而变化,可能洋面深厚降水过程主要发生在云体的中下层(如粒子碰并增长),故云顶附近粒子大小变化幅度不大;而这里较为深厚的层状降水云的Re随着降水率的增大稍有减小,这是因为洋面层状降水多由对流降水消亡产生,这时云中的上升气流弱,在重力作用下大粒子下降速度较小粒子快,造成地表降水增大,而云层上部包括云顶附近的Re相对小。在江淮及华南地区,深厚对流降水云的Re随着降水率增大而增大,说明这两个区域夏季强对流降水发生时,云内出现强烈的上升气流可以把中低层大云粒子带到云顶附近,致使云顶附近云粒子尺度也相应增大[92]。作者还利用该融合资料对我国南方暴雨云微物理参数与地表降水强度进行了系统分析,指出暴雨系统的强降水区云顶部云粒子有效半径多在15 μm以上。

潜热是成云-降水过程不可回避的问题之一。在地球气候系统中降水潜热释放对大气能量有着十分重要的贡献,在驱动大气环流的能量中,潜热能量约占75%。潜热分布的时空变化将引起不同高度大气的显著响应[93-94]。虽然气柱潜热能够根据降水强度来推算[95-96],但如何获取潜热的垂直结构(即廓线)目前仍是一个挑战。目前国际上仅有的六种降水潜热算法虽然让科学家们在一定程度上了解了潜热结构,但仍有很大的局限性,主要表现在这些算法严重地依赖云模式而非实际观测信息[97-101]。Li等[102]提出利用星载测雨雷达探测的降水廓线,结合云模式的模拟,可以提供较以往更为精准的降水潜热廓线估计。

对于东亚季风区,降水过程中降水强度变化与云参数改变之间存在何种内在联系、降水强度与相应的云参数存在怎样的关系、云参数对降水类型和结构是否具有指示意义、云-降水潜热垂直分布及变化与东亚季风活动间存在怎样的关系等,科学家们对这些问题的认知均有待提升。

4 气溶胶对云-降水的作用

气溶胶对云-降水的作用仍是目前极具有挑战性的难题之一。20世纪90年代中后期的印度洋试验计划(INDOEX)是这方面具有代表性的研究,该研究计划首次揭示了干季印度次大陆城市尘雾对其下风洋面地区云-降水的影响,发现印度次大陆随风漂移至洋面的气溶胶呈明显梯度分布,而云水含量、云粒子大小等云参数也相应成明显的梯度分布[36]。这种气溶胶与云的相互作用,形成了该区域南北梯度分布的大气辐射平衡[103-105]

在东亚及西北太平洋近海地区,APEX (Asian Atmospheric Particle Environmental Change Studies)、ACE-Asia (Aerosol Characterization Experiment-Asia)及TRACE-P (Transport and Chemical Evolution over the Pacific)的三次综合观测试验[106-109],揭示了该地区由于自然型沙尘和人为排放黑炭、硫酸盐、硝酸盐的混合,造成了气溶胶光学特性本身的不稳定性,并使得春季平均的晴空直接辐射强迫强度远远超过全球均值[110-111]。2004年中美联合实施的EAST-AIRE (East Asian Studies of Tropospheric Aerosols: an International Regional Experiment)亚洲对流层气溶胶观测计划研究结果,也揭示了城市霾雾气溶胶在区域尺度气溶胶辐射强迫中的独特作用[112-113]

观测研究表明气溶胶对云参数和降水的作用仍存在一些不确定性。一些研究认为降水被气溶胶抑制[114-115];而另外的研究则发现气溶胶使得降水强度增加[121]。气溶胶对降水落区的影响也存在分歧[116-118]。研究还表明不同气溶胶因其吸湿性、短波吸收率和尺寸等的差异,它在降水过程中对云参数的作用也有所不同,故对降水的影响亦存在不同[119]。最近的研究表明,相比于干洁区域,矿物沙尘气溶胶高浓度区域的层状降水云中小粒子多,且云冰粒子丰富[120];而对印度季风爆发和中断阶段的气溶胶和云相互作用研究结果表明,这两个阶段的印度中部和东北部及赤道印度洋的云光学厚度、云量、云高等云参数有显著差异,由此可见季风活动中不同阶段,气溶胶对云-降水的作用也不同[121]

作者及其合作者的研究表明,中国东部春夏季的沙尘和麦秆燃烧,使得江淮和黄淮流域的气溶胶光学厚度增大,其下风向气溶胶光学厚度则逐渐下降;云参数分析表明上述地区的云粒子有效半径比海洋的小[122]。而对15 a的PR探测结果分析表明,东亚季风降水以层状降水为主(> 70%),但对流降水和层状降水对总降水的贡献相当;研究还发现夏季东亚大气低层的季风活动在年际尺度上显著影响大气稳定度和降水强度,大气稳定度进一步调制着降水类型的面积比例和贡献比例。科学家们尚不清楚数值天气模式是否能给出东亚地区降水的上述年际变化特点,并给出相应的作用机制[123-125]

然而,正如Koren等[116]于2012年指出的那样:气溶胶对云,特别是对降水的影响,人们知道的还远远不够!因为这一影响过程还取决于地区、季节及时空尺度。此外,就卫星光谱遥感研究气溶胶对云-降水的作用而言,就是一对矛盾,因为云天时,卫星光谱仪器不能对云下方的气溶胶进行有效探测,而这些仪器能遥感到气溶胶时,多为晴空少云。利用星载激光雷达探测,可以解决薄云情况下的云及下方气溶胶的同步探测,但对厚云及降水云情况,激光雷达也难以有效探测云下方的气溶胶。因此,单纯利用卫星搭载仪器来研究气溶胶与云-降水的作用存在难度。作者及合作者,利用星载激光雷达探测,通过分析晴空区与云的过渡区,尝试研究气溶胶与云的相互作用[129]。但到目前为止,就东亚夏季风活动过程中,气溶胶对伴随季风活动的云-降水及潜热有怎样的作用,还正在研究之中。

5 利用多源卫星多仪器观测结果研究夏季东亚云-降水结构设想

2002年以来美国航空航天局(NASA)先后发射了A-Train系列卫星(Aqua、Aura、PARASOL、CALIPSO、CloudSat等)、GPM (Global Precipitation Measurement)等卫星,以卫星太空编队方式探测获得地球大气、陆面、海洋、资源和环境等信息。搭载在这些卫星上的众多仪器,为科学家们进一步研究云-降水及相关问题提供了丰富而全新的资料,如CERES利用3个宽波段探测,可以直接获取大气顶辐射通量的主要部分;MODIS通过36个可见光红外波段通道,能在大尺度空间范围内对云系和气溶胶实现高分辨率的遥测,且提供云参数产品和气溶胶产品资料;AIRS利用包括2 378条谱线的高光谱红外通道探测,可以给出大气温度廓线等产品;AMSR-E则利用12个微波通道探测,给出云水、水汽、降水信息;CALIOP通过两个波长的激光探测,可以给出更为细致的气溶胶和云廓线及其特性;CloudSat的CPR则通过94 GHz频率的主动微波探测,提供云相态、云中液态水和冰水含量、云滴粒径及其云廓线产品。此外,NASA也在尝试进行产品资料的融合,如MODIS与CloudSat探测结果的融合资料,就可以直接给出云的类型、云相态、云顶温度、云顶气压、有效云量、云水含量和云冰含量、云粒子有效半径和云光学厚度等一整套云微物理参数[126-129]。这些丰富的资料既为科学家们研究东亚夏季风云-降水时空特征提供了基础数据,同时也有待科学家们在研究中评估和检验其质量,因为利用光谱和微波及雷达探测结果,反演得到的这些产品资料仍有不同程度的不确定性。

东亚夏季风云-降水结构的研究,先要解决云参数及云-降水结构的数据集问题,建立云参数、云-降水类型及其廓线的时空同步且高空间分辨率的融合数据集是重要的基础。在具备这些数据基础上,首先研究东亚夏季风活动期间云参数、降水的气候特征,如不同类型云参数空间分布特征、不同类型降水空间分布特征及其垂直结构特征,上述特征的区域性差异等;其次是研究与东亚夏季风活动相联系的云参数与降水强度的关系,如雨带进退时云参数与降水强度之间关系,又如强对流及暴雨的云参数与降水强度的关系等;再次就是研究降水潜热垂直结构、云辐射强迫对季风活动的反馈作用,如对流降水与层状降水的潜热结构及其相联系的次级垂直环流对季风活动有怎样的反馈作用等,云辐射效应与季风区对流活动的关系等;最后是研究夏季风活动的特定区域气溶胶如何作用云参数及降水?在水汽输送充分和不充分时,气溶胶的间接效应表现何种差异?获得上述研究结果将从云雾物理学和大气热动学视角,提升人们对东亚夏季风云-降水特征的认知。

参考文献
[1]
陶诗言, 丁一汇, 周晓平. 暴雨和强对流天气的研究[J]. 大气科学, 1979, 3(3): 227-238. DOI:10.3878/j.issn.1006-9895.1979.03.05
[2]
陶诗言. 中国之暴雨[M]. 北京: 科学出版社, 1980.
[3]
Zhu Q, He J, Wang P. A study of circulation differences between East-Asian and Indian summer monsoons with their interaction[J]. Advances in Atmospheric Sciences, 1986, 3(4): 466-477.
[4]
Ding Y, Hu J. The variation of the heat sources in East China in the early summer of 1984 and their effects on the large-scale circulation in East Asia[J]. Advances in Atmospheric Sciences, 1988, 5(2): 171-180.
[5]
Ding Y. Summer monsoon rainfall in China[J]. Journal of the Meteorological Society of Japan, 1992, 70: 373-396. DOI:10.2151/jmsj1965.70.1B_373
[6]
吴国雄, 张永生. 青藏高原的热力和机械强迫作用以及亚洲季风的爆发[J]. 大气科学, 1998, 22(6): 825-838. DOI:10.3878/j.issn.1006-9895.1998.06.03
[7]
张庆云, 陶诗言. 夏季东亚热带和副热带季风与中国东部汛期降水[J]. 应用气象学报, 1998(S1): 17.
[8]
贝耐芳, 赵思雄. 1998年"二度梅"期间突发强暴雨系统的中尺度分析[J]. 大气科学, 2002, 26(4): 526-540. DOI:10.3878/j.issn.1006-9895.2002.04.10
[9]
孙健, 赵平, 周秀骥. 一次华南暴雨的中尺度结构及复杂地形的影响[J]. 气象学报, 2002, 60(3): 333-341. DOI:10.3321/j.issn:0577-6619.2002.03.009
[10]
廖捷, 谈哲敏. 一次梅雨锋特大暴雨过程的数值模拟研究:不同尺度天气系统的影响作用[J]. 气象学报, 2005, 63(5): 771-789. DOI:10.3321/j.issn:0577-6619.2005.05.021
[11]
刘奇, 傅云飞. 基于TRMM/TMI的亚洲夏季降水研究[J]. 中国科学:D辑, 2007, 37(1): 111-122.
[12]
Schiffer R A, Rossow W B. The International Satellite Cloud Climatology Project H-Series climate data record product[J]. Earth System Science Data, 2018, 10(1): 583-593. DOI:10.5194/essd-10-583-2018
[13]
Minnis P, Heck P W, Young D F, et al. Stratocumulus cloud properties derived from simultaneous satellite and island-based instrumentation during FIRE[J]. Journal of Applied Meteorology, 1992, 31(4): 317-339.
[14]
Klein S A, Hartmann D L. The seasonal cycle of low stratiform clouds[J]. Journal of Climate, 1993, 6(8): 1587-1606. DOI:10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2
[15]
Rossow W B, Garder L C. Cloud detection using satellite measurements of infrared and visible radiances for ISCCP[J]. Journal of climate, 1993, 6(12): 2341-2369. DOI:10.1175/1520-0442(1993)006<2341:CDUSMO>2.0.CO;2
[16]
Rossow W B, Schiffer R A. Advances in understanding clouds from IS-CCP[J]. Bulletin of the American Meteorological Society, 1999, 80(11): 2261-2287. DOI:10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2
[17]
刘洪利, 朱文琴, 宜树华, 等. 中国地区云的气候特征分析[J]. 气象学报, 2003, 61(4): 466-473. DOI:10.3321/j.issn:0577-6619.2003.04.008
[18]
李昀英, 宇如聪, 徐幼平, 等. 中国南方地区层状云的形成和日变化特征分析[J]. 气象学报, 2003, 61(6): 733-743. DOI:10.3321/j.issn:0577-6619.2003.06.010
[19]
刘奇, 傅云飞. 热带地区云量日变化的气候特征[J]. 热带气象学报, 2009, 25(6): 717-724. DOI:10.3969/j.issn.1004-4965.2009.06.010
[20]
Arking A, Childs J. Retrieval of cloud cover parameters from multispectral satellite images[J]. Journal of Climate and Applied Meteorology, 1985, 24(4): 322-333. DOI:10.1175/1520-0450(1985)024<0322:ROCCPF>2.0.CO;2
[21]
Huang H, Diak G. Retrieval of nonprecipitating liquid water cloud parameters from microwave data:A simulation study[J]. Journal of Atmospheric and Oceanic Technology, 1992, 9(4): 354-363. DOI:10.1175/1520-0426(1992)009<0354:RONLWC>2.0.CO;2
[22]
Rao N, Ou S C, Liou K N. Removal of the solar component in AVHRR 3.7μm radiances for the retrieval of cirrus cloud parameters[J]. Journal of Applied Meteorology, 1995, 34(2): 482-499. DOI:10.1175/1520-0450-34.2.482
[23]
MaKague D, Evans K F. Multichannel satellite retrieval of cloud parameter probability distribution functions[J]. Journal of the atmospheric sciences, 2002, 59(8): 1371-1382. DOI:10.1175/1520-0469(2002)059<1371:MSROCP>2.0.CO;2
[24]
Zhao L M, Weng F Z. Retrieval of ice cloud parameters using the Advanced Microwave Sounding Unit[J]. Journal of Applied Meteorology, 2002, 41(4): 384-395. DOI:10.1175/1520-0450(2002)041<0384:ROICPU>2.0.CO;2
[25]
Stephens G L, Vane D G, Boain R J, et al. The CloudSat mission and the A-Train:A new dimension of space-based observations of clouds and precipitation[J]. Bulletin of the American Meteorological Society, 2002, 83(12): 1771-1790.
[26]
Winker D M, Pelon J, Coakley Jr J A, et al. The CALIPSO mission:A global 3D view of aerosols and clouds[J]. Bulletin of the American Meteorological Society, 2010, 91(9): 1211-1230. DOI:10.1175/2010BAMS3009.1
[27]
Wang C, Yang P, Baum B A, et al. Retrieval of ice cloud optical thickness and effective particle size using a fast infrared radiative transfer model[J]. Journal of Applied Meteorology and Climatology, 2011, 50(11): 2283-2297. DOI:10.1175/JAMC-D-11-067.1
[28]
Weisz E, Li J, Menzel W P, et al. Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals[J]. Geophysical Research Letters, 2007, 34(17): 251-270.
[29]
Greenwald T J. A 2 year comparison of AMSR-E and MODIS cloud liquid water path observations[J]. Geophysical Research Letters, 2009, 36(20): 146-158.
[30]
Meskhidze N, Remer L A, Platnick S, et al. Exploring the differences in cloud properties observed by the Terra and Aqua MODIS Sensors[J]. Atmos Chem Phys, 2009, 9(10): 3461-3475. DOI:10.5194/acp-9-3461-2009
[31]
Lee S, Kahn B H, Teixeira J. Characterization of cloud liquid water content distributions from CloudSat[J]. Journal of Geophysical Research:Atmospheres, 2010, 115(D20): 1-13.
[32]
Rosenfeld D, Lensky I M. Satellite-based insights into precipitation formation processes in continental and maritime convective clouds[J]. Bulletin of the American Meteorological Society, 1998, 79(11): 2457-2476. DOI:10.1175/1520-0477(1998)079<2457:SBIIPF>2.0.CO;2
[33]
Rosenfeld D, Dai J, Yu X, et al. Inverse relations between amounts of air pollution and orographic precipitation[J]. Science, 2007, 315(5817): 1396-1398. DOI:10.1126/science.1137949
[34]
Liu G S, Curry J A. Determination of characteristic features of cloud liquid water from satellite microwave measurements[J]. Journal of Geophysical Research, 1993, 98(D3): 5069-5092. DOI:10.1029/92JD02888
[35]
Liu G S, Curry J A. Remote Sensing of Ice Water Characteristics in Tropical Clouds Using Aircraft Microwave Measurements[J]. Journal of Applied Meteorology, 1998, 37: 337-355. DOI:10.1175/1520-0450(1998)037<0337:RSOIWC>2.0.CO;2
[36]
Liu G S, Curry J A, Haggerty J A, et al. Retrieval and characterization of cloud liquid water path using airborne passive microwave data during INDOEX[J]. Journal of Geophysical Research:Atmospheres, 2001, 106(D22): 28719-28730. DOI:10.1029/2000JD900782
[37]
Wu D L, Ackerman S A, Davies R, et al. Vertical distributions and relationships of cloud occurrence frequency as observed by MISR, AIRS, MODIS, OMI, CALIPSO, and CloudSat[J]. Geophysical Research Letters, 2009, 36(9): 77-85.
[38]
Hu Y, Rodier S, Xu K, et al. Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/ⅡR/MODIS measurements[J]. Journal of Geophysical Research Atmospheres, 2010, 115(D4): 1-17.
[39]
Delanoe J, Hogan R J. Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds[J]. Journal of Geophysical Research:Atmospheres, 2010, 115(D4): 1-17.
[40]
Wang Y, Liu G S, Seo E K, et al. Liquid water in snowing clouds:Implications for satellite remote sensing of snowfall[J]. Atmospheric research, 2013, 131: 60-72. DOI:10.1016/j.atmosres.2012.06.008
[41]
Wetherald R T, Manabe S. Cloud feedback processes in a general circulation model[J]. Journal of the Atmospheric Sciences, 1988, 45(8): 1397-1416. DOI:10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2
[42]
Stephens G L, Miller S D, Benedetti A, et al. The department of energy's atmospheric radiation measurement (arm) unmanned aerospace vehicle (uav) program[J]. Bulletin of the American Meteorological Society, 2000, 81(12): 2915-2938. DOI:10.1175/1520-0477(2000)081<2915:TDOESA>2.3.CO;2
[43]
Li Z, Trishchenko A P. Quantifying uncertainties in determining SW cloud radiative forcing and cloud absorption due to variability in atmospheric conditions[J]. Journal of the atmospheric sciences, 2001, 58(4): 376-389. DOI:10.1175/1520-0469(2001)058<0376:QUIDSC>2.0.CO;2
[44]
Guichard F, Parsons D B, Dudhia J, et al. Evaluating mesoscale model predictions of clouds and radiation with SGP ARM data over a seasonal timescale[J]. Monthly weather review, 2003, 131(5): 926-944. DOI:10.1175/1520-0493(2003)131<0926:EMMPOC>2.0.CO;2
[45]
Tao W K, Johnson D, Shie C L, et al. The atmospheric energy budget and large-scale precipitation efficiency of convective systems during TOGA COARE, GATE, SCSMEX, and ARM:Cloud-resolving model simulations[J]. Journal of the atmospheric sciences, 2004, 61(20): 2405-2423. DOI:10.1175/1520-0469(2004)061<2405:TAEBAL>2.0.CO;2
[46]
Mather J H, Mather J H. Seasonal variability in clouds and radiation at the Manus ARM site[J]. Journal of climate, 2005, 18(13): 2417-2428. DOI:10.1175/JCLI3401.1
[47]
Dong X, Minnis P, Xi B. A climatology of midlatitude continental clouds from the ARM SGP central facility. Part Ⅱ:Cloud fraction and surface radiative forcing[J]. Journal of climate, 2006, 19(9): 1765-1783. DOI:10.1175/JCLI3710.1
[48]
Miller M A, Slingo A. The ARM Mobile Facility and its first international deployment:Measuring radiative flux divergence in West Africa[J]. Bulletin of the American Meteorological Society, 2007, 88(8): 1229-1244. DOI:10.1175/BAMS-88-8-1229
[49]
Mace G G, Benson S. The vertical structure of cloud occurrence and radiative forcing at the SGP ARM site as revealed by 8 years of continuous data[J]. Journal of Climate, 2008, 21(11): 2591-2610. DOI:10.1175/2007JCLI1987.1
[50]
Xie S C, McCoy R B, Klein S A, et al. Clouds and more:ARM climate modeling best estimate data:A new data product for climate studies[J]. Bulletin of the American Meteorological Society, 2010, 91(1): 13-20. DOI:10.1175/2009BAMS2891.1
[51]
Danahe P R, Jones C, Vaillancourt P A. Using ARM observations to evaluate cloud and clear-sky radiation processes as simulated by the Canadian regional climate model GEM[J]. Monthly Weather Review, 2010, 138(3): 818-838. DOI:10.1175/2009MWR2745.1
[52]
Lhermitte R. Attenuation and scattering of millimeter wavelength radiation by clouds and precipitation[J]. Journal of Atmospheric and Oceanic Technology, 1990, 7(3): 464-479.
[53]
Chin M, Rood R B, Lin S J, et al. Atmospheric sulfur cycle simulated in the global model GOCART:Model description and global properties[J]. Journal of Geophysical Research:Atmospheres, 2000, 105(D20): 24671-24687. DOI:10.1029/2000JD900384
[54]
Chang F, Li Z, Ackerman S A. Examining the relationship between cloud and radiation quantities derived from satellite observations and model calculations[J]. Journal of climate, 2000, 13(21): 3842-3859. DOI:10.1175/1520-0442(2000)013<3842:ETRBCA>2.0.CO;2
[55]
Gu Y, Liou K N. Interactions of radiation, microphysics, and turbulence in the evolution of cirrus clouds[J]. Journal of the atmospheric sciences, 2000, 57(15): 2463-2479. DOI:10.1175/1520-0469(2000)057<2463:IORMAT>2.0.CO;2
[56]
Gu Y, Liou K N. Radiation parameterization for three-dimensional inhomogeneous cirrus clouds:Application to climate models[J]. Journal of climate, 2001, 14(11): 2443-2457. DOI:10.1175/1520-0442(2001)014<2443:RPFTDI>2.0.CO;2
[57]
Bony S, Emanuel K A. On the role of moist processes in tropical intraseasonal variability:Cloud-radiation and moisture-convection feedbacks[J]. Journal of the atmospheric sciences, 2005, 62(8): 2770-2789. DOI:10.1175/JAS3506.1
[58]
Kato S, Rose F G, Rutan D A, et al. Cloud effects on the meridional atmospheric energy budget estimated from Clouds and the Earth's Radiant Energy System (CERES) data[J]. Journal of Climate, 2008, 21(17): 4223-4241. DOI:10.1175/2008JCLI1982.1
[59]
Lin B, Minnis P, Fan T, et al. Radiation characteristics of low and high clouds in different oceanic regions observed by CERES and MODIS[J]. International Journal of Remote Sensing, 2010, 31(24): 6473-6492. DOI:10.1080/01431160903548005
[60]
Sun W, Videen G, Kato Seiji S, et al. A study of subvisual clouds and their radiation effect with a synergy of CERES, MODIS, CALIPSO, and AIRS data[J]. Journal of Geophysical Research:Atmospheres, 2011, 116(D22): 1-10.
[61]
Li J, Yi Y, Minnis P, et al. Radiative effect differences between multi-layered and single-layer clouds derived from CERES, CALIPSO, and CloudSat data[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2011, 112(2): 361-375.
[62]
Liou K N. Influence of cirrus clouds on weather and climate processes:A global perspective[J]. Monthly Weather Review, 1986, 114(6): 1167-1199. DOI:10.1175/1520-0493(1986)114<1167:IOCCOW>2.0.CO;2
[63]
Jensen E J, Kinne S, Toon O B. Tropical cirrus cloud radiative forcing:Sensitivity studies[J]. Geophysical research letters, 1994, 21(18): 2023-2026. DOI:10.1029/94GL01358
[64]
Ou S C, Liou K N, Takano Y, et al. Remote sensing of cirrus cloud particle size and optical depth using polarimetric sensor measurements[J]. Journal of the atmospheric sciences, 2005, 62(12): 4371-4383. DOI:10.1175/JAS3634.1
[65]
Min M, Wang P, James J R, et al. Midlatitude cirrus cloud radiative forcing over China[J]. Journal of Geophysical Research:Atmospheres, 2010, 115(D20): 1-14.
[66]
Yang Y, Fu Y, Qin F, et al. Radiative forcing of the tropical thick anvil evaluated by combining TRMM with atmospheric radiative transfer model[J]. Atmospheric Science Letters, 2017, 18(5): 222-229. DOI:10.1002/asl.2017.18.issue-5
[67]
Ou S C, Liou K, Takano Y, et al. Remote sensing of cirrus cloud particle size and optical depth using polarimetric sensor measurements[J]. Journal of the atmospheric sciences, 2005, 62(12): 4371-4383. DOI:10.1175/JAS3634.1
[68]
Khain A, Pokrovsky A, Pinsky M, et al. Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part Ⅰ:Model description and possible applications[J]. Journal of the atmospheric sciences, 2004, 61(24): 2963-2982. DOI:10.1175/JAS-3350.1
[69]
Rosenfeld D, Givati A. Evidence of orographic precipitation suppression by air pollution-induced aerosols in the western United States[J]. Journal of Applied Meteorology and Climatology, 2006, 45(7): 893-911. DOI:10.1175/JAM2380.1
[70]
Storelvmo T, Kristjansson J E, Lohmann U. Aerosol influence on mixed-phase clouds in CAM-Oslo[J]. Journal of the atmospheric sciences, 2008, 65(10): 3214-3230. DOI:10.1175/2008JAS2430.1
[71]
Wood R, Kubar T L, Hartmann D L. Understanding the importance of microphysics and macrophysics for warm rain in marine low clouds. Part Ⅱ:Heuristic models of rain formation[J]. Journal of the Atmospheric Sciences, 2009, 66(10): 2973-2990. DOI:10.1175/2009JAS3072.1
[72]
Rosenfeld D, Wolff D B, Amitai E. The window probability matching method for rainfall measurements with radar[J]. Journal of applied meteorology, 1994, 33(6): 682-693. DOI:10.1175/1520-0450(1994)033<0682:TWPMMF>2.0.CO;2
[73]
Rosenfeld D, Lensky I M. Satellite-based insights into precipitation formation processes in continental and maritime convective clouds[J]. Bulletin of the American Meteorological Society, 1998, 79(11): 2457-2476. DOI:10.1175/1520-0477(1998)079<2457:SBIIPF>2.0.CO;2
[74]
Lensky I M, Rosenfeld D. Estimation of precipitation area and rain intensity based on the microphysical properties retrieved from NOAA AVHRR data[J]. Journal of Applied Meteorology, 1997, 36(3): 234-242. DOI:10.1175/1520-0450(1997)036<0234:EOPAAR>2.0.CO;2
[75]
Lensky I M, Rosenfeld D. Satellite-based insights into precipitation formation processes in continental and maritime convective clouds at nighttime[J]. Journal of Applied Meteorology, 2003, 42(9): 1227-1233. DOI:10.1175/1520-0450(2003)042<1227:SIIPFP>2.0.CO;2
[76]
Woodley W L, Rosenfeld D, Silverman B A, et al. Results of on-top glaciogenic cloud seeding in Thailand. Part Ⅱ:Exploratory analyses[J]. Journal of Applied Meteorology, 2003, 42(7): 939-951. DOI:10.1175/1520-0450(2003)042<0939:ROOGCS>2.0.CO;2
[77]
Rosenfeld D, Kaufman Y J, Koren I. Switching cloud cover and dynamical regimes from open to closed Benard cells in response to the suppression of precipitation by aerosols[J]. Atmospheric Chemistry and Physics, 2006, 6(9): 2503-2511.
[78]
周臖, 雷恒池, 陈洪滨, 等. 层析法微波辐射计遥感反演云液水含量的二维垂直分布[J]. 大气科学, 2010, 34(5): 1011-1025. DOI:10.3878/j.issn.1006-9895.2010.05.15
[79]
胡志晋, 秦瑜, 王玉彬. 层状冷云数值模式[J]. 气象学报, 1983, 41(2): 194-203.
[80]
肖辉, 徐华英, 黄美元. 积云中云滴谱形成的数值模拟研究(一)[J]. 大气科学, 1988, 12(2): 121-130. DOI:10.3878/j.issn.1006-9895.1988.02.02
[81]
Reisin T, Levin Z, Tzivion S. Rain production in convective clouds as simulated in an axisymmetric model with detailed microphysics. Part Ⅰ:Description of the model[J]. Journal of the atmospheric sciences, 1996, 53(3): 497-519. DOI:10.1175/1520-0469(1996)053<0497:RPICCA>2.0.CO;2
[82]
郭学良, 黄美元, 徐华英, 等. 层状云降水微物理过程的雨滴分档数值模拟[J]. 大气科学, 1999, 23(6): 745-752. DOI:10.3878/j.issn.1006-9895.1999.06.11
[83]
雷恒池, 洪延超, 赵震, 等. 近年来云降水物理和人工影响天气研究进展[J]. 大气科学, 2008, 32(4): 967-974. DOI:10.3878/j.issn.1006-9895.2008.04.21
[84]
傅云飞, 冯沙, 刘鹏, 等. 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, 2010, 68(2): 195-206. DOI:10.3969/j.issn.1006-8775.2010.02.012
[85]
傅云飞, 刘鹏, 刘奇, 等. 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, 2011, 6(2): 129. DOI:10.3969/j.issn.1673-6141.2011.02.009
[86]
Fu Y, Chen F, Liu G, et al. Recent trends of summer convective and stratiform precipitation in mid-eastern China[J]. Scientific reports, 2016, 6: 33044. DOI:10.1038/srep33044
[87]
潘晓, 傅云飞. 夏季青藏高原深厚及浅薄降水云气候特征分析[J]. 高原气象, 2015, 34(5): 1191-1203.
[88]
Qin F, Fu Y. TRMM-observed summer warm rain over the tropical and subtropical Pacific Ocean:Characteristics and regional differences[J]. Journal of Meteorological Research, 2016, 30(3): 371-385. DOI:10.1007/s13351-016-5151-x
[89]
Chen Y, Fu Y. Characteristics of VIRS Signals within Pixels of TRMM PR for Warm Rain in the Tropics and Subtropics[J]. Journal of Applied Meteorology and Climatology, 2017, 56(3): 789-801. DOI:10.1175/JAMC-D-16-0198.1
[90]
Fu Y, Pan X, Xian T, et al. Precipitation characteristics over the steep slope of the Himalayas in rainy season observed by TRMM PR and VIRS[J]. Climate Dynamics, 2017, 37(11): 1-19.
[91]
Nakajima T, King M D. Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part Ⅰ:Theory[J]. Journal of the atmospheric sciences, 1990, 47(15): 1878-1893.
[92]
Fu Y. Cloud parameters retrieved by the bispectral reflectance algorithm and associated applications[J]. Journal of Meteorological Research, 2014, 28(5): 965-982. DOI:10.1007/s13351-014-3292-3
[93]
Schumacher C, Houze Jr R A, Kraucunas I. The tropical dynamical response to latent heating estimates derived from the TRMM precipitation radar[J]. Journal of the Atmospheric Sciences, 2004, 61(12): 1341-1358. DOI:10.1175/1520-0469(2004)061<1341:TTDRTL>2.0.CO;2
[94]
Choudhury A D, Krishnan R. Dynamical response of the South Asian monsoon trough to latent heating from stratiform and convective precipitation[J]. Journal of the Atmospheric Sciences, 2011, 68(6): 1347-1363. DOI:10.1175/2011JAS3705.1
[95]
Yanai M S, Esbensen S, Chu J H. Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets[J]. Journal of the Atmospheric Sciences, 1973, 30(4): 611-627. DOI:10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2
[96]
Yang S, Smith E A. Moisture budget analysis of TOGA COARE area using SSM/I-retrieved latent heating and large-scale Q2 estimates[J]. Journal of Atmospheric and Oceanic Technology, 1999, 16(6): 633-655. DOI:10.1175/1520-0426(1999)016<0633:MBAOTC>2.0.CO;2
[97]
Tao W K, Lang S, Olson W S, et al. Retrieved vertical profiles of latent heat release using TRMM rainfall products for February 1998[J]. Journal of Applied Meteorology, 2001, 40(6): 957-982. DOI:10.1175/1520-0450(2001)040<0957:RVPOLH>2.0.CO;2
[98]
Tao W K, Smith E A, Adler R F, et al. Retrieval of latent heating from TRMM measurements[J]. Bulletin of the American Meteorological Society, 2006, 87(11): 1555-1572. DOI:10.1175/BAMS-87-11-1555
[99]
Chan S C, Nigam S. Residual diagnosis of diabatic heating from ERA-40 and NCEP reanalyses:Intercomparisons with TRMM[J]. Journal of Climate, 2009, 22(2): 414-428. DOI:10.1175/2008JCLI2417.1
[100]
Hagos S, Zhang C, Tao W K, et al. Estimates of tropical diabatic heating profiles:Commonalities and uncertainties[J]. Journal of Climate, 2010, 23(3): 542-558. DOI:10.1175/2009JCLI3025.1
[101]
Zhang C, Ling J, Hagos S, et al. MJO signals in latent heating:Results from TRMM retrievals[J]. Journal of the Atmospheric Sciences, 2010, 67(11): 3488-3508. DOI:10.1175/2010JAS3398.1
[102]
Li R, Min Q, Wu X, et al. Retrieving latent heating vertical structure from cloud and precipitation profiles-Part Ⅱ:Deep convective and stratiform rain processes[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2013, 122: 47-63. DOI:10.1016/j.jqsrt.2012.11.029
[103]
Rhoads K P, Kelley P, Dickerson R R, et al. Composition of the troposphere over the Indian Ocean during the monsoonal transition[J]. Journal of Geophysical Research:Atmospheres, 1997, 102(D15): 18981-18995. DOI:10.1029/97JD01078
[104]
Jayaraman A, Lubin D, Ramachandran S, et al. Direct observations of aerosol radiative forcing over the tropical Indian Ocean during the January-February 1996 pre-INDOEX cruise[J]. Journal of Geophysical Research:Atmospheres, 1998, 103(D12): 13827-13836. DOI:10.1029/98JD00559
[105]
Jr J A C, Tahnk W R, Jayaraman A, et al. Aerosol optical depth and direct radiative forcing for INDOEX derived from AVHRR:Observations, January-March 1996-2000[J]. Journal of Geophysical Research:Atmospheres, 2002, 107(D19): 8-18.
[106]
Nakajima T, Sekiguchi M, Takemura T, et al. Significance of direct and indirect radiative forcings of aerosols in the East China Sea region[J]. Journal of Geophysical Research:Atmospheres, 2003, 108(D23): 2139-2146.
[107]
Huebert B J, Bates T, Russell P B, et al. An overview of ACE-Asia:Strategies for quantifying the relationships between Asian aerosols and their climatic impacts[J]. Journal of Geophysical Research:Atmospheres, 2003, 108(D23): 1-20.
[108]
Seinfeld J H, Carmichael G R, Arimoto R, et al. ACE-ASIA:regional climatic and atmospheric chemical effects of Asian dust and pollution[J]. Bulletin of the American Meteorological Society, 2004, 85(3): 367-380. DOI:10.1175/BAMS-85-3-367
[109]
Jacob D J, Crawford J H, Kleb M M, et al. Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission:Design, execution, and first results[J]. Journal of Geophysical Research:Atmospheres, 2003, 108(D20): 1-19.
[110]
Remer L A, Kaufman Y J. Aerosol direct radiative effect at the top of the atmosphere over cloud free ocean derived from four years of MODIS data[J]. Atmospheric Chemistry and Physics, 2006, 6(1): 237-253. DOI:10.5194/acp-6-237-2006
[111]
Yu H, Kaufman Y J, Chin M, et al. A review of measurement-based assessments of the aerosol direct radiative effect and forcing[J]. Atmospheric Chemistry and Physics, 2006, 6(3): 613-666. DOI:10.5194/acp-6-613-2006
[112]
Li Z, Xia X, Cribb M, et al. Aerosol optical properties and their radiative effects in northern China[J]. Journal of Geophysical Research:Atmospheres, 2007, 112(D22): 321-341.
[113]
Xin J, Wang Y, Li Z, et al. Aerosol optical depth (AOD) and Ångström exponent of aerosols observed by the Chinese Sun Hazemeter Network from August 2004 to September 2005[J]. Journal of Geophysical Research:Atmospheres, 2007, 112(D5): 1703-1711.
[114]
Albrecht B A. cloud microphysics, and fractional cloudiness[J]. Science, 1989, 245(4923): 1227-1230. DOI:10.1126/science.245.4923.1227
[115]
Rosenfeld D. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall[J]. Geophysical research letters, 1999, 26(20): 3105-3108. DOI:10.1029/1999GL006066
[116]
Koren I, Orit A, Lorraine A R, et al. Aerosol-induced intensification of rain from the tropics to the mid-latitudes[J]. Nature Geoscience, 2012, 5(2): 118-122.
[117]
Borys R D, Lowenthal D H, Mitchell D L. The relationships among cloud microphysics, chemistry, and precipitation rate in cold mountain clouds[J]. Atmospheric Environment, 2000, 34(16): 2593-2602. DOI:10.1016/S1352-2310(99)00492-6
[118]
Givati A, Rosenfeld D. Quantifying precipitation suppression due to air pollution[J]. Journal of Applied meteorology, 2004, 43(7): 1038-1056. DOI:10.1175/1520-0450(2004)043<1038:QPSDTA>2.0.CO;2
[119]
Lynn B, Khain A, Rosenfeld D, et al. Effects of aerosols on precipitation from orographic clouds[J]. Journal of Geophysical Research:Atmospheres, 2007, 112(D10): 1-13.
[120]
Paldor N. On the estimation of trends in annual rainfall using paired gauge observations[J]. Journal of Applied Meteorology and Climatology, 2008, 47(6): 1814-1818. DOI:10.1175/2007JAMC1697.1
[121]
Min Q, Li R, Lin B, et al. Evidence of mineral dust altering cloud microphysics and precipitation[J]. Atmospheric Chemistry and Physics, 2009, 9(9): 3223-3231. DOI:10.5194/acp-9-3223-2009
[122]
Kiran V R, Rajeevan M, Rao S V B, et al. Analysis of variations of cloud and aerosol properties associated with active and break spells of Indian summer monsoon using MODIS data[J]. Geophysical Research Letters, 2009, 36(9): 1-5.
[123]
Chen Y L, Fu Y F, Yang Y J, et al. Analysis of relations between aerosol optical depth and cloud parameters over land and offshore area of Eastern China and America[C]//Remote Sensing of the Atmosphere, Clouds, and Precipitation V. International Society for Optics and Photonics, 2014
[124]
Fu Y, Chen F, Liu G, et al. Recent trends of summer convective and stratiform precipitation in mid-eastern China[J]. Scientific reports, 2016, 6: 33044. DOI:10.1038/srep33044
[125]
Lu D, Yang Y, Fu Y. Interannual variability of summer monsoon convective and stratiform precipitations in East Asia during 1998-2013[J]. International Journal of Climatology, 2016, 36(10): 3507-3520.
[126]
Remer L A, Kaufman Y J, Tanre D, et al. The MODIS aerosol algorithm, products, and validation[J]. Journal of the atmospheric sciences, 2005, 62(4): 947-973. DOI:10.1175/JAS3385.1
[127]
Ackerman S A, Holz R E, Frey R, et al. Cloud detection with MODIS. Part Ⅱ:validation[J]. Journal of Atmospheric and Oceanic Technology, 2008, 25(7): 1073-1086. DOI:10.1175/2007JTECHA1053.1
[128]
Menzel W P, Frey R A, Zhang H, et al. MODIS global cloud-top pressure and amount estimation:Algorithm description and results[J]. Journal of Applied Meteorology and Climatology, 2008, 47(4): 1175-1198. DOI:10.1175/2007JAMC1705.1
[129]
Wind G, Platnick S, King M D, et al. Multilayer cloud detection with the MODIS near-infrared water vapor absorption band[J]. Journal of Applied Meteorology and Climatology, 2010, 49(11): 2315-2333. DOI:10.1175/2010JAMC2364.1