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寇蕾蕾，郜海阳，林正健，廖淑君，丁丕满，朱维，商建，胡秀清.XXXX.星载主动遥感测云现状与展望.遥感学报，XX（XX）： 1-20
KOU Leilei，GAO Haiyang，LIN Zhengjian，LIAO Shujun，DING Piman，ZHU Wei，SHANG Jian，HU Xiuqing. XXXX. Status and Prospect of cloud measurement by satellite active remote sensing. National Remote Sensing Bulletin， XX（XX）：1-20
云是表征天气和气候变化的重要指标，在大气的能量分配、辐射传输等中起着重要作用。卫星遥感探测以其覆盖范围广、信息量大、重复频率高等诸多优势，成为研究云的主要手段。目前星载测云主动遥感技术主要包括毫米波雷达和激光雷达技术。本文介绍了星载毫米波雷达和激光雷达测云技术发展及数据应用研究现状，重点分析了CloudSat搭载的云廓线雷达CPR和CALIPSO搭载的云-气溶胶激光雷达CALIOP协同观测的成果和进一步发展需求。从全球云三维结构参数的需求分析，探讨了太赫兹雷达、高光谱激光雷达、多传感器数据融合等测云新技术发展和应用潜能，以及多星系统及单星多载荷等协同观测新模式的技术关键和轨道高度对探测性能的影响，提出多传感器共平台协同观测及数据融合研究三维云结构信息的设想。
ObjectiveCloud is an important indicator of weather and climate change. It is of great significance to both atmospheric energy distribution and radiative transfer. Satellite remote sensing has become an indispensable technology for cloud research due to its global coverage, multiple types of information, and high repetitive frequency. This work summarizes the development of cloud measurement technology and data application research of current spaceborne millimeter wave radar and lidar. The future development requirements and trend are then proposed according to the progress of current cloud measurement technology and applications.MethodFirstly, the technology research and data typical application status of spaceborne millimeter wave radar and lidar are introduced and summarized, in which the key technologies and important application of the cloud profile radar (CPR) on CloudSat and the cloud aerosol lidar (CALIOP) on CALIPSO are highlighted. The achievements of collaborative observation by both the CPR and CALIOP are then summarized emphatically as well as the aspects to be improved. Based on the requirement analysis of obtaining three-dimensional fine structure of global cloud, the development and application potential of novel measurement technologies such as terahertz radar, high spectral resolution lidar and multi-sensor data fusion are provided. Furthermore, we discuss the key technologies of the new cooperative observation modes, such as multi-satellite system and multi-payload on single satellite platform. The influence of orbit altitude on the detection performance is discussed based on the analysis on the relationship between the received intensity of echo signal and the orbit altitude.ResultIt is pointed out that the cloud load performance of active remote sensing, cooperative observation mode, cooperation with passive remote sensing instruments and multi-source data retrieval and fusion for spaceborne cloud measurement still need to be improved, according to the research status of spaceborne cloud radar and lidar. The latest advanced technology of spaceborne active remote sensing can be fully utilized for observing more accurate cloud parameters and more microphysical information, such as multi-wavelength, multi-polarization, Doppler technology, terahertz radar, hyperspectral lidar and multi-sensor data fusion method. The collaborative observation of microwave radar, lidar and other sensors on the same platform is necessary for better understanding the formation and evolution of cloud, so as to further improve the ability of weather forecasting and climate monitoring.ConclusionSatellite active remote sensing for cloud measurement is employed to observe accurately the macroscopic and microphysical characteristics of cloud. Its high-quality data has been widely applied to cloud physics, weather forecasting, environment monitoring and climate change. Also, it plays an important role on improving the accuracy of numerical weather forecasting and climate research. With the continuous progress of active remote sensing technology, more cloud parameters and higher performance can be realized for spaceborne cloud measurement. Besides the applications of high-tech in the spaceborne cloud remote sensing, collaborative observation is an important development trend. A future perspective is then projected on the cooperative observation mode of multi sensors with common platform and new data fusion methods.
星载测云主动遥感星载毫米波雷达星载激光雷达协同观测数据融合
Satellite cloud measurementactive remote sensingspaceborne millimeter wave radarspaceborne lidarcooperative observationdata fusion
Adhikari L, Wang Z E and Deng M. 2012. Seasonal variations of Antarctic clouds observed by CloudSat and CALIPSO satellites. Journal of Geophysical Research, 117(D4): D04202 [DOI: 10.1029/2011JD016719http://dx.doi.org/10.1029/2011JD016719]
Adhikari L and Wang Z E. 2013. An A-train satellite based stratiform mixed-phase cloud retrieval algorithm by combining active and passive sensor measurements. British Journal of Environment & Climate Change, 3(4): 587-611 [DOI: 10.9734/BJECC/2013/3055http://dx.doi.org/10.9734/BJECC/2013/3055]
Austin R T, Heymsfield A J and Stephens G L. 2009. Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature. Journal of Geophysical Research, 114(D00A23):1-19 [DOI:10.1029/2008JD010049http://dx.doi.org/10.1029/2008JD010049]
Barker H W, Jerg M P, Wehr T, Kato S, Donovan D P and Hogan R J. 2011. A 3D cloud‐construction algorithm for the EarthCARE satellite mission. Quarterly Journal of the Royal Meteorological Society, 137(657): 1042-1058 [DOI: 10.1002/qj.824http://dx.doi.org/10.1002/qj.824]
Battaglia A, Westbrook C D, Kneifel S, Kollias P, Humpage N and Petty G W. 2014. G-band atmospheric radars: new frontiers in cloud physics. Atmospheric Measurement Techniques, 7: 1527-1546 [DOI: 10.5194/amtd-7-1527-2014http://dx.doi.org/10.5194/amtd-7-1527-2014]
Battaglia A, Dhillon R and Lllingworth A. 2018. Doppler W-band polarization diversity spaceborne radar simulation for wind studies. Atmospheric Measurement Technique, 11: 5965-5979 [DOI: 10.5194/amt-11-5965-2018http://dx.doi.org/10.5194/amt-11-5965-2018]
Battaglia A, Kollias P, Dhillon R, Roy R, Tanelli S, Lamer K, Grecu M, Lebsock M, Watters D, Mroz K, Heymsfield G, Li L and Furukawa K. 2020. Spaceborne cloud and precipitation radars: status, challenges, and ways forward. Reviews of Geophysics, 58: 1-59 [DOI: 10.1029/2019RG000686http://dx.doi.org/10.1029/2019RG000686]
Carminati F, Atkinson N, Candy B, Lu Q F. 2020. Insight into the microwave instruments onboard the FengYun 3D satellite: data quality and assimilation in the Met office NWP system. Advances in Atmospheric Sciences, 8: 1-18 [DOI: 10.1007/s00376-020-0010-1http://dx.doi.org/10.1007/s00376-020-0010-1]
Chen G, Yang J, Zhang B T and Ma C Y. 2019. Thoughts and prospects on the new generation of marine science satellites. Periodical of Ocean University of China(Natural Science), 49(10): 110-117
陈戈, 杨杰, 张本涛, 马纯永. 2019. 新一代海洋科学卫星的思考与展望. 中国海洋大学学报 (自然科学版), 49(10): 110-117 [DOI: 10.16441/j.cnki.hdxb.20190226http://dx.doi.org/10.16441/j.cnki.hdxb.20190226]
Chen T M, Guo J P, Li Z Q, Zhao C F, Liu H, Cribb M, Wang F and He J. 2016. A CloudSat Perspective on the Cloud Climatology and Its Association with Aerosol Perturbations in the Vertical over Eastern China. Journal of the Atmospheric Sciences, 73(9): 3599-3616 [DOI: 10.1175/JAS-D-15-0309.1http://dx.doi.org/10.1175/JAS-D-15-0309.1]
Casella D, Panegrossi G, Sano P, Marra A C, Dietrich S, Johnson B T and Kulie M S. 2017. Evaluation of the GPM-DPR snowfall detection capability: comparison with CloudSat-CPR. Atmospheric Research, 197: 64-75 [DOI: 10.1002/2016WR019961http://dx.doi.org/10.1002/2016WR019961]
Chen Z T and Sun X B. 2019. Dynamic spatial fusion of cloud top phase from PARASOL, CALIPSO, CloudSat satellite data. Journal of Quantitative Spectroscopy and Radiative Transfer, 224: 176-184 [DOI: 10.1016/j.jqsrt.2018.11.010http://dx.doi.org/10.1016/j.jqsrt.2018.11.010]
Chepfer H, Bony S, Winker D, Chiriaco M, Dufresne J L and Sèze G. 2008. Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model. Geophysical Research Letters, 35(15): 1-13 [DOI: 10.1029/2008GL034207http://dx.doi.org/10.1029/2008GL034207]
da Silva A M, Maring H, Seidel F, Behrenfeld M, Ferrare R and Mace G. 2020. Aerosol, Cloud, Ecosystems (ACE) Final Study Report. National Aeronautics and Space Administration, Goddard Space Flight Center. [https://gmao.gsfc.nasa.gov/pubs/docs/da%20Silva1247.pdfhttps://gmao.gsfc.nasa.gov/pubs/docs/da%20Silva1247.pdf]
Dasa S K, Golhaitb P B and Umac K N. 2017. Clouds vertical properties over the Northern Hemisphere monsoon regions from CloudSat-CALIPSO measurements. Atmospheric Research, 183: 73-83 [DOI: 10.1016/j.atmosres.2016.08.011http://dx.doi.org/10.1016/j.atmosres.2016.08.011]
Devasthale A and Thomas M A. 2011. A global survey of aerosol-liquid water cloud overlap based on four years of CALIPSO-CALIOP data. Atmospheric Chemistry and Physics, 11(3): 22109-22130 [DOI: 10.5194/acpd-10-22109-2010http://dx.doi.org/10.5194/acpd-10-22109-2010]
Deng M, Mace G G, Wang Z and Berry E. 2015. CloudSat 2C-ICE product update with a new Ze parameterization in lidar-only region, Journal of Geophysical Research: Atmosphere, 120(12):198-208 [DOI:10.1002/2015JD023600http://dx.doi.org/10.1002/2015JD023600]
Ding S G, Zhao C S, Shi G Y and Wu C A. 2005. Analysis of global total cloud amount variation over the past 20 years. Journal of Applied Meteorological Science, 16(5): 670-677
丁守国，赵春生，石广玉，武春爱. 2005. 近20年全球云量变化趋势分析. 应用气象学报，16(5): 670-677 [DOI: 10.3969/j.issn.1001-7313.2005.05.014http://dx.doi.org/10.3969/j.issn.1001-7313.2005.05.014]
do Carmo J P, Villele G, Helière A, Wallace K, Lefebvre A and Chassat F. 2019. ATLID, ESA atmospheric backscatter LIDAR for the ESA EarthCARE mission. CEAS Space Journal, 11(4): 423-435 [DOI: 10.1007/s12567-019-00284-6http://dx.doi.org/10.1007/s12567-019-00284-6]
Dodson J B, Tylor P C and Branson M. 2018. Microphysical variability of Amazonian deep convective cores observed by CloudSat and simulated by a multiscale modeling framework. Atmospheric Chemistry and Physics, 18(9): 6493-6510. [DOI: 10.5194/acp-18-6493-2018http://dx.doi.org/10.5194/acp-18-6493-2018]
Donaldson J R. 1955. The Measurement of Cloud Liquid-Water Content by Radar. Journal of the Atmospheric Sciences 12: 238-244 [DOI: 10.1175/1520-0469(1955)012.0.CO;2http://dx.doi.org/10.1175/1520-0469(1955)012.0.CO;2]
Fu Y F. 2018. Research actuality of remote sensing on cloud precipitation and reflections on summer East Asian cloud precipitation. Torrential Rain and Disasters, 37(6): 493-451
傅云飞. 2018.云-降水遥感研究现状及夏季东亚云-降水研究思考. 暴雨灾害，37(6): 493-451 [DOI: 10.3969/j.issn.1004-9045.2018.06.001http://dx.doi.org/10.3969/j.issn.1004-9045.2018.06.001]
Galletti M, Huang D and Kollias P. 2014. Zenith/nadir pointing mm-wave radars: linear or circular polarization? IEEE Transactions on Geoscience and Remote Sensing, 52(1): 628-639 [DOI: 10.1109/TGRS.2013.2243155http://dx.doi.org/10.1109/TGRS.2013.2243155]
Gelsthorpe R, Toulemont Y, Hélière A and Hors L. 2017. ATLID, the atmospheric lidar on board the Earthcare Satellite. International Conference on Space Optics, 10564: 105642D [DOI: 10.1117/12.2309095http://dx.doi.org/10.1117/12.2309095]
Grenier P, Blanchet J P and Muñoz‐Alpizar R. 2009. Study of polar thin ice clouds and aerosols seen by CloudSat and CALIPSO during midwinter 2007. Journal of Geophysical Research, 114: D09201 [DOI: 10.1029/2008JD010927http://dx.doi.org/10.1029/2008JD010927]
Guo Z and Zhou T J. 2015. Seasonal variation and physical properties of the cloud system over southeastern China derived from CloudSat products. Advances in Atmospheric Sciences, 32(5): 659-670 [DOI: 10.1007/s00376-014-4070-yhttp://dx.doi.org/10.1007/s00376-014-4070-y]
Guo J J, Yan Z A, Wu S H, Song X Q and Liu Z S. 2008. Low level atmospheric temperature measurement with high spectral resolution lidar. Journal of Optoelectronics Laser, 19(1): 66-69
郭金家, 闫召爱, 吴松华, 宋小全, 刘智深. 2008. 高光谱分辨率激光雷达测量低层大气温度. 光电子.激光, 19(1): 66-69 [DOI: 10.16136/j.joel.2008.01.017http://dx.doi.org/10.16136/j.joel.2008.01.017]
Guo J P, Liu B M, Gong W, Shi L J, Zhang Y, Ma Y Y, Zhang J, Chen T M, Bai K X, Stoffelen A, Leeuw G and Xu X F. 2021. Technical note: First comparison of wind observations from ESA’s satellite mission Aeolus and ground-based radar wind profiler network of China. Atmospheric Chemistry and Physics, 21: 2945-2958 [DOI: 10.5194/acp-21-2945-2021http://dx.doi.org/10.5194/acp-21-2945-2021]
Guo J P, Liu H, Wang F, Huang J F, Xia F, Lou M Y, Wu Y R, Jiang J H, Xie T, Zhai Y Z and Yung Y L. 2016. Three-dimensional structure of aerosol in China: A perspective from multi-satellite observations. Atmospheric Research, 178-179: 580-589 [DOI: 10.1016/j.atmosres.2016.05.010http://dx.doi.org/10.1016/j.atmosres.2016.05.010]
Guo J P, Lou M Y, Miao Y C, Wang Y, Zeng Z L, Liu H, He J, Xu H, Wang F, Min M and Zhai P M. 2017. Trans-Pacific transport of dust aerosols from East Asia: Insights gained from multiple observations and modeling. Environmental Pollution, 230: 1030-1039 [DOI: 10.1016/j.envpol.2017.07.062http://dx.doi.org/10.1016/j.envpol.2017.07.062]
Hashino T, Satoh M, Hagihara Y, Kubota T, Matsui T, Nasuno T and Okamoto H. 2013. Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO. Journal of Geophysical Research, 118(13): 7273-7292 [DOI: 10.1002/jgrd.50564http://dx.doi.org/10.1002/jgrd.50564.]
Heidinger A K, Liu C Y, Kahn B H, Kahn B H and Liu C Y. 2007. Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals. Geophysical Research Letters, 34(17): L17811 [DOI: 10.1029/2007GL030676http://dx.doi.org/10.1029/2007GL030676]
Helière A, Wallace K, do Carmo P J, Eisinger M, Wehr T and Lefebvre A. 2017. EarthCARE instruments description. ESA (European Space Agency) Report [https://earth.esa.int/web/guest/missions/esa-future-missions/earthcarehttps://earth.esa.int/web/guest/missions/esa-future-missions/earthcare]
Holzworth G C and Edinger J G. 1960. Angcl observations with the AN/TPQ-6 at Santa Monica, Calif. Proceedings of the Eighth Weather Radar Conference, 135-142
Hu Z Y, Zhao C, Huang J P, Leung L R, Qian Y, Yu H B, Huang L and Kalashnikova O V. 2016. Trans-Pacific transport and evolution of aerosols: evaluation of quasi-global WRF-Chem simulation with multiple observations. Geoscientific Model Development, 9: 1725-1746 [DOI: 10.5194/gmd-9-1725-2016http://dx.doi.org/10.5194/gmd-9-1725-2016]
Huang J, Fu Q, Su J, Tang Q, Minnis P, Hu Y, Yi Y and Zhao Q. 2009. Taklimakan dust aerosol radiative heating derived from CALIPSO observations using the Fu-Liou radiation model with CERES constraints. Atmospheric Chemistry and Physics, 9(12): 4011-4021 [DOI: 10.5194/acp-9-4011-2009http://dx.doi.org/10.5194/acp-9-4011-2009]
Huang J F, Guo J P, Wang F, Liu Z Y, Jeong M J, Yu H B and Zhang Z B. 2015. CALIPSO inferred most probable heights of global dust and smoke layers. Journal of Geophysical Research: Atmosphere, 120: 5085-5100 [DOI: 10.1002/2014JD022898http://dx.doi.org/10.1002/2014JD022898]
Hunt W H, Winker D M, Vaughan M A, Powell K A, Lucker P L and Weimer C. 2009. CALIPSO lidar description and performance assessment. Journal of Atmospheric and Oceanic Technology, 26(7): 1214-1228 [DOI: 10.1175/2009JTECHA1223.1http://dx.doi.org/10.1175/2009JTECHA1223.1]
Illingworth A J, Barker H W, Beljaars A, Ceccaldi M, Chepfer H, Clerbaux N, Cole J, Delanoë J, Domenech C, Donovan D P, Fukuda S, Hirakata M, Hogan R J, Huenerbein A, Kollias P, Kubota T, Nakajima T, Nakajima T Y, Nishizawa T, Ohno Y, Okamoto H, Oki R, Sato K, Satoh M, Shephard M W, Velázquez-Blázquez A, Wandinger, U, Wehr T and Van Z G. 2015. The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation. Bulletin of the American Meteorological Society 96(8): 1311-1332 [DOI: 10.1175/BAMS-D-12-00227.1http://dx.doi.org/10.1175/BAMS-D-12-00227.1]
Im E, Durden S L, Li F K, Wu C and Haddad Z. 2001. CloudSat radar instrument design and development status. IEEE International Geoscience & Remote Sensing Symposium, Sydney, Australia 691-693 [DOI: 10.1109/IGARSS.2001.976604http://dx.doi.org/10.1109/IGARSS.2001.976604]
Kalesse H，Kollias P and Szyrmer W. 2013. On using the relation- ship between Doppler velocity and radar reflectivity to identify microphysical processes in midlatitudinal ice clouds. Journal of Geophysical Research，118(21) : 12-12 [DOI: 10.1002/2013JD020386http://dx.doi.org/10.1002/2013JD020386]
Kato S, Sun‐Mack S, Miller W F, Rose F G, Chen Y, Minnis P and Wielicki B A. 2010. Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles. Journal of Geophysical Research, 115(D4): D00H28 [DOI: 10.1029/2009JD012277http://dx.doi.org/10.1029/2009JD012277]
Kato S, Rose F G, Sun-Mack S, Miller W F, Rutan D A, Stephens G L, Loeb N G, Minnis P and Wielicki B A. 2011. Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties. Journal of Geophysical Research, 116: D19209. [DOI:10.1029/2011JD016 050http://dx.doi.org/10.1029/2011JD016050]
Kollias P, Clothiaux E E, Miller M A, Albrecht B A, Stephens G L and Ackerman T P. 2007. Millimeter-wavelength radars: new frontier in atmospheric cloud and precipitation research. Bulletin of the American Meteorological Society, 88(10): 1608-1624 [DOI: 10.1175/BAMS-88-10-1608http://dx.doi.org/10.1175/BAMS-88-10-1608]
Kollias P, Clothiaux E E, Ackerman T P, Albrecht B A, Widener K B, Moran K P and Mace G G. 2016. Development and applications of ARM millimeter-wavelength cloud radar. Meteorological Monographs,57: 17.1-17.19 [DOI: 10.1175/AMSMONOGRAPHS-D-15-0037.1http://dx.doi.org/10.1175/AMSMONOGRAPHS-D-15-0037.1]
Kukulies J, Chen D L and Wang M H. 2019. Temporal and spatial variations of convection and precipitation over the Tibetan Plateau based on recent satellite observations, part1: cloud climatology derived from CloudSat and CALIPSO. International Journal of Climatology, 39(4): 5396-5412 [DOI: 10.1002/joc.6162http://dx.doi.org/10.1002/joc.6162]
Labelle R, Girard R and Arbery G. 2003. A 94 GHz RF electronics subsystem for the CloudSat Cloud Profiling Radar. IEEE 33rd European Microwave Conference, Munich, Germany [DOI: 10.1109/EUMC.2003.1262856http://dx.doi.org/10.1109/EUMC.2003.1262856]
L'Ecuyer T S. 2011. Touring the Atmosphere Aboard the A-Train. American Institute of Physics, 1401(1): 245-256 [DOI: 10.1063/1.3653856http://dx.doi.org/10.1063/1.3653856]
L'Ecuyer T S, Wood N B, Haladay T, Stephens G L and Stackhouse Jr P W. 2008. Impact of clouds on atmospheric heating based on the R04 CloudSat fluxes and heating rates data set. Journal of Geophysical Research, 113: D00A15, [DOI:10.1029/2008JD009951http://dx.doi.org/10.1029/2008JD009951]
Li J, Qin Z and Liu G. 2016. A new generation of Chinese FY-3C microwave sounding measurements and the initial assessments of its observations. International Journal of Remote Sensing, 37(17): 4035-4058 [DOI: 10.1080/01431161.2016.1207260http://dx.doi.org/10.1080/01431161.2016.1207260]
Li L, Heymsfield G M, Racette P E, Tian L and Zenker E. 2004. A 94-GHz cloud radar system on a NASA high-altitude ER-2 aircraft. Journal of atmospheric and oceanic technology, 21(9): 1378-1388 [DOI: 10.1175/1520-0426(2004)0212.0.CO;2http://dx.doi.org/10.1175/1520-0426(2004)0212.0.CO;2]
Li Z Q, Barker H W and Moreau L. 1995. The variable effect of clouds on atmospheric absorption of solar radiation. Nature, 376(6540): 486-490 [DOI: 10.1038/376486a0http://dx.doi.org/10.1038/376486a0]
Liu B, Ma Y, Gong W, Zhang M, Wang W and Shi Y. 2018. Comparison of AOD from CALIPSO, MODIS, and sun photometer under different conditions over Central China. Scientific reports, 8(1): 10066. [DOI: 10.1038/s41598-018-28417-7http://dx.doi.org/10.1038/s41598-018-28417-7]
Liu Y M, Yan Y F, Lv J H and Liu X L. 2018. Review of current investigations of cloud, radiation and rainfall over the Tibetan Plateau with the CloudSat/CALIPSO dataset. Chinese Journal of Atmospheric Sciences, 42(04): 847-858
刘屹岷，燕亚菲，吕建华，刘肖林. 2018. 基于CloudSat/CALIPSO卫星资料的青藏高原云辐射及降水的研究进展.大气科学，42(04)：847-858.
Liu Y, Key J R, Ackerman S A, Mace G G and Zhang Q. 2012. Arctic cloud macrophysical characteristics from CloudSat and CALIPSO. Remote Sensing of Environment, 124: 159-173 [DOI: 10.1016/j.rse.2012.05.006http://dx.doi.org/10.1016/j.rse.2012.05.006]
Liu Z, Vaughan M, Winker D M, Kittaka C, Getzewich B, Kuehn R, Omar A, Powell K, Trepte C and Hostetler C. 2009. The CALIPSO lidar cloud and aerosol discrimination: version 2 algorithm and initial assessment of performance. Journal of Atmospheric and Oceanic Technology, 26(7): 1198-1213 [DOI: 10.1175/2009JTECHA1229.1http://dx.doi.org/10.1175/2009JTECHA1229.1]
Lu N M, Min M, Dong L X, Guo J P, Niu T, Liu H L, Bi Y M, Wang X and Chen L. 2016. Development and prospect of spaceborne LiDAR for atmospheric detection. Journal of Remote Sensing, 20(1)：1-10
卢乃锰，闵敏，董立新，郭建平，牛涛，刘洪利，毕研盟，王新，陈林. 2016. 星载大气探测激光雷达发展与展望. 遥感学报，20(1)：1-10 [DOI: 10.11834/jrs.20165050http://dx.doi.org/10.11834/jrs.20165050]
Luo Z, Liu G Y and Stephens G L. 2008. CloudSat adding new insight into tropical penetrating convection. Geophysical Research Letters, 35(19): 610-616 [DOI:10.1029/2008GL035330http://dx.doi.org/10.1029/2008GL035330]
Lux O, Lemmerz C, Weiler F, Marksteiner U, Witschas B, Rahm S, Alexander G and Reitebuch O. 2020. Intercomparison of wind observations from the European Space Agency's Aeolus satellite mission and the ALADIN Airborne Demonstrator. Atmospheric Measurement Techniques, 13(4): 2075-2097 [DOI: 10.5194/amt-13-2075-2020http://dx.doi.org/10.5194/amt-13-2075-2020]
Mace G G, Zhang Q, Vaughan M, Marchand R, Stephens G L, Trepte C, and Winker D M. 2009. A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data. Journal of Geophysical Research, 114: D00A26 [DOI: 10.1029/2007JD009755http://dx.doi.org/10.1029/2007JD009755]
Mao F, Zhao M, Gong W, Chen L and Liang Z. 2020. Layer detection algorithm for CALIPSO observation based on automatic segmentation with a minimum cost function. Journal of Quantitative Spectroscopy and Radiative Transfer,261: 107498. [10.1016/J.JQSRT.2020.107498http://dx.doi.org/10.1016/J.JQSRT.2020.107498]
Marini A. 1998. ATLID: The Technology Development Programme for ESA's Satellite-borne Atmospheric Lidar. ESA bulletin, 1998(95): 113-118 [http://www.esa.int/esapub/bulletin/bullet95/MARINI.pdfhttp://www.esa.int/esapub/bulletin/bullet95/MARINI.pdf]
Marchand R, Haynes J, Gerald G M, Ackerman T and Stephens G. 2009. A comparison of simulated cloud radar output from the multiscale modeling framework global climate model with CloudSat cloud radar observations. Journal of Geophysical Research, 114: D00A20 [DOI: 10.1029/2008JD009790http://dx.doi.org/10.1029/2008JD009790]
Matrosov S V. 2011. CloudSat measurements of landfalling hurricanes Gustav and Ike (2008). Journal of Geophysical Research, 116: D01203. [DOI: 10.1029/2010JD014506http://dx.doi.org/10.1029/2010JD014506]
McGill M,Yorks J, Scott V, Kupchock A and Patrick S. 2015. The Cloud-Aerosol Transport System (CATS): a technology demonstration on the International Space Station. SPIE Optical Enineering and Applications, California, United States, 96120A [DOI: 10.1117/12.2190841]
Müller D, Hostetler C A, Ferrare R A, Burton S P, Chemyakin E, Kolgotin A, Hair J W, Cook A L, Harper D B and Rogers R R. 2014. Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US. Atmospheric Measurement Techniques, 7(10): 3487-3496 [DOI: 10.5194/amt-7-3487-2014http://dx.doi.org/10.5194/amt-7-3487-2014]
Min M, Wang P, Campbell J R, Zong X, and Li Y. 2010. Midlatitude cirrus cloud radiative forcing over China. Journal of Geophysical Research, 115: D20210 [DOI: 10.1029/2010JD014161http://dx.doi.org/10.1029/2010JD014161]
Mu Y J, Wan Y, Liu J Q, Hou X and Chen W B. 2018. Optomechanical analysis and optimization of spaceborne lidar telescope primary mirror. Infrared and laser engineering, 47(07): 203-209
穆永吉, 万渊, 刘继桥, 侯霞, 陈卫标. 2018. 星载激光雷达望远镜主镜光机分析与优化. 红外与激光工程, 47(07): 203-209[DOI: 10.3788/irla201847.0718002http://dx.doi.org/10.3788/irla201847.0718002]
Nam C C W and Quaas J. 2012. Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data. Journal of Climate, 25(14): 4975-4992 [DOI: 10.1175/JCLI-D-11-00347.1http://dx.doi.org/10.1175/JCLI-D-11-00347.1]
Nazaryan H, McCormick M P, and Menzel W P. 2008, Global characterization of cirrus clouds using CALIPSO data, Journal of Geophysical Research, 113: D16211 [DOI: 10.1029/2007JD009481http://dx.doi.org/10.1029/2007JD009481]
Norin L, Devasthale A, L'Ecuyer S T, Wood B N and Smalley M. 2015. Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden. Atmospheric Measurement Techniques, 8(12):5009-5021 [DOI: 10.5194/amtd-8-8157-2015http://dx.doi.org/10.5194/amtd-8-8157-2015]
Pan H L, Bu L B, Kumar K R, Gao H Y, Huang X Y and Zhang W T. 2017. A new retrieval method for the ice water content of cirrus using data from the CloudSat and CALIPSO. Journal of Atmospheric and Solar-Terrestrial Physics, 161:134-142 [DOI: 10.1016/j.jastp.2017.07.001http://dx.doi.org/10.1016/j.jastp.2017.07.001]
Parkinson C L. 2003. Aqua: An Earth-observing satellite mission to examine water and other climate variables. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 173-183 [DOI: 10.1109/TGRS.2002.808319http://dx.doi.org/10.1109/TGRS.2002.808319]
Partick G and Blanchet J P. 2010. Investigation of the sulphate-induced freezing inhibition effect from CloudSat and CALIPSO measurements. Journal of Geophysical Research, 115: D22205 [DOI: 10.1029/2010JD013905http://dx.doi.org/10.1029/2010JD013905]
Pitts M, Poole L and Thomason L. 2009. CALIPSO polar stratospheric cloud observations: Second-generation detection algorithm and composition discrimination. Atmospheric Chemistry and Physics, 9: 7577-7589 [DOI: 10.5194/acp-9-7577-2009http://dx.doi.org/10.5194/acp-9-7577-2009]
Qiu J H, Li D R, Chen H B, Wang G C and Shi G Y. 2003. Modern research progress in atmospheric physics. Chinese Journal of Atmospheric Science, 27(4): 628-652
邱金桓，吕达仁，陈洪滨，王庚辰，石广玉. 现代大气物理学进展. 大气科学，27(4): 628-652 [DOI: 10.3878/j.issn.1006-9895.2003.04.14http://dx.doi.org/10.3878/j.issn.1006-9895.2003.04.14]
Qiu J W, Xia H Y, Dou X K, Li M Y, Shang G M, Wang C. Shang X, Liu S F and Liu J J. 2017. Micro-pulse polarization lidar at 1.5 Jim using a single superconducting nanowire single photon detector, Optics Letters, 42(21): 4454-4457 [DOI: 10.1364/OL.42.004454http://dx.doi.org/10.1364/OL.42.004454]
Qiu Y J and Wang H A. 2017. The vertical distribution of cloud properties in two regions of northern China based on CloudSat/CALIPSO data. Transactions of Atmospheric Sciences, 40(4)：553-561
邱玉珺，王宏奥. 2017. 基于CloudSat/CALIPSO资料的我国北方2个区域云垂直分布差异研究. 大气科学学报，40(4)：553-561 [DOI: 10. 13878/j.cnki.dqkxxb.20160517002http://dx.doi.org/10.13878/j.cnki.dqkxxb.20160517002]
Randall D, Khairoutdinov M, Arakawa A and Grabowski W. 2003. Breaking the cloud parameterization deadlock. Bulletin of the American Meteorological Society, 84(11): 1547-1564. [DOI: 101175/BAMS-84-11-1547http://dx.doi.org/101175/BAMS-84-11-1547]
Sassen K and Wang Z. 2008. Classifying clouds around the globe with the CloudSat radar: 1-year of results. Geophysical Research Letters, 35:L04805 [DOI: 10.1029/2007GL032591http://dx.doi.org/10.1029/2007GL032591]
Shang J, Yang H, Yin H G, Wu Q and Guo Y. 2012. First results from field campaign of spaceborne precipitation radar in China: radar performance analysis. Journal of Remote Sensing, 16(3): 435-447 [DOI: CNKI:SUN:YGXB.0.2012-03-002http://dx.doi.org/CNKI:SUN:YGXB.0.2012-03-002]
Shao J F, Hua D X, Wang L, Wang D and Pan R. 2020. Development of Ultraviolet Dual-Wavelength Lidar and Analysis of Its Signal-to-Noise Ratio. Acta Optica Sinica, 40(12): 33-41.
邵江锋, 华灯鑫, 汪丽, 王东, 潘睿. 紫外双波长激光雷达系统研制与信噪比分析. 2020. 光学学报, 40(12): 33-41 [DOI: 10.3788/AOS202040.1201004http://dx.doi.org/10.3788/AOS202040.1201004]
Shang J, Wu Q, Dou F L and An D W. 2018. Requirements index analysis and demonstration of spaceborne terahertz cloud measuring radar. Meteorological and Environmental Science, 41(1):125-131
商建，吴琼，窦芳丽，安大伟. 2018. 天基太赫兹云雷达需求指标分析与论证. 气象与环境科学，41(1):125-131 [DOI: 10.16765/j.cnki.1673-7148.2018.01/016http://dx.doi.org/10.16765/j.cnki.1673-7148.2018.01/016]
Sekiyama T T, Tanaka T Y, Shimizu A and Miyoshi T. 2010. Data assimilation of CALIPSO aerosol observations. Atmospheric Chemistry and Physics, 10(1): 39-49 [DOI: 10.5194/acp-10-39-2010http://dx.doi.org/10.5194/acp-10-39-2010]
Skofronick-Jackson G, Kirschbaum D, Petersen W, Huffman G, Kidd C, Stocker E and Kakar R. 2018. The global precipitation measurement (GPM) mission’s scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations. Quarterly Journal of the Royal Meteorological Society, 144(1)：27-48 [DOI: 10.1002/qj.3313http://dx.doi.org/10.1002/qj.3313]
Song C B and Zhao Y M. 2017. Development status and direction of spaceborne Lidar and radar for cloud and aerosol remote sensing. Journal of Telemetry, Tracking and Command, 38(6): 10-16
宋长波, 赵一鸣. 2017. 星载云、气溶胶遥感雷达技术现状与发展趋势. 遥测遥控，38(6): 10-16 [DOI: 10.13435/j.cnki.ttc.002894http://dx.doi.org/10.13435/j.cnki.ttc.002894]
Stephens G L, Vane D G, Boain R J, Mace G G, Sassen K, Wang Z, Illingworth A J, O'connor E J, Rossow W B, Durden S L, Miller S D, Austin R T, Benedetti A, Mitrescu C and the CloudSat Science Team. 2002. The CloudSat mission and the A-Train. Bulletin of the American Meteorological Society, 83(12): 1771-1790 [DOI: 10.1175/BAMS-83-12-1771http://dx.doi.org/10.1175/BAMS-83-12-1771]
Stephens G L. 2005. Cloud feedbacks in the climate system: a critical review. Journal of Climate, 18(2): 237-273. [DOI: 10.1175/JCLI-3243.1http://dx.doi.org/10.1175/JCLI-3243.1]
Stephens G L, Vane D G, Tanelli S, Im E and Suzuki K. 2008. CloudSat mission: performance and early science after the first year of operation. Journal of Geophysical Research, 113: D00A18. [DOI: 10.1029/2008JD009982http://dx.doi.org/10.1029/2008JD009982]
Stephens G, Winker D M, Pelon J, Trepte C, Vane D, Yuhas C, L’Ecuyer T and Lebsock M. 2018. CloudSat and CALIPSO within the A-Train: Ten years of actively observing the Earth system. Bulletin of the American Meteorological Society, 99(3): 569-581 [DOI: 10.1175/BAMS-D-16-0324.1http://dx.doi.org/10.1175/BAMS-D-16-0324.1]
Takahashi H, Suzuki K and Stephens G. 2017. Land-ocean differences in the warm-rain formation process in satellite and ground-based observations and model simulations. Quarterly Journal of the Royal Meteorological Society, 143(705): 1804-1815 [DOI: 10.1002/qj.3042http://dx.doi.org/10.1002/qj.3042]
Tanelli S, Durden S L, Im E, Pak K S, Reinke D G, Partain P, Haynes J M and Marchand R T. 2008. CloudSat’s cloud profiling radar after two years in orbit: performance, calibration, and processing. IEEE Transactions on Geoscience and Remote Sensing, 46(11): 3560-3573 [DOI: 10.1109/TGRS.2008.2002030http://dx.doi.org/10.1109/TGRS.2008.2002030]
Tang Y H, Zhou Y Q, Cai M and Ma Q R. 2020. Global distribution of clouds based on CloudSat and CALIPSO combined observations. Transactions of Atmospheric Sciences, 43(5): 917-931
唐雅慧，周毓荃，蔡淼，马茜蓉. 2020. 基于CloudSat与CALIPSO联合观测研究全球云分布特征. 大气科学学报，43(5): 917-931 [DOI: 10.13878/j.cnki.dqkxxb.20180104001http://dx.doi.org/10.13878/j.cnki.dqkxxb.20180104001]
Tian X M, Liu D, Xu J W, Wang Z Z, Wang B X, Wu D C, Zhong Z Q, Xie C B and Wang Y J. 2018. Review of Lidar Technology for Atmosphere Monitoring. Journal of atmospheric and environmental optics, 13(05): 321-341
田晓敏, 刘东, 徐继伟, 王珍珠, 王邦新, 吴德成, 钟志庆, 谢晨波, 王英俭. 2018. 大气探测激光雷达技术综述. 大气与环境光学学报, 13(05): 401-416 [DOI: 10.3969/j.issn.1673-6141.2018.05.001http://dx.doi.org/10.3969/j.issn.1673-6141.2018.05.001]
Tomiyama N, Tomita E, Furukawa K, Nakatsuka H, Seki Y, Aida Y, Okada K, Maruyama K, Ishii Y, Ohno Y, Horie, H and Satoh K. 2020. EarthCARE/CPR development status and performance. Conference: Remote Sensing of Clouds and the Atmosphere XXV [DOI: 10.1117/12.2574244http://dx.doi.org/10.1117/12.2574244]
Vernier J P, Thomason L W, and Kar J. 2011. CALIPSO detection of an Asian tropopause aerosol layer. Geophysical Research Letters, 38(7): 1-6 [DOI: 10.1029/2010GL046614http://dx.doi.org/10.1029/2010GL046614]
Várnai T and Marshak A. 2010. Global CALIPSO observations of aerosol changes near clouds. IEEE Geoscience and Remote Sensing Letters, 8(1): 19-23 [DOI: 10.1109/LGRS.2010.2049982http://dx.doi.org/10.1109/LGRS.2010.2049982]
Wang F, Guo J P, Zhang J H, Huang J F, Min M, Chen T M, Liu H, Deng M J and Li X W. 2015. Multi-sensor quantification of aerosol-induced variability in warm clouds over eastern China. Atmospheric Environment, 113:1-9 [DOI: 10.1016/j.atmosenv.2015.04.063http://dx.doi.org/10.1016/j.atmosenv.2015.04.063]
Wang H, Luo Y L and Zhang R H. 2011. Analyzing seasonal variation of clouds over the Asian monsoon regions and the Tibetan Plateau region using CloudSat/CAPLISO data. Chinese Journal of Atmospheric Sciences, 35(6)：1117-1131
汪会，罗亚丽，张人禾. 2011. 用CloudSat/CALIPSO资料分析亚洲季风区和青藏高原地区云的季节变化特征.大气科学，35(6)：1117-1131 [DOI: 10.3878/j.issn.1006-9895.2011.06.11http://dx.doi.org/10.3878/j.issn.1006-9895.2011.06.11]
Wang P and Wang H T. 2017. Discussion of spaceborne Terahertz active cloud profiling radar. Journal of Terahertz Science and Electronic Information Technology, 15(5): 733-739
王平，王海涛. 2017. 星载太赫兹主动云雷达探讨. 太赫兹科学与电子信息学报，15(5): 733-739 [DOI: 10.11805/TKYDA201705.0733http://dx.doi.org/10.11805/TKYDA201705.0733]
Wang S H, Han Z G, Yao Z G and Zhao Z L. 2011. An analysis of cloud types and macroscopic characteristics over China and its neighborhood based on the CloudSat data. Acta Meteorologica Sinica, 69(5): 883-899.
王帅辉，韩志刚，姚志刚，赵增亮. 2011. 基于CloudSat资料的中国及周边地区各类云的宏观特征分析. 气象学报，69(5):883-899 [DOI: 10.11676/qxxb2011.077http://dx.doi.org/10.11676/qxxb2011.077]
Wang T, Fetzer E J, Wong S, Kahn B H and Yue Q. 2016. Validation of MODIS cloud mask and multilayer flag using CloudSat‐CALIPSO cloud profiles and a cross‐reference of their cloud classifications. Journal of Geophysical Research, 121(19): 11-620 [DOI: 10.1002/2016JD025239http://dx.doi.org/10.1002/2016JD025239]
Weisz E, Li J, Menzel W P, Heidinger A K, Kahn B H and Liu C Y. 2007. Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals. Geophysical Research Letters, 34(17): L17811 [DOI: 10.1029/2007GL030676http://dx.doi.org/10.1029/2007GL030676]
Winker D M, Couch R H and McCormick M P. 1996. An overview of LITE: NASA's Lidar In-space Technology Experiment. Proceedings of the IEEE, 164-180 [DOI: 10.1109/5.482227http://dx.doi.org/10.1109/5.482227]
Winker D M, Vaughan M A, Omar A, Hu Y, Powell K A, Liu Z, Hunt W H and Young S A. 2009. Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology 26(11): 2310-2323 [DOI: 10.1175/2009JTECHA1281.1http://dx.doi.org/10.1175/2009JTECHA1281.1]
Winker D M, Pelon J, Coakley J A, Ackerman S A, Charlson R J, Colarco P R, Flamant P, Fu Q, Hoff R M, Kittaka C, Kubar T L, Treut H L, Mccormick M P, Megie G, Poole L, Powell K, Trepte C, Vaughan M A and Wielicki B A. 2010. The CALIPSO mission: a global 3D view of aerosols and clouds. Bulletin of the American Meteorological Society, 91(9):1211-1229 [DOI: 10.1175/2010BAMS3009.1http://dx.doi.org/10.1175/2010BAMS3009.1]
Wu J X, Dou F L, An D W, Gu Y, Zhou Q and Liu W. 2019. Sensitivity of dual wavelength reflectivity ratio of 94/220 GHz spaceborne radar to cloud parameters with non-spherical ice crystals. Acta Meteorologica Sinica, 77(3): 529-540
吴举秀，窦芳丽，安大伟，顾瑜，周青，刘伟. 2019. 94/220 GHz星载雷达双波长比对非球形冰晶云参数敏感性分析. 气象学报，77(3): 529-540 [DOI: 10.11676/qxxb2019.016http://dx.doi.org/10.11676/qxxb2019.016]
Wu Q, Yang M L, Dou F L, Guo Y and An D W. 2018. A study of cloud parameters retrieval algorithm for spaceborne millimeter wavelength cloud radar. Acta Meteorologica Sinica, 76(1): 160-168
吴琼，仰美霖，窦芳丽，郭杨，安大伟. 2018. 星载双频云雷达的云微物理参数反演算法研究. 气象学报，76(1): 160-168 [DOI: 10.11676/qxxb2017.07http://dx.doi.org/10.11676/qxxb2017.07]
Xie Y Y, Liu J Q, Jiang J X and Chen W B. 2014. Wavelengths optimization to decrease error for a space-borne lidar measuring CO2 concentration. Infrared and laser engineering, 43(1): 88-93
谢杨易, 刘继桥, 姜佳欣, 陈卫标. 2014. 使CO2 浓度测量误差减小的星载激光雷达波长优化. 红外与激光工程, 43(1): 88-93 [DOI: 10.3788/IRLA201847.0718002http://dx.doi.org/10.3788/IRLA201847.0718002]
Xiong X, Angal A, Wu A, Barnes W and Salomonson V. 2016. Terra and Aqua MODIS instrument performance. Geoscience and Remote Sensing Symposium of IEEE, 7388-7391. [DOI: 10.1109/IGARSS.2016.7730927http://dx.doi.org/10.1109/IGARSS.2016.7730927]
Ｘu J J, Bu L B, Liu J Q, Zhang Y, Zhu S Z, Wang Q, Zhu X P and Chen W B. 2020. Airborne High-Spectral-Resolution Lidar for Atmospheric Aerosol Detection. Chinese Journal of lasers, 47(7): 411-420
徐俊杰, 卜令兵, 刘继桥, 张扬, 朱首正, 王勤, 竹孝鹏, 陈卫标. 2020. 机载高光谱分辨率激光雷达探测大气气溶胶的研究. 中国激光, 47(7): 411-420 [DOI: 10.3788/CJL202047.0710003http://dx.doi.org/10.3788/CJL202047.0710003]
Yan W, Yang H L and Zhou XW. 2008. A-train satellite formation and its application to cloud research. Remote Sensing Information, 2: 93-96
严卫，杨汉乐，周兴旺. 2008. A-Train卫星编队及其在云研究领域中的应用. 遥感信息，2: 93-96 [DOI: 10.3969/j.issn.1000-3177.2008.02.020http://dx.doi.org/10.3969/j.issn.1000-3177.2008.02.020]
Yan W, Ren J Q, Lu W and Wu X. 2011. Cloud phase discrimination technology based on spaceborne millimeter wave radar and lidar data. Journal of Infrared & Millimeter Waves, 30(1): 68-3
严卫，任建奇，陆文，吴限. 2011. 联合星载毫米波雷达和激光雷达资料的云相态识别技术. 红外与毫米波雷达，30(1): 68-3 [DOI: 10.3724/SP.J.1010.2011.00068http://dx.doi.org/10.3724/SP.J.1010.2011.00068]
Yang B Y, Wu X J and Guo Z. 2017. The characteristics of cloud properties in deep convective clouds across China with the CloudSat dataset. Plateau Meteorology, 36(6): 1655-1664
杨冰韵，吴晓京，郭徵. 2017. 基于CloudSat资料的中国地区深对流云物理特征研究. 高原气象，36(6): 1655-1664 [DOI: 10.7522/j.issn.1000-0534.2017.0006http://dx.doi.org/10.7522/j.issn.1000-0534.2017.0006]
Yu L, Fu Y F, Yang Y, Li R, Qiu X and Cai H. 2018. Assessment of longwave radiative effect of nighttime cirrus based on CloudSat and CALIPSO measurements and single-column radiative transfer simulations. Journal of Quantitative Spectroscopy and Radiative Transfer, 221: 87-97 [DOI: 10.1016/j.jqsrt.2018.09.019http://dx.doi.org/10.1016/j.jqsrt.2018.09.019]
Yu C R, Liu Z S, Bi D C, Li Z G and Liu B Y. 2013. Comparison of simulated performance of filters in space-borne wind lidar system. Chinese journal of quantum electronics, 30(5): 615-620
于翠荣, 刘智深, 毕德仓, 李志刚, 刘秉义. 2013. 星载测风激光雷达系统鉴频器性能模拟比较. 量子电子学报, 30(5): 615-629 [DOI: 10.3969/j.issn.1007-5461. 2013.05.017http://dx.doi.org/10.3969/j.issn.1007-5461.2013.05.017]
Yu H, Chin M, Winker D M, Omar A H, Liu Z, Kittaka C and Diehl T. 2010. Global view of aerosol vertical distributions from CALIPSO lidar measurements and GOCART simulations: Regional and seasonal variations. Journal of Geophysical Research, 115: D00H30 [DOI: 10.1029/2009JD013364http://dx.doi.org/10.1029/2009JD013364]
Zhai X C, Marksteiner U, Weiler F, Lemmerz C, Lux O, Witschas B and Reitebuch O. 2020. Rayleigh wind retrieval for the ALADIN airborne demonstrator of the Aeolus mission using simulated response calibration. Atmospheric Measurement Techniques, 13(2):445-465 [DOI: 10.5194/amt-13-445-2020http://dx.doi.org/10.5194/amt-13-445-2020]
Zhang D A, Liu D B, Luo T A, Wang Z A and Yin Y C. 2015. Aerosol impacts on cloud thermodynamic phase change over East Asia observed with CALIPSO and CloudSat measurements. Journal of Geophysical Research, 120(4): 1490-1501 [DOI: 10.1002/2014JD022630http://dx.doi.org/10.1002/2014JD022630]
Zhang D M, Wang Z E and Liu D. 2010. A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements. Journal of Geophysical Research, 115: D00H13 [DOI: 10.1029/2009JD012143http://dx.doi.org/10.1029/2009JD012143]
Zhang H, Yang B Y, Peng J, Wang Z L and Jing X W. 2015. The Characteristics of cloud microphysical properties in East Asia with the CloudSat datasat. Chinese Journal of Atmospheric Sciences, 39(2):235-248
张华，杨冰韵，彭杰，王志文，荆现文. 2015. 东亚地区云微物理量分布特征的CloudSat卫星观测研究. 大气科学，39(2):235-248 [DOI: 10.3878/j.issn.1006-9895http://dx.doi.org/10.3878/j.issn.1006-9895]
Zhao Y F, Wang D H and Yin J F. 2014. A study on cloud microphysical characteristics over the Tibetan plateau using CloudSat data. Journal of Tropical Meteorology, 30(2): 239-248
赵艳风，王东海，尹金方. 2014. 基于CloudSat资料的青藏高原地区云微物理特征分析. 热带气象学报，30(2): 239-248 [DOI: 10.3969/j.issn.1004-4965.2014.02.005http://dx.doi.org/10.3969/j.issn.1004-4965.2014.02.005]
Zheng J Y, Liu D, Wang Z E, Tian X M, Wang Y J and Xie C B. 2018. Global distribution and seasonal variation of clouds observed from CloudSat/CALIPSO. Acta Meteorologica Sinica, 76(3): 420-433
郑建宇, 刘东, 王志恩, 田晓敏, 王英俭, 谢晨波. 2018. CloudSat/CALIPSO 卫星资料分析云的全球分布及其季节变化特征. 气象学报, 76(3): 420-433 [DOI: 10.11676/qxxb2017.094http://dx.doi.org/10.11676/qxxb2017.094]
Zhong L Z, Liu L P and Ge R S. 2009. Characteristics about the millimeter-wavelength radar and its status and prospect in and abroad. Advances in Earth Science, 24(4)：383-391
仲凌志，刘黎平，葛润生. 2009. 毫米波测云雷达的特点及其研究现状与展望. 地球科学进展，24(4)：383-391 [DOI: 10.3321/j.issn:1001-8166.2009.04.004http://dx.doi.org/10.3321/j.issn:1001-8166.2009.04.004]
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