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Assessment on Main Kinds of Satellite Cloud Climate Datasets
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摘要: 自20世纪70年代气象卫星进入业务化观测以来，气象卫星已提供了40余年的观测数据。长时间序列的卫星数据为云气候研究提供了可能。基于长时间序列的卫星数据，构建云气候数据集会涉及诸如定标、反演算法、反演数据精度验证等方面。目前国际上也已生成了一系列的云气候数据集，如ISCCP，Patmos-x，CLARA和MODIS-ST等，这些数据集所选用的探测数据、反演算法不尽一致，数据集产品的时空属性各异。如何发挥极轨和静止气象卫星各自优势，融合两类卫星数据，形成高时间分辨率、质量稳定的长时间序列云气候数据集是未来需要解决的问题。Abstract: Cloud is not only a key parameter that affects the radiation balance between earth and atmosphere system, but also is an important index to research atmosphere cycle and climate change. Cloud information can be achieved by surface observation, but it is limited by the spatial and temporal distribution of stations. Only satellite observations can provide a continuous synoptic survey of the state of the atmosphere over the entire globe, and satellite remote sensing also has advantages in observation area and time frequency. The operational weather satellite sensors can supply data records as long as more than 40 years, provide major support for cloud climate research. Whereas polar-orbiting cross-track scanning sensors generally only provide daily global coverage at particular local times, geostationary satellites are placed at particular longitudes along the equator and permit higher-frequency temporal sampling.Building the cloud climate dataset is related with some factors, such as recalibration, stable retrieval algorithm and validation. Based on long term satellite data, several cloud climate datasets, such as ISCCP, Patmos-x, CLARA, MODIS-ST, HIRS and so on are built selecting different instruments and different retrieval algorithm. Spatial and temporal resolutions of these cloud climate datasets are also different. Focusing on different properties of these cloud climate datasets including instruments and retrieval algorithm, references are cited to show the accuracy of these cloud climate datasets. Applications of these cloud climate data in weather and climate analysis are also introduced. As an example, the Tibetan Plateau is selected to analyze the difference between Patmos-x and CLARA-A2 that has the same satellite data source and high similar cloud detection algorithm. In long term, the changing trend is similar. The difference between these two cloud datasets is the spatial resolution:For Patmos-x, it's 0.1°, while for CLARA-A2, it's 0.25°. Compared with CLARA-A2, the Patmos-x has smaller cloud amount at day-time and has larger cloud amount at night-time. Based on NOAA-18 and Aqua data, Patmos-x, CLARA-A2 and Aqua/MODIS cloud amount during 2005 and 2015 are compared. Results show that the difference between three kinds of cloud amount is smaller in day-time, especially in summer. The difference increases in night-time, especially in winter and spring. The main cause may come from different observation ability, retrieval algorithm and observation time of different satellites.
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Key words:
- satellite;
- cloud;
- climate data
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图 2 1982—2015年20°~40°N，73°~105°E区域内NOAA-07，NOAA-09，NOAA-11，NOAA-14，NOAA-16和NOAA-18卫星的CLARA-A1，CLARA-A2及Patmos-x月平均白天云量时序图
Fig. 2 Monthly mean cloud fraction at day-time from CLARA-A1, CLARA-A2 and Patmos-x by NOAA-07, NOAA-09, NOAA-11, NOAA-14, NOAA-16 and NOAA-18 over 20°-40°N, 73°-105°E during 1982-2015
图 3 1982—2015年20°~40°N，73°~105°E区域内NOAA-07，NOAA-09，NOAA-11，NOAA-14，NOAA-16和NOAA-18卫星的CLARA-A1，CLARA-A2及Patmos-x月平均夜间云量时序图
Fig. 3 Monthly mean cloud fraction at night-time from CLARA-A1, CLARA-A2 and Patmos-x by NOAA-07, NOAA-09, NOAA-11, NOAA-14, NOAA-16 and NOAA-18 over 20°-40°N, 73°-105°E during 1982-2015
图 4 2005—2015年20°~40°N，73°~105°E区域内CLARA-A2, Patmos-x的NOAA-18和Aqua/MODIS月平均白天云量时序图
Fig. 4 Monthly mean cloud fraction at day-time from CLARA-A2, Patmos-x by NOAA-18 and Aqua/MODIS over 20°-40°N, 73°-105°E during 1982-2015
图 5 2005—2015年20°~40°N，73°~105°E区域内CLARA-A2, Patmos-x的NOAA-18和Aqua/MODIS月平均夜间云量时序图
Fig. 5 Monthly mean cloud fraction at night-time from CLARA-A2, Patmos-x by NOAA-18 and Aqua/MODIS over 20°-40°N, 73°-105°E during 1982-2015
表 1 主要云数据集信息
Table 1 Some kinds of cloud climate dataset information
数据集名称 空间分辨率 时间分辨率 时间范围 主要数据源 ISCCP 2.5°×2.5°(C和D系列)
30 km×30 km(DX数据)3 h, 日, 月 1983—2009年 NOAA，GMS，GOES，METEOSAT Patmos-x 0.1°×0.1° 每日2次，月 1979年至今 NOAA，Metop CLARA-A1 0.25°×0.25° 日，月 1982—2009年 NOAA CLARA-A2 0.25°×0.25° 日，月 1982—2015年 NOAA，Metop MODIS-ST 1 km×1 km，5 km×5 km 每日2次，月 2000年至今
2003年至今EOS/Terra
EOS/AquaHIRS 月 1980—2015年 NOAA，Metop 
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[1] Di G, Menzies A, Zhao G, et al.MISR Level 3 Cloud Fraction by Altitude Algorithm Theoretical Basis.Jet Propulsion Laboratory Rep.JPL D-62358, 2010. [2] Goloub P, Herman M, Chepfer H, et al.Cloud thermodynamical phase classification from the POLDER spaceborne instrument.J Geophys Res, 2000, 105:14747-14759. doi: 10.1029/1999JD901183 [3] Fritz S, Wark D Q, Fleming H E, et al.Temperature Sounding from Satellites.NOAA Technical Report, 1972, NESS 59.US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite Service, Washington D C.1972. [4] Rodgers C D.Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation.Rev Geophys Space Phys, 1976, 14:609-624. doi: 10.1029/RG014i004p00609 [5] Houghton J T, Taylor F W, Rodgers C D.Remote Sounding of Atmospheres.Cambridge:Cambridge University Press, 1984. [6] Twomey S.An Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements.New York:Elsevier, 1977. [7] Scot N A, Chedin A, Armante R, et al.Characteristics of the TOVS Pathfinder Path-B dataset.Bull Amer Meteor Soc, 1999, 80:2679-2701. doi: 10.1175/1520-0477(1999)080<2679:COTTPP>2.0.CO;2 [8] O'Dell C W, Wentz F J, Bennartz R.Cloud liquid water path from satellite-based passive microwave observations:A new climatology over the global oceans.J Climate, 2008, 21:1721-1739. doi: 10.1175/2007JCLI1958.1 [9] Stephens G L, and Coauthors.The CloudSat mission and the A-Train.Bull Amer Meteor Soc, 2002, 83:1771-1790. doi: 10.1175/BAMS-83-12-1771 [10] Stubenrauch C J, Rossow W B, Kinne S, et al.Assessment of global cloud datasets from satellites.Bull Amer Meteor Soc, 2013, 6:1031-1049. [11] Schiffer R A, Rossow W B.The International Satellite Cloud Climatology Project (ISCCP):The first project of the World Climate Research Programme.Bull Amer Meteor Soc, 1983, 64:779-784. http://d.wanfangdata.com.cn/OAPaper/oai_doaj-articles_fd48456dce06470da8cf82dcdc3148fc [12] Hidinger A K, Foster M, Walther A, et al.The pathfinder atmospheres-extended AVHRR climate dataset.Bull Amer Meteor Soc, 2014, 7:909-922. http://connection.ebscohost.com/c/articles/97240863/pathfinder-atmospheres-extended-avhrr-climate-dataset [13] Karlsson K G, Riihelä A, Müller R, et al, CLARA-A1:A cloud, albedo, and radiation dataset from 28 yr of global AVHRR data.Atmos Chem Phys, 2013, 13:5351-5367. doi: 10.5194/acp-13-5351-2013 [14] Karlsson K G, Anttila K, Trentmann J, et al.CLARA-A2:The second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data.Atmos Chem Phys, 2017, 17:5809-5828. doi: 10.5194/acp-17-5809-2017 [15] Schiffer R A, Rossow W B.ISCCP global radiance data set:A new resource for climate research.Bull Amer Meteor Soc, 1985, 66:1498-1505. doi: 10.1175/1520-0477(1985)066<1498:IGRDSA>2.0.CO;2 [16] Rossow W B, Kinsella E, Wolf A, et al. International Satellite Cloud Climatology Project (ISCCP) Description of Reduced Resolution Radiance Data.WMO/TD 58(Revised), World Climate Research Program (ICSU and WMO), 1987. [17] Rossow W B, Schiffer R A.ISCCP cloud data products.Bull Amer Meteor Soc, 1991, 72:2-20. doi: 10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2 [18] Rossow W B, Schiffer R A.Advances in understanding clouds from ISCCP.Bull Amer Meteor Soc, 1999, 80(11):2261-2287. doi: 10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2 [19] Rossow W B, Walker A W, Gander L C.Comparison of ISCCP and other cloud amounts.J Climate, 1993, 6:2394-2418. doi: 10.1175/1520-0442(1993)006<2394:COIAOC>2.0.CO;2 [20] 魏丽, 钟强, 侯萍.中国大陆卫星反演云参数的评估.高原气象, 1996, 15(2):147-156. http://d.wanfangdata.com.cn/Thesis/Y1077918 [21] 翁笃鸣, 韩爱梅.我国卫星总云量与地面总云量分布的对比分析.应用气象学报, 1998, 9(1):32-37. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19980105&flag=1 [22] 刘洪利, 朱文琴, 直树华, 等.中国地区云的气候特征分析.气象学报, 2003, 61(4):466-473. doi: 10.11676/qxxb2003.045 [23] 丁守国, 赵春生, 石广玉, 等.近20年全球总云量变化趋势分析.应用气象学报, 2005, 16(5):670-676. doi: 10.11898/1001-7313.20050514 [24] 王旻燕, 王伯民.ISCCP产品和我国地面观测总云量差异.应用气象学报, 2009, 20(4):411-418. doi: 10.11898/1001-7313.20090404 [25] 刘瑞霞, 刘玉洁, 杜秉玉, 等.利用ISCCP资料分析青藏高原云气候特征.南京气象学院学报, 2002, 25(2):226-234. http://d.wanfangdata.com.cn/Periodical/njqxxyxb200202013 [26] 王可丽, 江灏, 陈世强, 等.青藏高原地区的总云量-地面观测、卫星反演和同化资料的对比分析.高原气象, 2001, 20(3):252-257. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gyqx200103004&dbname=CJFD&dbcode=CJFQ [27] 陈勇航, 黄建平, 王天河, 等.西北地区不同类型云的时空分布及其与降水的关系.应用气象学报, 2005, 16(6):717-727. doi: 10.11898/1001-7313.20050612 [28] 陈勇航, 陈艳, 黄建平, 等.中国西北地区云的分布及其变化趋势.高原气象, 2007, 26(4):741-748. http://d.wanfangdata.com.cn/Periodical/gyqx200704011 [29] 刘健.中国区域云特性分析及其在FY-2云检测中的应用.应用气象学报, 2009, 20(6):673-681. doi: 10.11898/1001-7313.20090604 [30] Heidinger A, Straka W C, Molling C C, et al.Deriving an inter sensor consistent calibration for the AVHRR solar reflectance data record.Int J Remote Sensing, 2010, 31:6493-6517. doi: 10.1080/01431161.2010.496472 [31] Heidinger A, Cao C, Sullivan J T. Using Moderate Resolution Imaging Spectrometer (MODIS) to calibrate Advanced Very High Resolution Radiometer reflectance channels.J Geophys Res, 2002, 107:4702. doi: 10.1029-2001JD002035/ [32] Zhao Y T, Heidinger A K, Knapp K R.Long-term trends of zonally averaged aerosol optical thickness observed from operational satellite AVHRR instrument.Meteor Appl, 2011, 18:440-445. doi: 10.1002/met.v18.4 [33] Cermak J, Wild M, Knutti R, et al.Consistency of global satellite-derived aerosol and cloud data sets with recent brightening observations.Geophys Res Lett, 2010, 37:L21704. doi: 10.1029%2F2010GL044632 [34] Rao C R N, Sullivan J T, Walton C C, et al.Nonlinearity Corrections for the Thermal Infrared Channels of the Advanced Very hIgh Resolution Radiometer:Assessment and Corrections.NOAA Tech Rep, NESDIS 69, 1993. [35] Heidinger A K, Evan A T, Foster M J, et al.A naive Bayesian cloud detection scheme derived from CALIPSO and applied within PATMOS-x.J Appl Meteor Climatol, 2012, 51:1129-1144. doi: 10.1175/JAMC-D-11-02.1 [36] Heidinger A K, Pavolonis M J.Gazing at cirrus clouds for 25 years through a split window.Part Ⅰ:Methodology.J Appl Meteor Climatol, 2009, 48:1100-1116. doi: 10.1175/2008JAMC1882.1 [37] Walther A, Heidinger A.Implementation of the daytime cloud optical and microphysical properties algorithm (DCOMP) in PATMOS-x.J Appl Meteor Climatol, 2012, 51:1371-1390. doi: 10.1175/JAMC-D-11-0108.1 [38] Heidinger A, Foster M, Botambekov D, et al.Using the NASA EOS A-train to probe the performance of the NOAA PATMOS-x cloud fraction CDR.Remote Sensing, 2016, 8:511-528. doi: 10.3390/rs8060511 [39] Foster M J, Heidinger A.Entering the Era of 30-year satellite cloud climatologies:A north American case study.J Climate, 2014, 27:6687-6697. doi: 10.1175/JCLI-D-14-00068.1 [40] 涂钢, 刘波, 于清波.PATMOS-X、ISCCP云量产品及地面观测在中国区域的对比分析.地理科学, 2014, 34(2):198-204. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=dlkx201402010&dbname=CJFD&dbcode=CJFQ [41] Karl-Göran K, Kati A, Jörg T, et al.CLARA-A2:The second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data.Atmos Chem Phys, 2017, 17:5809-5828. doi: 10.5194/acp-17-5809-2017 [42] Sun B M, FreeM, Yoo H Y, et al.Variability and trends in US cloud cover:ISCCP, Patmos-x and CLARA-A1 compared to homogeneity-adjusted weather observations.J Climate, 2015, 28:4373-4389. doi: 10.1175/JCLI-D-14-00805.1 [43] Ackerman S A, Strabala K I, Menzel W P, et al, Discriminating clear-sky from clouds with MODIS.J Geophys Res, 1998, 103(D24):32141-32157. doi: 10.1029/1998JD200032 [44] Frey R A, Ackerman S A.Cloud detection with MODIS.Part Ⅰ:Recent improvements in the MODIS cloud mask.J Atmos Oceanic Technol, 2008, 25:1057-1072. doi: 10.1175/2008JTECHA1052.1 [45] Menzel W P, and Coauthors.MODIS global cloud-top pressure and amount estimation:Algorithm description and results.J Appl Meteor Climatol, 2008, 47:1175-1198. doi: 10.1175/2007JAMC1705.1 [46] Platnick S, King M D, Ackerman S A, et al.The MODIS cloud products:Algorithms and examples from Terra.IEEE Trans Geosci Remote Sens, 2003, 41:459-473. doi: 10.1109/TGRS.2002.808301 [47] Minnis P, Szedung S M, Yan C.CERES edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data.Part Ⅰ:Algorithms.IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11):4374-4400. doi: 10.1109/TGRS.2011.2144601 [48] Minnis P, Szedung S M, Yan C, et al.CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data.Part Ⅱ:Examples of average results and comparisons with other data.IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11):4401-4430. doi: 10.1109/TGRS.2011.2144602 [49] Kotarba A Z.A comparison of MODIS-derived cloud amount with visual surface observations.Atmospheric Research, 2009, 92:522-530. doi: 10.1016/j.atmosres.2009.02.001 [50] 曹芸, 何永健, 邱新法, 等.基于地面观测资料的MODIS云量产品订正.遥感学报, 2012, 16(2):325-342. doi: 10.11834/jrs.2012368 [51] 刘瑞霞, 陈洪滨, 郑照军, 等.总云量产品在中国区域的分析检验.应用气象学报, 2009, 20(5):571-578. doi: 10.11898/1001-7313.20090508 [52] 陈勇航, 毛晓琴, 黄建平, 等.西北典型地域条件下云量的对比分析.气候与环境研究, 2009, 14(1):77-84. http://d.wanfangdata.com.cn/Periodical/qhyhjyj200901009 [53] 段皎, 刘煜.中国地区云光学厚度和云滴有效半径变化趋势.气象科技, 2011, 39(4):408-416. http://d.wanfangdata.com.cn/Periodical/qxkj201104004 [54] 吴晓, 游然, 王旻燕, 等.基于MODIS云宏微观特性的卫星云分类方法.应用气象学报, 2016, 27(2):201-208. doi: 10.11898/1001-7313.20160208 [55] 刘健.利用卫星数据分析青藏高原云微物理特性.高原气象, 2015, 32(1):38-45. http://d.wanfangdata.com.cn/Periodical/gyqx201301005 [56] Wylie D P, Menzel W P.Eight years of high cloud statistics using HIRS.J Climate, 1999, 12:170-184. doi: 10.1175/1520-0442-12.1.170 [57] Wylie D P, Jackson D L, Menzel W P, et al.Trends in global cloud cover in two decades of HIRS observations.J Climate, 2005, 18:3021-3031. doi: 10.1175/JCLI3461.1 [58] Cao C, Goldberg M, Wang L.Spectral bias estimation of historical HIRS using IASI observations for improved fundamental climate data records.J Atmos Oceanic Technol, 2009, 26:1378-1387. doi: 10.1175/2009JTECHA1235.1 [59] Chen R, Cao C.Physical analysis and recalibration of MetOp HIRS using IASI for cloud studies.J Geophys Res, 2012, 117:D03103. http://adsabs.harvard.edu/abs/2012JGRD..117.3103C [60] Chen R, Cao C, Menzel W P.Intersatellite calibration of NOAA HIRS CO2 channels for climate studies.J Geophys Res, 2013, 118:5190-5203. doi: 10.1002/jgra.50449 [61] Nagle F W, Holz R E.Computationally efficient methods of collocating satellite, aircraft, and ground observations.J Atmos Oceanic Technol, 2009, 26:1585-1595. doi: 10.1175/2008JTECHA1189.1 [62] Menzel W P, and Coauthors.MODIS global cloud-top pressure and amount estimation:Algorithm description and results.J Appl Meteor Climatol, 2008, 47:1175-1198. doi: 10.1175/2007JAMC1705.1 [63] Baum B A, Menzel W P, Frey R A, et al.MODIS cloudtop property refinements for collection 6.J Appl Meteor Climatol, 2012, 51:1145-1163. doi: 10.1175/JAMC-D-11-0203.1 [64] Menzel W P, Frey R A, Borbas E E, et al.Reprocessing of HIRS satellite measurements from 1980 to 2015:Development toward a consistent decadal cloud record.J Appl Meteor Climate, 2016, 55:2397-2410. doi: 10.1175/JAMC-D-16-0129.1 [65] 刘健, 张里阳.气象卫星高空间分辨率数据的云量计算与检验.应用气象学报, 2011, 22(1):35-45. doi: 10.11898/1001-7313.20110104 [66] 刘健, 杨晓峰, 崔鹏.NOAA卫星2007年总云量数据精度评估.高原气象, 2016, 35(4):1027-1038. http://d.wanfangdata.com.cn/Periodical/gyqx201604017 -
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