Ice clouds are crucial to the Earth's radiation balance. Two active sensors, the CloudSat radar and the CALIPSO lidar, provide the opportunity to detect ice clouds over the global region. Data DARDAR (liDAR/raDAR) combines these two active sensors advantages to derive ice cloud products, which make obtain optical thin and thick ice cloud vertical properties possible. Based on the data product DARDAR from January 2013 to December 2016, the ice cloud occurrence frequency, horizontal and vertical distribution, seasonal variation and microphysical properties of various optical depth ice clouds in China were analyzed. The results show that the occurrence frequency of ice clouds is 52% over the last four years, which is higher in spring and summer than in autumn and winter and the occurrence height is mainly between 5 and 10 km. The mean ice cloud optical depth, ice water path, and effective radius of China are approximately 4, 157 g·m-2 and 51 μm, respectively. The properties of ice cloud in China not only have obvious regional and seasonal variation characteristics, but also related to the type of ice cloud defined by different optical depth τ values. Optically thin ice clouds (0.03 < τ < 0.3) and opaque ice clouds (0.3 < τ < 3) are the most frequently observed in China which account for approximately 65% of all ice cloud samples and the high concentration area is in the Qinghai-Tibet Plateau. Except for the subvisual ice clouds (τ < 0.03), the main occurrence height of other types of ice clouds decreases with the increase of optical depth. Seasonal changes of different types of ice clouds are not obvious except for thicker ice clouds (τ>20). The more thick ice clouds with τ>3 are more occurred in summer while the thin ice cloud with τ < 3 are more occurred in winter. In terms of microphysical properties, the ice water content (IWC) are changing with the variation of optical depth. The Probability Distribution Function (PDF) of subvisual ice clouds accumulate with the IWC distribution. However, the range of PDF increases with optical depth increasing and both of the IWC distribution range and mode value increase. The relationship between the effective particle radius and height of the ice cloud is more closely related to the optical depth. According to the vertical frequency distribution analysis, all types of ice cloud effective radius increases with height decreasing significantly, optically thinner ones (τ < 3) frequency distribution almost unanimously, while thicker ones (τ>3) effective radius is generally larger.
CHEN Wenfeng
,
ZHENG Youfei
,
WANG Liwen
,
ZHENG Qian
,
LIN Tong
. Properties of Ice Clouds under Different Optical Depth over China based on DARDAR Data[J]. Plateau Meteorology, 2019
, 38(6)
: 1309
-1319
.
DOI: 10.7522/j.issn.1000-0534.2019.00051
[1]Brown P R A, Francis P N, 1995. Improved measurements of the ice water content in cirrus using a total-water probe[J]. Journal of Atmospheric and Oceanic Technology, 12(2):410-414.
[2]Chen T, Rossow W B, Zhang Y, 2000. Radiative effects of cloud-type variations[J]. Journal of Climate, 13(1):264-286.
[3]Delano? J, Hogan R J, 2008. A variational scheme for retrieving ice cloud properties from combined radar, lidar, and infrared radiometer[J]. Journal of Geophysical Research:Atmospheres, 113:1829-1836.
[4]Delano? J, Hogan R J, 2010. Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds[J]. Journal of Geophysical Research, 115(4):1307-1314.
[5]Eliasson S, Buehler S A, Milz M, et al, 2011. Assessing observed and modelled spatial distributions of ice water path using satellite data[J]. Atmospheric Chemistry and Physics, 11(1):375-391.
[6]Foot J S, 1988. Some observations of the optical properties of clouds. Ⅱ:Cirrus[J]. Quarterly Journal of the Royal Meteorological Society, 114(479):145-164.
[7]Francis P N, Hignett P, Macke A, 1998. The retrieval of cirrus cloud properties from aircraft multi-spectral reflectance measurements during EUCREX'93[J]. Quarterly Journal of the Royal Meteorological Society, 124(548):1273-1291.
[8]Haynes J M, Vonder Haar T H, L'Ecuyer T, et al, 2013. Radiative heating characteristics of Earth's cloudy atmosphere from vertically resolved active sensors[J]. Geophysical Research Letters, 40(3):624-630.
[9]Hogan R J, 2006. Fast approximate calculation of multiply scattered lidar returns[J]. Applied Optics, 45(23):5984-5992.
[10]Hong Y, Liu G, 2015. The characteristics of ice cloud properties derived from cloudsat and CALIPSO Measurements[J]. Journal of Climate, 28(9):3880-3901.
[11]Hong Y, Liu G, Li J L F, 2016. Assessing the radiative effects of global ice clouds based on cloudSat and CALIPSO Measurements[J]. Journal of Climate, 29(21):7651-7674.
[12]LiouK, 1986. Influence of cirrus clouds on weather and climate processes:a global perspective[J]. Monthly Weather Review, 114(6):1167-1199.
[13]Matus A V, L'Ecuyer T S, 2017. The role of cloud phase in Earth's radiation budget[J]. Journal of Geophysical Research:Atmospheres, 122(5):2559-2578.
[14]Sassen K, Cho B S, 1992. Subvisual-thin cirrus lidar dataset for satellite verification and climatological research[J]. Journal of Applied Meteorology, 1992(31):1275-1285.
[15]Sassen K, Wang Z, 2008. Classifying clouds around the globe with the CloudSat radar:1-year of results[J]. Geophysical Research Letters, 35(4):228-236.
[16]Stephens G L, 2005.Cloud feedbacks in the climate system:A critical review[J]. Journal of Climate, 18(2):237-273.
[17]单坤玲, 刘新波, 卜令兵, 等, 2015.激光雷达和毫米波雷达的卷云微物理特性的联合反演方法[J].红外与激光工程, 44(9):2742-2746. DOI:10.3969/j.issn.1007-2276.2015.09.034.
[18]段皎, 刘煜, 2011.中国地区云光学厚度和云滴有效半径变化趋势[J].气象科技(4):408-416. DOI:10.3969/j.issn.1671-6345.2011.04.004.
[19]霍娟, 2015.基于CloudSat/CALIPSO资料的海陆上空中云的物理属性分析[J].气候与环境研究, 20(1):30-40. DOI:10.3878/j.issn.1006-9585.2014.13188.
[20]李积明, 2011.结合主动和被动卫星遥感资料研究云的垂直分布特征[D].兰州: 兰州大学.
[21]李特, 郑有飞, 王立稳, 等, 2017.基于MODIS产品的中国陆地冰云季节变化特征[J].应用气象学报, 28(6):724-736.
[22]林彤, 郑有飞, 李特, 等, 2018.基于卫星资料的中国西北地区冰云特征分析[J].高原气象, 37(4):1051-1060. DOI:10.7522/j.issn.1000-0534.2017.00088.
[23]刘健, 王锡津, 2017.主要卫星云气候数据集评述[J].应用气象学报, (6):16-27.
[24]刘玉芝, 石广玉, 赵剑琦, 2007.一维辐射对流模式对云辐射强迫的数值模拟研究[J].大气科学, 31(3):486-494. DOI:10.3878/j.issn.1006-9895.2007.03.12.
[25]芦荀, 2017.云微物理参数的联合反演及其辐射效应的研究[D].南京: 南京信息工程大学.
[26]闵敏, 王普才, 宗雪梅, 2011.中国地区卷云分布特征的星载激光雷达遥感[J].气候与环境研究(3):301-309. DOI:10.3878/j.issn.1006-9585.2011.03.05.
[27]孙治安, 1996.混合云在GCM气候模拟中的重要性[J].应用气象学报, 7(4):452-459.
[28]王帅辉, 韩志刚, 姚志刚, 等, 2011.基于CloudSat资料的中国及周边地区各类云的宏观特征分析[J].气象学报, 69(5):883-899.
[29]位晶, 段克勤, 2018.基于卫星资料的秦岭南北云系及其垂直结构特征[J].高原气象, 37(3):777-785. DOI:10.7522/j.issn.1000-0534.2018.00057.
[30]薛小宁, 邓小波, 刘贵华, 2018.基于卫星资料的青藏高原卷云特性研究[J].高原气象, 37(2):505-513. DOI:10.7522/j.issn.1000-0534.2017.00047.
[31]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[J].高原气象, 36(6):1655-1664. DOI:10.7522/j.issn.1000-0534.2017.00006.
[32]杨冰韵, 张华, 彭杰, 等, 2014.利用CloudSat卫星资料分析云微物理和光学性质的分布特征[J].高原气象, 33(4):1105-1118. DOI:10.7522/j.issn.1000-0534.2013.00026.
[33]张国栋, 1997.冰云短波辐射特性参数化[J].应用气象学报, 8(3):283-291.
