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总云量产品在中国区域的分析检验
引用本文:刘瑞霞,陈洪滨,郑照军,刘年庆,师春香,刘玉洁.总云量产品在中国区域的分析检验[J].应用气象学报,2009,20(5):571-578.
作者姓名:刘瑞霞  陈洪滨  郑照军  刘年庆  师春香  刘玉洁
作者单位:1.中国科学院大气物理研究所中层大气与全球环境观测实验室, 北京 100029
基金项目:国家科技部基础条件平台工作,公益性行业(气象)科研专项(GYHY,中国气象局成都高原气象开放实验室基金课题 
摘    要:对ISCCP、常规观测以及MODIS总云量3种目前使用较多的总云量资料进行对比分析, 重点考察时间序列较长的ISCCP和常规观测总云量, 给出定量对比结果, 为使用这3种总云量资料的用户提供参考。研究表明:ISCCP与常规观测总云量相比, 7月二者的空间分布具有很好的一致性, 但白天ISCCP总云量比常规观测总云量多, 夜间却往往比常规观测总云量少, 二者误差分布表现为东部和东南部小于西北部的特征; 而1月二者空间分布比较一致, 但是在天山和东北地区高、低值中心经常不匹配, 这两个区域总云量资料需慎用; 7月ISCCP总云量精度明显高于1月。ISCCP、常规观测以及MODIS总云量对比结果表明:1月MODIS总云量比其他两种资料大, 而7月为最小。相对常规观测, 1月ISCCP总云量精度优于MODIS, 而7月MODIS总云量略优于ISCCP。

关 键 词:云量    ISCCP    常规观测    MODIS    对比分析
收稿时间:2008-07-28

Analysis and Validation of Total Cloud Amount Data in China
Liu Ruixia,Chen Hongbin,Zheng Zhaojun,Liu Nianqing,Shi Chunxiang,Liu Yujie.Analysis and Validation of Total Cloud Amount Data in China[J].Quarterly Journal of Applied Meteorology,2009,20(5):571-578.
Authors:Liu Ruixia  Chen Hongbin  Zheng Zhaojun  Liu Nianqing  Shi Chunxiang  Liu Yujie
Institution:1.Lab for Middle Atmosphere and Global Environment Observation, Institute of the Atmospheric Physics, CAS, Beijing 1000292.Graduate University of Chinese Academy of Sciences, Beijing 1000493.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081
Abstract:ISCCP, station observation and MODIS data are the major sources for cloud amount so far. Cloud a-mount is crucial for climate analysis and climate model modulating. These three types of cloud amount data, especially the ISCCP and station observations are compared because they are of long term sequence, and the quantity results are given for future reference.Cloud amount data from ISCCP, station observations and MODIS in January and July 2004 are selected. Their spatial and temporal distribution characteristics are compared, and then the absolute error, relative error, bias, root-mean-square error and correlation coefficient between them are calculated in order to estimate the differences between them quantificationally.The analysis show that spatial distribution of cloud amount from ISCCP and station observation in January and July are similar, but the high and low value regions don't match very well in Tianshan Mountain and Northeast China in January, especially at night. The disagreement may come from observation error in station data. The data at night in these two regions should be used carefully. In January the correlation coefficient between cloud amount from ISCCP and station observation is 0.59, the absolute error is2.56, the relative error is 1.49, the bias is 0.99 and the root-mean-square error is 3.55. In July, the correlation coefficient between them is 0.67, the absolute error is 2.06, the relative error is 0.85, the bias is 1.13 and the root-mean-square error is 2.9.The comparison of cloud amount from ISCCP, station observations and MODIS shows that in January the cloud amount derived from MODIS is the largest, but in July it is the smallest. And in January the correlation coefficient between cloud amount from MODIS and station observations is 0. 5, absolute error is 3.15, relative error is 1.5, bias is 2.0 and root-mean-square error is 4. 1. In July the correlation coefficient between them is 0.69, absolute error is 1.96, relative error is 0.77, bias is 0.52 and root-mean-square error is 2.83.There is systematic error between cloud amount from satellite and ground station observations, so it's necessary to correct it.Above all, the cloud amount data from ISCCP is of long time series and global. Its accuracy, spatial and temporal resolutions can meet climate research needs in main.
Keywords:ISCCCP  MODIS  cloud amount  ISCCP  station observation  MODIS  comparison
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