CAJ | 학술논문

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,重点考察时问序列较长的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.
대ISCCP、상규관측이급MODIS총운량3충목전사용교다적총운량자료진행대비분석,중점고찰시문서렬교장적ISCCP화상규관측총운량,급출정량대비결과,위사용저3충총운량자료적용호제공삼고.연구표명:ISCCP여상규관측총운량상비,7월이자적공간분포구유흔호적일치성,단백천ISCCP총운량비상규관측총운량다,야간각왕왕비상규관측총운량소,이자오차분포표현위동부화동남부소우서북부적특정;이1월이자공간분포비교일치,단시재천산화동북지구고、저치중심경상불필배,저량개구역총운량자료수신용;7월ISCCP총운량정도명현고우1월.ISCCP、상규관측이급MODIS총운량대비결과표명:1월MODIS총운량비기타량충자료대,이7월위최소.상대상규관측,1월ISCCP총운량정도우우MODIS,이7월MODIS총운량략우우ISCCP.
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.