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总云量产品在中国区域的分析检验
Analysis and Validation of Total Cloud Amount Data in China
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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。Abstract: ISCCP, station observation and MODIS data are the major sources for cloud am unt so far.Cloud amount 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 toestimate the differences between them quantificationally.The analysis show that spatial distribution of cloud amount from ISCCP and station observation inJanuary 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 is1.13 and the root-mean-square error is 2.9.The comparison of cloud amount from ISCCP, station observations and MODIS shows that in Januarythe 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 is3.15, relative error is 1.5, bias is 2.0 and root-mean-square error is 4.1.In July the correlation coefficientbetween 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'snecessary to correct it.Above all, the cloud amount data from ISCCP is of long time series and global.Its accuracy, spatialand temporal resolutions can meet climate research needs in main.
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Key words:
- cloud amount;
- ISCCP;
- station observation;
- MODIS;
- comparison
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图 1 2004年7月28日09:00 ISCCP (a) 与常规观测总云量 (b) 的空间分布
Fig. 1 The special distribution of cloud amount from ISCCP (a) and observations (b) at 09:00 28 July 2004
图 2 2004年7月总云量的逐日变化图
Fig. 2 Daily variation of cloud amount from ISCCP and observations in July 2004
图 3 2004年7月我国平均ISCCP与常规观测总云量关系散点图
Fig. 3 The scatter diagram between cloud amount from ISCCP and observations in July 2004
图 4 2004年7月ISCCP和常规观测总云量相对差、均方根差、相关系数空间分布图
Fig. 4 The distribution of relative error, root-mean-square error and correlation coefficient between cloud amount from ISCCP and observations in July 2004
图 5 2004年1月1日00:00和1月9日21:00 ISCCP与常规观测总云量空间分布
Fig. 5 The special distribution of cloud amount from ISCCP and observations at 00:00 1 January 2004 and 21:00 9 January 2004
图 6 2004年1月ISCCP与常规观测总云量关系点聚图
Fig. 6 The scatter diagram between cloud amount from ISCCP and observations in January 2004
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