Analysis and Validation of Total Cloud Amount Data in China

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.