Abstract: An important research subjectis to reveal the correlation of two meteorolog ical fields while analyzing in the meteorological data.In this field,two kinds of different analytical methods existat present:one is canonical correlation analysis,abbrev iated as CCA;the other is singular value decomposition,abbreviated as SVD.CCA and SVD is compared using theory analysis,method comparison and example calculation.Four principal results have been achieved as folows.Firstly,to the same data X and Y,after carrying on relevant analysis with CCA and SVD method separately,the results are total different.The results indicate that SVD cannot distill correlativity of two variable fields effectively,but CCA can distill themas effectively as possible.Correlation of CCA combination variable fields can embody more correlativity of original variable fields than that of SVD,and using CCA eigenvect or fields to express their correlation distribution are significant than using that of SVD.The example calculation also shows that maximal correlations obtained from the two methods have prominent difference and the value of correlation coefficient of CCA is obviously bigger than that of SVD.Secondly,it is more significant to study the correlations of source variable fields through studying the correlation of combination variable fields using CCA than that of using SVD.The combination variable fields obtained from CCA are independent and or thogonal but not for SVD.Thirdly,it is obvious that CCA is more fully and truly embody the correlativity of original variable fields than that of SVD for CCA takes into account that the self change of each variable fields when studying original variable fields but not for SVD.Finally,it is deserved to study more about the results obtained from SVD in respect that the correlation between x and y is significant if CCA is adopted.