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I am trying to understand singular value decomposition analysis. I compared two gridded atmospheric data.

The Mode 1 has 79.5% squared covariance fraction. Modes 2 and 3 have 3% and 2%, respectively. From Mode 1 to 3, the Varf are 39, 12, 5, respectively. Then, there is heterogeneous correlation at 0.5, 0.4, 0.6 for Modes 1 to 3.

My question is how to interpret these values or what are these values/numbers? What is scf and how is it different from varf? What is a heterogeneous correlation and how is it different from the homogeneous one? I get frustrated by the lack of online reference on this.

kjetil b halvorsen
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user2543
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  • Does [this](https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca/134283?r=SearchResults#134283) answer your question? – mhdadk Apr 01 '21 at 17:19
  • @mhdadk I did not find the discussion on what is the purpose of SCF, VARF, among other things. I was actually looking for straightforward answers and not on the mathematical/theoretical aspect of SVD. – user2543 Apr 02 '21 at 01:18
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    Could you tell us some details on how you compared gridded data? SVD on what? The difference of the grids? ... also explain your abbrevs, and have a look at https://stats.stackexchange.com/questions/177102/what-is-the-intuition-behind-svd. Maybe you can do interpretation via reconstructions? – kjetil b halvorsen Apr 04 '21 at 23:48

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