I want to estimate an interpolation model(s) for daily minimum temperature, daily maximum temperature and daily rainfall. I have samples of approximately the same size for all three variables at different locations although for certain days and locations only 1 or 2 of the 3 covariates are sampled . All three variables have some correlation, especially minimum temperature and maximum temperature. I read that co-kriging is especially adequate in cases where:

  • one wishes to interpolate one variable that is relatively sparsely sampled
  • one has available another covariate which is more densely sampled

This is not my case. Rather I have available samples for three covariates and want to interpolate all three. I have the following questions:

  1. Is it better to develop a separate ordinary/universal kriging model for each variable or to develop a co-kriging model for all three.
  2. I am using days and locations with no missing values to estimate the parameters of the interpolation model , but want to use it to interpolate missing values in different days and locations. Is it possible to do this in cases where:

    • certain locations have all three variables missing
    • certain locations have one o two out of the three variables missing

I am working in R in case there are any special considerations to heed.


Yes, it is appropriate. Prediction by kriging can theoretically only get better when you bring in more correlated information, and that is what you do when moving from kriging to co-kriging. In practice, the gain can be disappointing, considering the effort it takes.

There can also be other reasons to favor co-kriging. An example is when you need the correlation of the kriging prediction errors for two or more variables, e.g. because you want to sum them or compute their difference.

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