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:
- Is it better to develop a separate ordinary/universal kriging model for each variable or to develop a co-kriging model for all three.
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.