I want to create a dataset of estimated values using inverse distance weighting. I am starting with a dataset of daily values of measurements for specific coordinates. I created a model (below) which iterates through daily values and creates raster layers of interpolated values (IDW tool). Then, values from a set of specific coordinates are extracted from each raster later and are output as a table (sample tool). Finally, tables are converted to .xls and I will use a different data management software to complete the final merge.
As far as I can tell, this model works. But, it takes some time and I have three decades worth of daily measurements to run this for.
How can I make this model more efficient?
Thoughts I had:
-delete the intermediate raster and info table files after they have been converted to .xls --> i tried selecting "delete intermediate data" but files weren't deleted.
-use collect values, can't figure how this works
-use the batch option to run this model in batches by 1..5..10 years.
-merge in arcmap