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Using
open_mfdataset()
indata_wrangling.py
caused our forcing dataset to be chunked intime
. This didn't play nicely withxr.map_blocks()
, resulting in the wrong forcing data being available when trying to read multiple netcdf files (such as the 0.1 degree POP time series files). Usingxr.open_dataset()
and then merging all the datasets does not introduce chunking in thetime
dimension, soxr.map_blocks()
receives the entire forcing dataset.Note that this increases the memory footprint, especially in the Run Multiple Years (highres) notebook. I've had trouble getting enough resources on casper to run two years at a time.
@rmshkv -- do you want to play with this branch and see if you can get two years per run with the 0.1 degree forcing? Or should we bring it in as-is and then figure out how to update the notebook later?