Fix how forcing data is read in#56
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mnlevy1981 wants to merge 4 commits into
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Using open_mfdataset() in data_wrangling.py caused our forcing dataset to be chunked in time. This didn't play nicely with xr.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). Using xr.open_dataset() and then merging all the datasets does not introduce chunking in the time dimension, so xr.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.
To decrease memory usage, create temporary forcing stream files that only include years that might be used by the current run (based on start_year and nyears). That's start_year-1, start_year, ... start_year + nyears -1 (end_year), and start_year + nyears
Also update Run Multiple Years (highres).ipynb to only use one year of forcing at a time (by creating temporary forcing stream files)
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Using
open_mfdataset()indata_wrangling.pycaused 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 thetimedimension, 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?