Preprocessing of REMO output for double nesting

Preprocessing of REMO output data is required if you want to a parent REMO run to drive a higher-resolution double nesting run.

Accessing REMO output

Preparing REMO output for preprocessing if quite easy since we alredy have one timestep per file (the tfile). We can speed things up if we preprocess files in parallel.

[1]:
%load_ext autoreload
%autoreload 2
[2]:
import dask
import xarray as xr
from dask.distributed import Client
import tarfile
import glob
import os
import pyremo as pr
from pyremo.preproc import remap_remo, write_forcing_file
[3]:
client = Client(dashboard_address="localhost:8787", threads_per_worker=1, n_workers=32)
2025-04-08 16:52:21,237 - distributed.scheduler - WARNING - Failed to format dashboard link, unknown value: 'JUPYTERHUB_SERVICE_PREFIX'

First, we prepare the input data by extracting a tfile archive from a parent REMO run.

[4]:
tar_file_path = (
    "/work/bg1439/data/remo-results/remo_results_067000/1979/e067000t197901.tar"
)
destination_path = "/scratch/g/g300046/067000/1979/"
[5]:
with tarfile.open(tar_file_path, "r") as tar:
    tar.extractall(path=destination_path)

Now, we collect the extracted files into a list for preprocessing:

[8]:
tfiles = sorted(glob.glob(os.path.join(destination_path, "*.nc")))

Now, we define some functions to handle the preprocessing. Note, that we delay the remap function so that we can compute them later in parallel.

[9]:
def open_remo_dataset(filename):
    return pr.parse_dates(xr.open_dataset(filename), use_cftime=True)


@dask.delayed
def remap(filename, em, hm, vc, surflib, path, expid):
    ds = open_remo_dataset(filename)
    ads = remap_remo(ds, em, hm, vc, surflib, initial=True, lice=True)
    path = path.format(date=ds.time.item())
    os.makedirs(path, exist_ok=True)
    return write_forcing_file(ads, path=path, expid=expid)

Now, let’s define some details about the input and output grids and file pathes.

[10]:
expid = "000000"
path = "/scratch/g/g300046/000000/{date:%Y}/{date:%m}"

vc = pr.vc.tables["vc_49lev_nh_pt2000"]
surflib = pr.update_meta_info(
    xr.open_dataset("/scratch/g/g300046/lib_WRC-0275_frac.nc").squeeze(drop=True).load()
)
em = pr.domain_info("AFR-22")
hm = pr.domain_info("WRC-3")

afiles = [
    remap(
        tfile,
        em,
        hm,
        vc,
        surflib,
        path,
        expid,
    )
    for tfile in tfiles
]
[11]:
%time afiles_ = dask.compute(*afiles)
/work/ch0636/g300046/conda_envs/pyremo/lib/python3.10/site-packages/distributed/client.py:3358: UserWarning: Sending large graph of size 10.33 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
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CPU times: user 1min, sys: 10.7 s, total: 1min 10s
Wall time: 3min 50s