jbusecke

jbusecke

jbusecke

Shared dataset on the princeton HPC filesystems

We often use datasets of a variety of variables (e.g. temperature, salinity, chlorophyll) to validate model runs or conduct analyses.

Ideally, we want to:

1. avoid storing duplicates of files on the server.
2. be able to share these datasets with others in the group (downloading and postprocessing is boring).

Our group will be able to get to cool results much quicker if each person takes a bit more time and does the postprocessing

jbusecke

dc_dx = c[:,1:] - c[:,0:-1] / dx[:,1:]

#or was it this way?
dc_dx = c[1:,:] - c[0:-1,:] / dx[1:,:]

...

jbusecke

import intake
import matplotlib.pyplot as plt
import xarray as xr
import numpy as np
import cmocean.cm as cmo
url = "https://raw.githubusercontent.com/NCAR/intake-esm-datastore/master/catalogs/pangeo-cmip6.json"
col = intake.open_esm_datastore(url)
cat = col.search(
    table_id='Omon',
    grid_label='gn',

jbusecke

from dask.distributed import Client, progress
from dask_gateway import Gateway
gateway = Gateway()
cluster = gateway.new_cluster()
cluster.adapt(minimum=2, maximum=12)
cluster

jbusecke

from fastjmd95 import rho
ds['sigma_0'] = rho(ds.so, ds.thetao, 0) - 1000

jbusecke

ds_section = ds.sel(
  time='2010-01',
  y=slice(-90,10),
  lev=slice(0,1500)
).interp(x=230).squeeze()

jbusecke

fig, axarr = plt.subplots(nrows=3, figsize=[10,12], sharex=True)
for ax, var, cmap in zip(
    axarr.flat,
    ['thetao', 'so', 'cfc11'],
    [cmo.thermal, cmo.haline, cmo.matter]
):
    
    kwargs = dict(yincrease=False, x='lat', y='lev', ax=ax)
    
    # Plot a section along the dateline in 1980 in the southern ocean

jbusecke

# create the grid object
from xgcm import Grid
grid = Grid(
  ds,
  periodic=False,
  coords={'Z':{'center':'lev', 'outer':'lev_outer'}})
grid