Simple conversion of netCDF4.Dataset to xarray Dataset

Question

I know how to convert netCDF4.Dataset to xarray DataArray manually. However, I would like to know whether is there any simple and elegant way, e.g. using xarray backend, for simple conversion of the following 'netCDF4.Dataset' object to xarray DataArray object:

<type 'netCDF4.Dataset'>
root group (NETCDF4 data model, file format HDF5):
    Originating_or_generating_Center: US National Weather Service, National Centres for Environmental Prediction (NCEP)
    Originating_or_generating_Subcenter: NCEP Ensemble Products
    GRIB_table_version: 2,1
    Type_of_generating_process: Ensemble forecast
    Analysis_or_forecast_generating_process_identifier_defined_by_originating_centre: Global Ensemble Forecast System (GEFS)
    Conventions: CF-1.6
    history: Read using CDM IOSP GribCollection v3
    featureType: GRID
    History: Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)
Original Dataset = /data/ldm/pub/native/grid/NCEP/GEFS/Global_1p0deg_Ensemble/member/GEFS_Global_1p0deg_Ensemble_20170926_0600.grib2.ncx3#LatLon_181X360-p5S-180p0E; Translation Date = 2017-09-26T17:50:23.259Z
    geospatial_lat_min: 0.0
    geospatial_lat_max: 90.0
    geospatial_lon_min: 0.0
    geospatial_lon_max: 359.0
    dimensions(sizes): time2(2), ens(21), isobaric1(12), lat(91), lon(360)
    variables(dimensions): float32 u-component_of_wind_isobaric_ens(time2,ens,isobaric1,lat,lon), float64 time2(time2), int32 ens(ens), float32 isobaric1(isobaric1), float32 lat(lat), float32 lon(lon), float32 v-component_of_wind_isobaric_ens(time2,ens,isobaric1,lat,lon)
    groups: 

I've got this using siphon.ncss.

Answer 1

The next release of xarray (0.10) has support for this very thing, or at least getting an xarray dataset from a netCDF4 one, for exactly the reason you're trying to use it:

import xarray as xr

nc = nc4.Dataset('filename.nc', mode='r')  # Or from siphon.ncss
dataset = xr.open_dataset(xr.backends.NetCDF4DataStore(nc))

Or with siphon.ncss, this would look like:

from datetime import datetime
from siphon.catalog import TDSCatalog
import xarray as xr

gfs_cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog'
                     '/grib/NCEP/GFS/Global_0p5deg/catalog.xml')
latest = gfs_cat.latest
ncss = latest.subset()
query = ncss.query().variables('Temperature_isobaric')
query.time(datetime.utcnow()).accept('netCDF4')
nc = ncss.get_data(query)
dataset = xr.open_dataset(xr.backends.NetCDF4DataStore(nc))

Until it's released, you could install xarray from master. Otherwise, the only other solution is to do everything manually.