def main(args):
fname = args.file
save_file = args.save_file
# List of variables that are already included in the WRF output and that we want to compute using the wrf-python
variables = dict(primary=[
'XLAT', 'XLONG', 'T2', 'SWDOWN', 'LWUPB', 'GLW', 'PSFC', 'RAINC',
'RAINNC', 'RAINSH', 'SNOWNC', 'SST', 'DIFFUSE_FRAC', 'LANDMASK',
'LAKEMASK', 'PBLH', 'TSK', 'UST'
],
computed=['rh2', 'slp', 'mdbz'])
# Generate height table for interpolation of U and V components
gen_heights = [
20, 320, 20
] # minimum height, maximum height, distance between heights
# Output time units
time_units = 'seconds since 1970-01-01 00:00:00'
os.makedirs(os.path.dirname(save_file), exist_ok=True)
# create_dir(os.path.dirname(save_file))
# Create list of heights between min and max height separated by a stride value defined above
heights = list(np.arange(gen_heights[0], gen_heights[1], gen_heights[2]))
heights.append(gen_heights[1])
# Open using netCDF toolbox
ncfile = xr.open_dataset(fname)
original_global_attributes = ncfile.attrs
ncfile = ncfile._file_obj.ds
# Load primary variables and append to list
primary_vars = {}
for var in variables['primary']:
primary_vars[var] = cf.delete_attr(getvar(ncfile, var))
# Calculate diagnostic variables defined above and add to dictionary
diagnostic_vars = {}
for dvar in variables['computed']:
diagnostic_vars[dvar.upper()] = cf.delete_attr(getvar(ncfile, dvar))
# Subtract terrain height from height above sea level
new_z = getvar(ncfile, 'z') - getvar(ncfile, 'ter')
# Calculate u and v components of wind rotated to Earth coordinates
uvm = getvar(ncfile, 'uvmet')
# interpolate u and v components of wind to 0-200m by 10m
uvtemp = interplevel(uvm, new_z, heights, default_fill(np.float32))
uvtemp = uvtemp.rename({'level': 'height'})
utemp, vtemp = cf.split_uvm(uvtemp)
# Calculate 10m u and v components of wind rotated to Earth coordinates and split into separate variables
primary_vars['U10'], primary_vars['V10'] = cf.split_uvm(
getvar(ncfile, 'uvmet10'))
# Concatenate the list of calculated u and v values into data array. Append to diagnostic_vars list
diagnostic_vars['U'] = xr.concat(utemp, dim='height')
diagnostic_vars['V'] = xr.concat(vtemp, dim='height')
# Interpolate u and v components of wind to Boundary Layer Heights for wind gust calculation
uvpblh = interplevel(uvm, new_z, primary_vars['PBLH'])
uvpblh = cf.delete_attr(uvpblh).drop(['u_v'
]) # drop unnecessary attributes
upblh = uvpblh[0].rename('upblh')
vpblh = uvpblh[1].rename('vpblh')
# Calculate wind gust
sfcwind = np.sqrt(primary_vars['U10']**2 + primary_vars['V10']**2)
pblwind = np.sqrt(upblh**2 + vpblh**2)
delwind = pblwind - sfcwind
pblval = primary_vars['PBLH'] / 2000
pblval = pblval.where(
pblval < 0.5, other=0.5
) # if the value is less than 0.5, keep it. otherwise, change to 0.5
delwind = delwind * (1 - pblval)
gust = sfcwind + delwind
gust = gust.drop('level') # drop the 'level' coordinate
gust = gust.astype(np.float32)
# Create xarray dataset of primary and diagnostic variables
ds = xr.Dataset({**primary_vars, **diagnostic_vars})
ds['U'] = ds.U.astype(np.float32)
ds['V'] = ds.V.astype(np.float32)
ds['height'] = ds.height.astype(np.int32)
try:
del ds.U.attrs['vert_units']
del ds.V.attrs['vert_units']
except KeyError:
pass
ds['Times'] = np.array([
pd.Timestamp(ds.Time.data).strftime('%Y-%m-%d_%H:%M:%S')
]).astype('<S19')
ds = ds.expand_dims('Time', axis=0)
# Add description and units for lon, lat dimensions for georeferencing
ds['XLAT'].attrs['description'] = 'latitude'
ds['XLAT'].attrs['units'] = 'degree_north'
ds['XLONG'].attrs['description'] = 'longitude'
ds['XLONG'].attrs['units'] = 'degree_east'
# Set XTIME attribute
ds['XTIME'].attrs['units'] = 'minutes'
# Set lon attributes
ds['XLONG'].attrs['long_name'] = 'Longitude'
ds['XLONG'].attrs['standard_name'] = 'longitude'
ds['XLONG'].attrs['short_name'] = 'lon'
ds['XLONG'].attrs['units'] = 'degrees_east'
ds['XLONG'].attrs['axis'] = 'X'
ds['XLONG'].attrs['valid_min'] = np.float32(-180.0)
ds['XLONG'].attrs['valid_max'] = np.float32(180.0)
# Set lat attributes
ds['XLAT'].attrs['long_name'] = 'Latitude'
ds['XLAT'].attrs['standard_name'] = 'latitude'
ds['XLAT'].attrs['short_name'] = 'lat'
ds['XLAT'].attrs['units'] = 'degrees_north'
ds['XLAT'].attrs['axis'] = 'Y'
ds['XLAT'].attrs['valid_min'] = np.float32(-90.0)
ds['XLAT'].attrs['valid_max'] = np.float32(90.0)
# Set depth attributes
ds['height'].attrs['long_name'] = 'Height Above Ground Level'
ds['height'].attrs['standard_name'] = 'height'
ds['height'].attrs[
'comment'] = 'Derived from subtracting terrain height from height above sea level'
ds['height'].attrs['units'] = 'm'
ds['height'].attrs['axis'] = 'Z'
ds['height'].attrs['positive'] = 'up'
# Set u attributes
ds['U'].attrs['long_name'] = 'Eastward Wind Component'
ds['U'].attrs['standard_name'] = 'eastward_wind'
ds['U'].attrs['short_name'] = 'u'
ds['U'].attrs['units'] = 'm s-1'
ds['U'].attrs['description'] = 'earth rotated u'
ds['U'].attrs['valid_min'] = np.float32(-300)
ds['U'].attrs['valid_max'] = np.float32(300)
# Set v attributes
ds['V'].attrs['long_name'] = 'Northward Wind Component'
ds['V'].attrs['standard_name'] = 'northward_wind'
ds['V'].attrs['short_name'] = 'v'
ds['V'].attrs['units'] = 'm s-1'
ds['V'].attrs['description'] = 'earth rotated v'
ds['V'].attrs['valid_min'] = np.float32(-300)
ds['V'].attrs['valid_max'] = np.float32(300)
# Set u10 attributes
ds['U10'].attrs['long_name'] = 'Eastward Wind Component - 10m'
ds['U10'].attrs['standard_name'] = 'eastward_wind'
ds['U10'].attrs['short_name'] = 'u'
ds['U10'].attrs['units'] = 'm s-1'
ds['U10'].attrs['description'] = '10m earth rotated u'
ds['U10'].attrs['valid_min'] = np.float32(-300)
ds['U10'].attrs['valid_max'] = np.float32(300)
# Set v10 attributes
ds['V10'].attrs['long_name'] = 'Northward Wind Component - 10m'
ds['V10'].attrs['standard_name'] = 'northward_wind'
ds['V10'].attrs['short_name'] = 'v'
ds['V10'].attrs['units'] = 'm s-1'
ds['V10'].attrs['description'] = '10m earth rotated v'
ds['V10'].attrs['valid_min'] = np.float32(-300)
ds['V10'].attrs['valid_max'] = np.float32(300)
# set primary attributes
ds['GLW'].attrs[
'standard_name'] = 'surface_downwelling_longwave_flux_in_air'
ds['GLW'].attrs['long_name'] = 'Surface Downwelling Longwave Flux'
ds['LWUPB'].attrs['standard_name'] = 'surface_upwelling_longwave_flux'
ds['LWUPB'].attrs['long_name'] = 'Surface Upwelling Longwave Flux'
ds['PSFC'].attrs['standard_name'] = 'surface_air_pressure'
ds['PSFC'].attrs['long_name'] = 'Air Pressure at Surface'
ds['RH2'].attrs['standard_name'] = 'relative_humidity'
ds['RH2'].attrs['long_name'] = 'Relative Humidity'
ds['SLP'].attrs['standard_name'] = 'air_pressure_at_sea_level'
ds['SLP'].attrs['long_name'] = 'Air Pressure at Sea Level'
ds['SWDOWN'].attrs[
'standard_name'] = 'surface_downwelling_shortwave_flux_in_air'
ds['SWDOWN'].attrs['long_name'] = 'Surface Downwelling Shortwave Flux'
ds['T2'].attrs['standard_name'] = 'air_temperature'
ds['T2'].attrs['long_name'] = 'Air Temperature at 2m'
ds['RAINC'].attrs['long_name'] = 'Accumulated Total Cumulus Precipitation'
ds['RAINNC'].attrs[
'long_name'] = 'Accumulated Total Grid Scale Precipitation'
ds['RAINSH'].attrs[
'long_name'] = 'Accumulated Shallow Cumulus Precipitation'
ds['SNOWNC'].attrs['standard_name'] = 'surface_snow_thickness'
ds['SNOWNC'].attrs[
'long_name'] = 'Accumulated Total Grid Scale Snow and Ice'
ds['SNOWNC'].attrs['description'] = '{}; water equivalent'.format(
ds['SNOWNC'].description)
ds['SST'].attrs['standard_name'] = 'sea_surface_temperature'
ds['SST'].attrs['long_name'] = 'Sea Surface Temperature'
ds['DIFFUSE_FRAC'].attrs[
'long_name'] = 'Diffuse Fraction of Surface Shortwave Irradiance'
ds['MDBZ'].attrs['long_name'] = 'Maximum Radar Reflectivity'
ds['TSK'].attrs['long_name'] = 'Surface Skin Temperature'
ds['LANDMASK'].attrs['standard_name'] = 'land_binary_mask'
ds['LANDMASK'].attrs['long_name'] = 'Land Mask'
ds['LAKEMASK'].attrs['long_name'] = 'Lake Mask'
ds['PBLH'].attrs[
'long_name'] = 'Height of the Top of the Planetary Boundary Layer (PBL)'
ds['UST'].attrs['long_name'] = 'Friction Velocity'
ds['XTIME'].attrs['long_name'] = 'minutes since simulation start'
# add the calculated wind gust to the dataset
windgust_attrs = dict(
long_name='Near Surface Wind Gust',
description=
'Calculated wind gust, computed by mixing down momentum from the level at the '
'top of the planetary boundary layer',
units='m s-1')
ds['WINDGUST'] = xr.Variable(gust.dims, gust.values, attrs=windgust_attrs)
ds['WINDGUST'] = ds['WINDGUST'].expand_dims('Time', axis=0)
# Set time attribute
ds['Time'].attrs['standard_name'] = 'time'
datetime_format = '%Y%m%dT%H%M%SZ'
created = pd.Timestamp(pd.datetime.utcnow()).strftime(
datetime_format) # creation time Timestamp
time_start = pd.Timestamp(pd.Timestamp(
ds.Time.data[0])).strftime(datetime_format)
time_end = pd.Timestamp(pd.Timestamp(
ds.Time.data[0])).strftime(datetime_format)
global_attributes = OrderedDict([
('title', 'Rutgers Weather Research and Forecasting Model'),
('summary', 'Processed netCDF containing subset of RUWRF output'),
('keywords', 'Weather Advisories > Marine Weather/Forecast'),
('Conventions', 'CF-1.7'),
('naming_authority', 'edu.rutgers.marine.rucool'),
('history',
'Hourly WRF raw output processed into new hourly file with selected variables.'
), ('processing_level', 'Level 2'),
('comment', 'WRF Model operated by RUCOOL'),
('acknowledgement',
'This data is provided by the Rutgers Center for Ocean Observing Leadership. Funding is provided by the New Jersey Board of Public Utilities).'
), ('standard_name_vocabulary', 'CF Standard Name Table v41'),
('date_created', created), ('creator_name', 'Joseph Brodie'),
('creator_email', '*****@*****.**'),
('creator_url', 'rucool.marine.rutgers.edu'),
('institution',
'Center for Ocean Observing and Leadership, Department of Marine & Coastal Sciences, Rutgers University'
),
('project',
'New Jersey Board of Public Utilities - Offshore Wind Energy - RUWRF Model'
), ('geospatial_lat_min', -90), ('geospatial_lat_max', 90),
('geospatial_lon_min', -180), ('geospatial_lon_max', 180),
('geospatial_vertical_min', 0.0), ('geospatial_vertical_max', 0.0),
('geospatial_vertical_positive', 'down'),
('time_coverage_start', time_start), ('time_coverage_end', time_end),
('creator_type', 'person'),
('creator_institution', 'Rutgers University'),
('contributor_name', 'Joseph Brodie'),
('contributor_role', 'Director of Atmospheric Research'),
('geospatial_lat_units', 'degrees_north'),
('geospatial_lon_units', 'degrees_east'), ('date_modified', created),
('date_issued', created), ('date_metadata_modified', created),
('keywords_vocabulary', 'GCMD Science Keywords'),
('platform', 'WRF Model Run'), ('cdm_data_type', 'Grid'),
('references',
'http://maracoos.org/node/146 https://rucool.marine.rutgers.edu/facilities https://rucool.marine.rutgers.edu/data'
)
])
global_attributes.update(original_global_attributes)
ds = ds.assign_attrs(global_attributes)
# Add compression to all variables
encoding = {}
for k in ds.data_vars:
encoding[k] = {'zlib': True, 'complevel': 1}
# add the encoding for time so xarray exports the proper time.
# Also remove compression from dimensions. They should never have fill values
encoding['Time'] = dict(units=time_units,
calendar='gregorian',
zlib=False,
_FillValue=False,
dtype=np.double)
encoding['XLONG'] = dict(zlib=False, _FillValue=False)
encoding['XLAT'] = dict(zlib=False, _FillValue=False)
encoding['height'] = dict(zlib=False, _FillValue=False, dtype=np.int32)
ds.to_netcdf(save_file,
encoding=encoding,
format='netCDF4',
engine='netcdf4',
unlimited_dims='Time')
def main(args):
fname = args.file
save_file = args.save_file
# List of variables to subset
variables = ['XLAT', 'XLONG', 'temp', 'rh', 'z', 'pressure', 'ter', 'slp', 'cloudfrac', 'td', 'height_agl']
# Output time units
time_units = 'seconds since 1970-01-01 00:00:00'
os.makedirs(os.path.dirname(save_file), exist_ok=True)
# Open using netCDF toolbox
ncfile = xr.open_dataset(fname)
original_global_attributes = ncfile.attrs
ncfile = ncfile._file_obj.ds # to use xarray as an input to wrf-python (instead of netcdf4)
# Load variables and append to list
vars = {}
for var in variables:
if var == 'z': # change 'z' to 'height_msl'
varname = 'height_msl'
else:
varname = var
vars[varname] = cf.delete_attr(getvar(ncfile, var))
# Get u and v components of wind rotated to Earth coordinates
uvm = getvar(ncfile, 'uvmet')
for uv in ['u', 'v']:
da = uvm.sel(u_v=uv)
da = cf.delete_attr(da).drop(['u_v']) # delete extra attributes and drop the extra coordinate
da = da.rename(uv)
vars[uv] = da # add to variable dictionary
# Create xarray dataset
ds = xr.Dataset(vars)
ds['Times'] = np.array([pd.Timestamp(ds.Time.data).strftime('%Y-%m-%d_%H:%M:%S')]).astype('<S19')
ds = ds.expand_dims('Time', axis=0)
# format bottom_top and low_mid_high dimensions
ds['bottom_top'] = np.int32(ds.bottom_top)
ds['low_mid_high'] = np.array([300, 2000, 6000], dtype='int32')
# add attributes for model levels
ds['bottom_top'].attrs['units'] = '1'
ds['bottom_top'].attrs['long_name'] = 'Model Level'
ds['bottom_top'].attrs['comment'] = 'Integer coordinate for native WRF model level'
ds['bottom_top'].attrs['axis'] = 'Z'
ds['bottom_top'].attrs['positive'] = 'up'
ds['bottom_top'].attrs['_CoordinateAxisType'] = 'GeoZ' # need this attribute for THREDDs aggregation
ds['bottom_top'].attrs['_CoordinateZisPositive'] = 'up' # need this attribute for THREDDs aggregation
ds['low_mid_high'].attrs['units'] = 'm'
ds['low_mid_high'].attrs['long_name'] = 'Cloud Layer'
ds['low_mid_high'].attrs['comment'] = 'Cloud layer, low 300 m, mid 2000 m, high 6000 m'
ds['low_mid_high'].attrs['axis'] = 'Z'
ds['low_mid_high'].attrs['positive'] = 'up'
ds['low_mid_high'].attrs['_CoordinateAxisType'] = 'Height' # need this attribute for THREDDs aggregation
ds['low_mid_high'].attrs['_CoordinateZisPositive'] = 'up' # need this attribute for THREDDs aggregation
# Add description and units for lon, lat dimensions for georeferencing
ds['XLAT'].attrs['description'] = 'latitude'
ds['XLAT'].attrs['units'] = 'degree_north'
ds['XLONG'].attrs['description'] = 'longitude'
ds['XLONG'].attrs['units'] = 'degree_east'
# Set time attribute
ds['Time'].attrs['standard_name'] = 'time'
ds['Time'].attrs['long_name'] = 'Time'
# Set XTIME attribute
ds['XTIME'].attrs['units'] = 'minutes'
ds['XTIME'].attrs['long_name'] = 'minutes since simulation start'
# Set lon attributes
ds['XLONG'].attrs['long_name'] = 'Longitude'
ds['XLONG'].attrs['standard_name'] = 'longitude'
ds['XLONG'].attrs['short_name'] = 'lon'
ds['XLONG'].attrs['units'] = 'degrees_east'
ds['XLONG'].attrs['axis'] = 'X'
ds['XLONG'].attrs['valid_min'] = np.float32(-180.0)
ds['XLONG'].attrs['valid_max'] = np.float32(180.0)
# Set lat attributes
ds['XLAT'].attrs['long_name'] = 'Latitude'
ds['XLAT'].attrs['standard_name'] = 'latitude'
ds['XLAT'].attrs['short_name'] = 'lat'
ds['XLAT'].attrs['units'] = 'degrees_north'
ds['XLAT'].attrs['axis'] = 'Y'
ds['XLAT'].attrs['valid_min'] = np.float32(-90.0)
ds['XLAT'].attrs['valid_max'] = np.float32(90.0)
# Set attributes
ds['temp'].attrs['long_name'] = 'Air Temperature'
ds['temp'].attrs['standard_name'] = 'air_temperature'
ds['rh'].attrs['long_name'] = 'Relative Humidity'
ds['rh'].attrs['standard_name'] = 'relative_humidity'
ds['u'].attrs['long_name'] = 'Eastward Wind Component'
ds['u'].attrs['standard_name'] = 'eastward_wind'
ds['u'].attrs['short_name'] = 'u'
ds['u'].attrs['description'] = 'earth rotated u'
ds['u'].attrs['valid_min'] = np.float32(-300)
ds['u'].attrs['valid_max'] = np.float32(300)
ds['v'].attrs['long_name'] = 'Northward Wind Component'
ds['v'].attrs['standard_name'] = 'northward_wind'
ds['v'].attrs['short_name'] = 'v'
ds['v'].attrs['description'] = 'earth rotated v'
ds['v'].attrs['valid_min'] = np.float32(-300)
ds['v'].attrs['valid_max'] = np.float32(300)
ds['height_msl'].attrs['long_name'] = 'Model Height for Mass Grid (MSL)'
ds['height_msl'].attrs['standard_name'] = 'height_above_mean_sea_level'
ds['height_msl'].attrs['comment'] = 'Model height above mean sea level'
ds['pressure'].attrs['long_name'] = 'Air Pressure'
ds['pressure'].attrs['standard_name'] = 'air_pressure'
ds['ter'].attrs['long_name'] = 'Model Terrain Height'
ds['slp'].attrs['long_name'] = 'Air Pressure at Sea Level'
ds['slp'].attrs['standard_name'] = 'air_pressure_at_mean_sea_level'
ds['cloudfrac'].attrs['long_name'] = 'Layer Cloud Area Fraction'
ds['cloudfrac'].attrs['standard_name'] = 'cloud_area_fraction_in_atmosphere_layer'
ds['td'].attrs['long_name'] = 'Dew Point Temperature'
ds['td'].attrs['standard_name'] = 'dew_point_temperature'
ds['height_agl'].attrs['long_name'] = 'Model Height for Mass Grid (AGL)'
ds['height_agl'].attrs['standard_name'] = 'height'
ds['height_agl'].attrs['comment'] = 'Model height above ground level'
ds['height_agl'].attrs['axis'] = 'Z'
ds['height_agl'].attrs['positive'] = 'up'
datetime_format = '%Y%m%dT%H%M%SZ'
created = pd.Timestamp(pd.datetime.utcnow()).strftime(datetime_format) # creation time Timestamp
time_start = pd.Timestamp(pd.Timestamp(ds.Time.data[0])).strftime(datetime_format)
time_end = pd.Timestamp(pd.Timestamp(ds.Time.data[0])).strftime(datetime_format)
global_attributes = OrderedDict([
('title', 'Rutgers Weather Research and Forecasting Model'),
('summary', 'Processed netCDF containing subset of RUWRF output'),
('keywords', 'Weather Advisories > Marine Weather/Forecast'),
('Conventions', 'CF-1.7'),
('naming_authority', 'edu.rutgers.marine.rucool'),
('history', 'Hourly WRF raw output processed into new hourly file with selected variables.'),
('processing_level', 'Level 2'),
('comment', 'WRF Model operated by RUCOOL'),
('acknowledgement', 'This data is provided by the Rutgers Center for Ocean Observing Leadership. Funding is provided by the New Jersey Board of Public Utilities.'),
('standard_name_vocabulary', 'CF Standard Name Table v72'),
('date_created', created),
('creator_name', 'Joseph Brodie'),
('creator_email', '*****@*****.**'),
('creator_url', 'rucool.marine.rutgers.edu'),
('institution', 'Center for Ocean Observing and Leadership, Department of Marine & Coastal Sciences, Rutgers University'),
('project', 'New Jersey Board of Public Utilities - Offshore Wind Energy - RUWRF Model'),
('geospatial_lat_min', -90),
('geospatial_lat_max', 90),
('geospatial_lon_min', -180),
('geospatial_lon_max', 180),
('geospatial_vertical_min', 0.0),
('geospatial_vertical_max', 0.0),
('geospatial_vertical_positive', 'down'),
('time_coverage_start', time_start),
('time_coverage_end', time_end),
('creator_type', 'person'),
('creator_institution', 'Rutgers University'),
('contributor_name', 'Joseph Brodie'),
('contributor_role', 'Director of Atmospheric Research'),
('geospatial_lat_units', 'degrees_north'),
('geospatial_lon_units', 'degrees_east'),
('date_modified', created),
('date_issued', created),
('date_metadata_modified', created),
('keywords_vocabulary', 'GCMD Science Keywords'),
('platform', 'WRF Model Run'),
('cdm_data_type', 'Grid'),
('references', 'http://maracoos.org/node/146 https://rucool.marine.rutgers.edu/facilities https://rucool.marine.rutgers.edu/data')])
global_attributes.update(original_global_attributes)
ds = ds.assign_attrs(global_attributes)
# Add compression to all variables
encoding = {}
for k in ds.data_vars:
encoding[k] = {'zlib': True, 'complevel': 1}
# add the encoding for time so xarray exports the proper time.
# Also remove compression from dimensions. They should never have fill values
encoding['Time'] = dict(units=time_units, calendar='gregorian', zlib=False, _FillValue=False, dtype=np.double)
encoding['XLONG'] = dict(zlib=False, _FillValue=False)
encoding['XLAT'] = dict(zlib=False, _FillValue=False)
ds.to_netcdf(save_file, encoding=encoding, format='netCDF4', engine='netcdf4', unlimited_dims='Time')
def main(args):
fname = args.file
save_file = args.save_file
# List of variables that are already included in the WRF output
variables = ['XLAT', 'XLONG', 'LANDMASK', 'LAKEMASK']
# Output time units
time_units = 'seconds since 1970-01-01 00:00:00'
os.makedirs(os.path.dirname(save_file), exist_ok=True)
# Create list of heights in meters and pressure (mb) for interpolation of U and V components
heights_m = [50, 100, 150, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500]
heights_mb = [200, 300, 500, 700, 850, 925]
# Open using netCDF toolbox
ncfile = xr.open_dataset(fname)
original_global_attributes = ncfile.attrs
ncfile = ncfile._file_obj.ds
# Load variables and append to dictionary
nc_vars = {}
for v in variables:
nc_vars[v] = cf.delete_attr(getvar(ncfile, v))
# Calculate u and v components of wind rotated to Earth coordinates
uvm = getvar(ncfile, 'uvmet')
# Subtract terrain height from height above sea level for height in meters
new_z = getvar(ncfile, 'z') - getvar(ncfile, 'ter')
# interpolate u and v components of wind to defined heights (in meters)
uvtemp = interplevel(uvm, new_z, heights_m, default_fill(np.float32))
uvtemp = uvtemp.rename({'level': 'height'})
utemp, vtemp = cf.split_uvm(uvtemp)
# Concatenate the list of calculated u and v values into data array
nc_vars['UH'] = xr.concat(utemp, dim='height')
nc_vars['VH'] = xr.concat(vtemp, dim='height')
# Get pressure - units are Pa so convert to mb
p = getvar(ncfile, 'p') * .01
# interpolate u and v components of wind to defined pressure heights (mb)
uvtemp = interplevel(uvm, p, heights_mb, default_fill(np.float32))
uvtemp = uvtemp.rename({'level': 'pressure'})
utemp, vtemp = cf.split_uvm(uvtemp)
# get geopotential height and interpolate to defined pressure heights (mb)
ght = g_geoht.get_height(ncfile, msl=True)
geoht = interplevel(ght, p, heights_mb, default_fill(np.float32))
geoht = geoht.rename({'level': 'pressure'})
# Concatenate the list of calculated u and v values and geopotential height into data array
nc_vars['UP'] = xr.concat(utemp, dim='pressure')
nc_vars['VP'] = xr.concat(vtemp, dim='pressure')
nc_vars['geoht'] = xr.concat(geoht, dim='pressure')
# Calculate 10m u and v components of wind rotated to Earth coordinates and split into separate variables
nc_vars['U10'], nc_vars['V10'] = cf.split_uvm(getvar(ncfile, 'uvmet10'))
# Create xarray dataset of variables
ds = xr.Dataset({**nc_vars})
ds['UH'] = ds.UH.astype(np.float32)
ds['VH'] = ds.VH.astype(np.float32)
ds['UP'] = ds.UP.astype(np.float32)
ds['VP'] = ds.VP.astype(np.float32)
ds['geoht'] = ds.geoht.astype(np.float32)
ds['height'] = ds.height.astype(np.int32)
ds['pressure'] = ds.pressure.astype(np.int32)
try:
del ds.UH.attrs['vert_units']
del ds.VH.attrs['vert_units']
del ds.UP.attrs['vert_units']
del ds.VP.attrs['vert_units']
except KeyError:
pass
ds['Times'] = np.array([
pd.Timestamp(ds.Time.data).strftime('%Y-%m-%d_%H:%M:%S')
]).astype('<S19')
ds = ds.expand_dims('Time', axis=0)
# Add description and units for lon, lat dimensions for georeferencing
ds['XLAT'].attrs['description'] = 'latitude'
ds['XLAT'].attrs['units'] = 'degree_north'
ds['XLONG'].attrs['description'] = 'longitude'
ds['XLONG'].attrs['units'] = 'degree_east'
# Set XTIME attribute
ds['XTIME'].attrs['units'] = 'minutes'
# Set lon attributes
ds['XLONG'].attrs['long_name'] = 'Longitude'
ds['XLONG'].attrs['standard_name'] = 'longitude'
ds['XLONG'].attrs['short_name'] = 'lon'
ds['XLONG'].attrs['units'] = 'degrees_east'
ds['XLONG'].attrs['axis'] = 'X'
ds['XLONG'].attrs['valid_min'] = np.float32(-180.0)
ds['XLONG'].attrs['valid_max'] = np.float32(180.0)
# Set lat attributes
ds['XLAT'].attrs['long_name'] = 'Latitude'
ds['XLAT'].attrs['standard_name'] = 'latitude'
ds['XLAT'].attrs['short_name'] = 'lat'
ds['XLAT'].attrs['units'] = 'degrees_north'
ds['XLAT'].attrs['axis'] = 'Y'
ds['XLAT'].attrs['valid_min'] = np.float32(-90.0)
ds['XLAT'].attrs['valid_max'] = np.float32(90.0)
# Set depth attributes
ds['height'].attrs['long_name'] = 'Height Above Ground Level'
ds['height'].attrs['standard_name'] = 'height'
ds['height'].attrs[
'comment'] = 'Derived from subtracting terrain height from height above sea level'
ds['height'].attrs['units'] = 'm'
ds['height'].attrs['axis'] = 'Z'
ds['height'].attrs['positive'] = 'up'
ds['pressure'].attrs['long_name'] = 'Pressure'
ds['pressure'].attrs['standard_name'] = 'air_pressure'
ds['pressure'].attrs['units'] = 'millibars'
ds['pressure'].attrs['axis'] = 'Z'
ds['pressure'].attrs['positive'] = 'up'
# Set u attributes - interpolated to height in meters
ds['UH'].attrs['long_name'] = 'Eastward Wind Component'
ds['UH'].attrs['standard_name'] = 'eastward_wind'
ds['UH'].attrs['short_name'] = 'u'
ds['UH'].attrs['units'] = 'm s-1'
ds['UH'].attrs[
'description'] = 'earth rotated u, interpolated to Height Above Ground Level in meters'
ds['UH'].attrs['valid_min'] = np.float32(-300)
ds['UH'].attrs['valid_max'] = np.float32(300)
# Set v attributes - interpolated to height in meters
ds['VH'].attrs['long_name'] = 'Northward Wind Component'
ds['VH'].attrs['standard_name'] = 'northward_wind'
ds['VH'].attrs['short_name'] = 'v'
ds['VH'].attrs['units'] = 'm s-1'
ds['VH'].attrs[
'description'] = 'earth rotated v, interpolated to Height Above Ground Level in meters'
ds['VH'].attrs['valid_min'] = np.float32(-300)
ds['VH'].attrs['valid_max'] = np.float32(300)
# Set u attributes - interpolated to pressure in mb
ds['UP'].attrs['long_name'] = 'Eastward Wind Component'
ds['UP'].attrs['standard_name'] = 'eastward_wind'
ds['UP'].attrs['short_name'] = 'u'
ds['UP'].attrs['units'] = 'm s-1'
ds['UP'].attrs[
'description'] = 'earth rotated u, interpolated to pressure in millibars'
ds['UP'].attrs['valid_min'] = np.float32(-300)
ds['UP'].attrs['valid_max'] = np.float32(300)
# Set v attributes - interpolated to pressure in mb
ds['VP'].attrs['long_name'] = 'Northward Wind Component'
ds['VP'].attrs['standard_name'] = 'northward_wind'
ds['VP'].attrs['short_name'] = 'v'
ds['VP'].attrs['units'] = 'm s-1'
ds['VP'].attrs[
'description'] = 'earth rotated v, interpolated to pressure in millibars'
ds['VP'].attrs['valid_min'] = np.float32(-300)
ds['VP'].attrs['valid_max'] = np.float32(300)
# Set u10 attributes
ds['U10'].attrs['long_name'] = 'Eastward Wind Component - 10m'
ds['U10'].attrs['standard_name'] = 'eastward_wind'
ds['U10'].attrs['short_name'] = 'u'
ds['U10'].attrs['units'] = 'm s-1'
ds['U10'].attrs['description'] = '10m earth rotated u'
ds['U10'].attrs['valid_min'] = np.float32(-300)
ds['U10'].attrs['valid_max'] = np.float32(300)
# Set v10 attributes
ds['V10'].attrs['long_name'] = 'Northward Wind Component - 10m'
ds['V10'].attrs['standard_name'] = 'northward_wind'
ds['V10'].attrs['short_name'] = 'v'
ds['V10'].attrs['units'] = 'm s-1'
ds['V10'].attrs['description'] = '10m earth rotated v'
ds['V10'].attrs['valid_min'] = np.float32(-300)
ds['V10'].attrs['valid_max'] = np.float32(300)
# Set geopotential height attributes
ds['geoht'].attrs['long_name'] = 'Geopotential Height Above Mean Sea Level'
ds['geoht'].attrs['standard_name'] = 'geopotential_height'
ds['geoht'].attrs['units'] = 'm'
ds['geoht'].attrs[
'description'] = 'geopotential height above mean sea level, interpolated to pressure in millibars'
ds['LANDMASK'].attrs['standard_name'] = 'land_binary_mask'
ds['LANDMASK'].attrs['long_name'] = 'Land Mask'
ds['LAKEMASK'].attrs['long_name'] = 'Lake Mask'
ds['XTIME'].attrs['long_name'] = 'minutes since simulation start'
# Set time attribute
ds['Time'].attrs['standard_name'] = 'time'
datetime_format = '%Y%m%dT%H%M%SZ'
created = pd.Timestamp(pd.datetime.utcnow()).strftime(
datetime_format) # creation time Timestamp
time_start = pd.Timestamp(pd.Timestamp(
ds.Time.data[0])).strftime(datetime_format)
time_end = pd.Timestamp(pd.Timestamp(
ds.Time.data[0])).strftime(datetime_format)
global_attributes = OrderedDict([
('title', 'Rutgers Weather Research and Forecasting Model'),
('summary', 'Processed netCDF containing subset of RUWRF output'),
('keywords', 'Weather Advisories > Marine Weather/Forecast'),
('Conventions', 'CF-1.7'),
('naming_authority', 'edu.rutgers.marine.rucool'),
('history',
'10-minute WRF raw output processed into new 10-minute file with selected variables.'
), ('processing_level', 'Level 2'),
('comment', 'WRF Model operated by RUCOOL'),
('acknowledgement',
'This data is provided by the Rutgers Center for Ocean Observing Leadership. Funding is provided by the New Jersey Board of Public Utilities).'
), ('standard_name_vocabulary', 'CF Standard Name Table v41'),
('date_created', created), ('creator_name', 'Joseph Brodie'),
('creator_email', '*****@*****.**'),
('creator_url', 'rucool.marine.rutgers.edu'),
('institution',
'Center for Ocean Observing and Leadership, Department of Marine & Coastal Sciences, Rutgers University'
),
('project',
'New Jersey Board of Public Utilities - Offshore Wind Energy - RUWRF Model'
), ('geospatial_lat_min', -90), ('geospatial_lat_max', 90),
('geospatial_lon_min', -180), ('geospatial_lon_max', 180),
('geospatial_vertical_min', 0.0), ('geospatial_vertical_max', 0.0),
('geospatial_vertical_positive', 'down'),
('time_coverage_start', time_start), ('time_coverage_end', time_end),
('creator_type', 'person'),
('creator_institution', 'Rutgers University'),
('contributor_name', 'Joseph Brodie'),
('contributor_role', 'Director of Atmospheric Research'),
('geospatial_lat_units', 'degrees_north'),
('geospatial_lon_units', 'degrees_east'), ('date_modified', created),
('date_issued', created), ('date_metadata_modified', created),
('keywords_vocabulary', 'GCMD Science Keywords'),
('platform', 'WRF Model Run'), ('cdm_data_type', 'Grid'),
('references',
'http://maracoos.org/node/146 https://rucool.marine.rutgers.edu/facilities https://rucool.marine.rutgers.edu/data'
)
])
global_attributes.update(original_global_attributes)
ds = ds.assign_attrs(global_attributes)
# Add compression to all variables
encoding = {}
for k in ds.data_vars:
encoding[k] = {'zlib': True, 'complevel': 1}
# add the encoding for time so xarray exports the proper time.
# Also remove compression from dimensions. They should never have fill values
encoding['Time'] = dict(units=time_units,
calendar='gregorian',
zlib=False,
_FillValue=False,
dtype=np.double)
encoding['XLONG'] = dict(zlib=False, _FillValue=False)
encoding['XLAT'] = dict(zlib=False, _FillValue=False)
encoding['height'] = dict(zlib=False, _FillValue=False, dtype=np.int32)
encoding['pressure'] = dict(zlib=False, _FillValue=False, dtype=np.int32)
ds.to_netcdf(save_file,
encoding=encoding,
format='netCDF4',
engine='netcdf4',
unlimited_dims='Time')