def narr2suntansrad(outfile,tstart,tend,bbox,utmzone):
"""
Extraxts NARR model data and converts to a netcdf file format recognised by SUNTANS
This function is for extracting shortwave and longwave radiation
"""
# Initialise the NARR object (for all variables)
basefile = 'narr-b_221_' # These variables are stored in the NARR-B files
narr=getNARR(tstart,tend,bbox,basefile=basefile)
#Lookup table that matches variables to name in NARR file
varlookup={'Hsw_up':'Upward_short_wave_radiation_flux', 'Hsw_down':'Downward_shortwave_radiation_flux',\
'Hlw_up':'Upward_long_wave_radiation_flux','Hlw_down':'Downward_longwave_radiation_flux'}
# Set some meta variables
Nc = narr.nx*narr.ny
nctime = convertTime(narr.time)
meta={}
meta.update({'Hsw_up':{'long_name':'Upward shortwave radiation','units':'W m-2','scalefactor':1.0,'addoffset':0.0}})
meta.update({'Hsw_down':{'long_name':'Downward shortwave radiation','units':'W m-2','scalefactor':1.0,'addoffset':0.0}})
meta.update({'Hlw_up':{'long_name':'Upward longwave radiation','units':'W m-2','scalefactor':1.0,'addoffset':0.0}})
meta.update({'Hlw_down':{'long_name':'Downward longwave radiation','units':'W m-2','scalefactor':1.0,'addoffset':0.0}})
# Convert the coordinates to UTM
ll = np.hstack((np.reshape(narr.lon,(Nc,1)), np.reshape(narr.lat,(Nc,1))))
xy = ll2utm(ll,utmzone)
# Loop through each variable and store in a dictionary
output = {}
coords={}
for vv in varlookup.keys():
data = narr(varlookup[vv])
# Convert the units
data = data*meta[vv]['scalefactor']+meta[vv]['addoffset']
output[vv] = {'Data':np.reshape(data,(narr.nt,Nc))}
output[vv].update({'long_name':meta[vv]['long_name'],'units':meta[vv]['units']})
# Update the coordinates dictionary
coords['x_'+vv]=xy[:,0]
coords['y_'+vv]=xy[:,1]
coords['z_'+vv]=narr.z * np.ones((Nc,))
# Write to NetCDF
write2NC(outfile,coords,output,nctime)
def narr2suntansrad(outfile, tstart, tend, bbox, utmzone):
"""
Extraxts NARR model data and converts to a netcdf file format recognised by SUNTANS
This function is for extracting shortwave and longwave radiation
"""
# Initialise the NARR object (for all variables)
basefile = 'narr-b_221_' # These variables are stored in the NARR-B files
narr = getNARR(tstart, tend, bbox, basefile=basefile)
#Lookup table that matches variables to name in NARR file
varlookup={'Hsw_up':'Upward_short_wave_radiation_flux', 'Hsw_down':'Downward_shortwave_radiation_flux',\
'Hlw_up':'Upward_long_wave_radiation_flux','Hlw_down':'Downward_longwave_radiation_flux'}
# Set some meta variables
Nc = narr.nx * narr.ny
nctime = convertTime(narr.time)
meta = {}
meta.update({
'Hsw_up': {
'long_name': 'Upward shortwave radiation',
'units': 'W m-2',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
meta.update({
'Hsw_down': {
'long_name': 'Downward shortwave radiation',
'units': 'W m-2',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
meta.update({
'Hlw_up': {
'long_name': 'Upward longwave radiation',
'units': 'W m-2',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
meta.update({
'Hlw_down': {
'long_name': 'Downward longwave radiation',
'units': 'W m-2',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
# Convert the coordinates to UTM
ll = np.hstack((np.reshape(narr.lon,
(Nc, 1)), np.reshape(narr.lat, (Nc, 1))))
xy = ll2utm(ll, utmzone)
# Loop through each variable and store in a dictionary
output = {}
coords = {}
for vv in list(varlookup.keys()):
data = narr(varlookup[vv])
# Convert the units
data = data * meta[vv]['scalefactor'] + meta[vv]['addoffset']
output[vv] = {'Data': np.reshape(data, (narr.nt, Nc))}
output[vv].update({
'long_name': meta[vv]['long_name'],
'units': meta[vv]['units']
})
# Update the coordinates dictionary
coords['x_' + vv] = xy[:, 0]
coords['y_' + vv] = xy[:, 1]
coords['z_' + vv] = narr.z * np.ones((Nc, ))
# Write to NetCDF
write2NC(outfile, coords, output, nctime)
def narr2suntans(outfile,tstart,tend,bbox,utmzone):
"""
Extraxts NARR model data and converts to a netcdf file format recognised by SUNTANS
"""
# Initialise the NARR object (for all variables)
narr=getNARR(tstart,tend,bbox)
#Lookup table that matches variables to name in NARR file
varlookup={\
'Uwind':'u_wind_height_above_ground',\
'Vwind':'v_wind_height_above_ground',\
'Tair':'Temperature_height_above_ground',\
'Pair':'Pressure_reduced_to_MSL',\
'RH':'Relative_humidity',\
'cloud':'Total_cloud_cover',\
'rain':'Precipitation_rate',\
}
# Set some meta variables
Nc = narr.nx*narr.ny
nctime = convertTime(narr.time)
meta={}
meta.update({'Uwind':{'long_name':'Eastward wind velocity component','units':'m s-1','scalefactor':1.0,'addoffset':0.0}})
meta.update({'Vwind':{'long_name':'Northward wind velocity component','units':'m s-1','scalefactor':1.0,'addoffset':0.0}})
meta.update({'Tair':{'long_name':'Air Temperature','units':'Celsius','scalefactor':1.0,'addoffset':-273.15}})
meta.update({'Pair':{'long_name':'Air Pressure','units':'hPa','scalefactor':0.01,'addoffset':0.0}})
meta.update({'RH':{'long_name':'Relative Humidity','units':'percent','scalefactor':1.0,'addoffset':0.0}})
meta.update({'cloud':{'long_name':'Cloud cover fraction','units':'dimensionless','scalefactor':0.01,'addoffset':0.0}})
meta.update({'rain':{'long_name':'rain fall rate','units':'kg m2 s-1','scalefactor':1.0,'addoffset':0.0}})
# Convert the coordinates to UTM
ll = np.hstack((np.reshape(narr.lon,(Nc,1)), np.reshape(narr.lat,(Nc,1))))
xy = ll2utm(ll,utmzone)
# Loop through each variable and store in a dictionary
output = {}
coords={}
# Get all data
narrvars = [vv for vv in varlookup.itervalues()]
data = narr(narrvars) # This stores all of the data in a dictionary
for vv in varlookup.keys():
# Convert the units
vnarr = varlookup[vv]
data[vnarr] = data[vnarr]*meta[vv]['scalefactor']+meta[vv]['addoffset']
output[vv] = {'Data':np.reshape(data[vnarr],(narr.nt,Nc))}
output[vv].update({'long_name':meta[vv]['long_name'],'units':meta[vv]['units']})
# Update the coordinates dictionary
coords['x_'+vv]=xy[:,0]
coords['y_'+vv]=xy[:,1]
coords['z_'+vv]=narr.z * np.ones((Nc,))
# Write to NetCDF
write2NC(outfile,coords,output,nctime)
def narr2suntans(outfile, tstart, tend, bbox, utmzone):
"""
Extraxts NARR model data and converts to a netcdf file format recognised by SUNTANS
"""
# Initialise the NARR object (for all variables)
narr = getNARR(tstart, tend, bbox)
#Lookup table that matches variables to name in NARR file
varlookup={\
'Uwind':'u_wind_height_above_ground',\
'Vwind':'v_wind_height_above_ground',\
'Tair':'Temperature_height_above_ground',\
'Pair':'Pressure_reduced_to_MSL',\
'RH':'Relative_humidity',\
'cloud':'Total_cloud_cover',\
'rain':'Precipitation_rate',\
}
# Set some meta variables
Nc = narr.nx * narr.ny
nctime = convertTime(narr.time)
meta = {}
meta.update({
'Uwind': {
'long_name': 'Eastward wind velocity component',
'units': 'm s-1',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
meta.update({
'Vwind': {
'long_name': 'Northward wind velocity component',
'units': 'm s-1',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
meta.update({
'Tair': {
'long_name': 'Air Temperature',
'units': 'Celsius',
'scalefactor': 1.0,
'addoffset': -273.15
}
})
meta.update({
'Pair': {
'long_name': 'Air Pressure',
'units': 'hPa',
'scalefactor': 0.01,
'addoffset': 0.0
}
})
meta.update({
'RH': {
'long_name': 'Relative Humidity',
'units': 'percent',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
meta.update({
'cloud': {
'long_name': 'Cloud cover fraction',
'units': 'dimensionless',
'scalefactor': 0.01,
'addoffset': 0.0
}
})
meta.update({
'rain': {
'long_name': 'rain fall rate',
'units': 'kg m2 s-1',
'scalefactor': 1.0,
'addoffset': 0.0
}
})
# Convert the coordinates to UTM
ll = np.hstack((np.reshape(narr.lon,
(Nc, 1)), np.reshape(narr.lat, (Nc, 1))))
xy = ll2utm(ll, utmzone)
# Loop through each variable and store in a dictionary
output = {}
coords = {}
# Get all data
narrvars = [vv for vv in varlookup.values()]
data = narr(narrvars) # This stores all of the data in a dictionary
for vv in list(varlookup.keys()):
# Convert the units
vnarr = varlookup[vv]
data[vnarr] = data[vnarr] * meta[vv]['scalefactor'] + meta[vv][
'addoffset']
output[vv] = {'Data': np.reshape(data[vnarr], (narr.nt, Nc))}
output[vv].update({
'long_name': meta[vv]['long_name'],
'units': meta[vv]['units']
})
# Update the coordinates dictionary
coords['x_' + vv] = xy[:, 0]
coords['y_' + vv] = xy[:, 1]
coords['z_' + vv] = narr.z * np.ones((Nc, ))
# Write to NetCDF
write2NC(outfile, coords, output, nctime)
