print "Sa: ", cov.shape
priorCDF_filename = 'Xa_Sa_datafile.55_levels.' + yyyymmdd + '.' + hh + '.' + type + '.cdf'
print priorCDF_filename
stop
data = Dataset(priorCDF_filename, 'w')
data.Date_created = datetime.strftime(datetime.now(), '%a %b %d %H:%M:%S %Y')
data.Version = 'get_RUCRAP_prior.py'
data.Machine_used = 'N/A'
data.LBL_HOME = climo.LBL_HOME
data.Standard_atmos = climo.Standard_atmos
data.QC_limits_T = climo.QC_limits_T
data.QC_limits_q = climo.QC_limits_q
data.Comment = 'Thermodynamic profiles from the ' + type + ' analyses.'
#Need to include the web links to the data used to produce these files
#Need to include the times used to produce this file.
data.Nsonde = str(
priors.shape[0]
) + ' profiles were included in the computation of this prior dataset.'
data.lat = aeri_lat
data.lon = aeri_lon
data.domain_size = str(2 * size) + "x" + str(2 * size)
data.grid_spacing = '13 km'
print data.Date_created
data.createDimension('height', len(all_temps.T))
data.createDimension('wnum', len(climo.variables['wnum'][:]))
data.createDimension('height2', len(mean))
def WriteNCCF(FileName,Dates,Latitudes,Longitudes,ClimPoints,DataObject,DimObject,AttrObject,GlobAttrObject):
''' Sort out the date/times to write out and time bounds '''
''' Sort out clim bounds '''
''' Sort out lat and long bounds '''
''' Convert variables using the obtained scale_factor and add_offset: stored_var=int((var-offset)/scale) '''
''' Write to file, set up given dimensions, looping through all potential variables and their attributes, and then the provided dictionary of global attributes '''
# Sort out date/times to write out
print(Dates)
TimPoints,TimBounds = MakeDaysSince(Dates['StYr'],Dates['StMon'],Dates['EdYr'],Dates['EdMon'])
nTims = len(TimPoints)
# Sort out clim bounds - paired strings
ClimBounds = np.empty((12,2),dtype='|S10')
for mm in range(12):
ClimBounds[mm,0] = str(ClimPoints[0])+'-'+str(mm+1)+'-'+str(1)
ClimBounds[mm,1] = str(ClimPoints[1])+'-'+str(mm+1)+'-'+str(MonthDays[mm])
# Sort out LatBounds and LonBounds
LatBounds = np.empty((len(Latitudes),2),dtype='float')
LonBounds = np.empty((len(Longitudes),2),dtype='float')
LatBounds[:,0] = Latitudes - ((Latitudes[1]-Latitudes[0])/2.)
LatBounds[:,1] = Latitudes + ((Latitudes[1]-Latitudes[0])/2.)
LonBounds[:,0] = Longitudes - ((Longitudes[1]-Longitudes[0])/2.)
LonBounds[:,1] = Longitudes + ((Longitudes[1]-Longitudes[0])/2.)
#pdb.set_trace()
# No need to convert float data using given scale_factor and add_offset to integers - done within writing program (packV = (V-offset)/scale
# Not sure what this does to float precision though...
# Change mdi into an integer -999 because these are stored as integers
for vv in range(len(DataObject)):
DataObject[vv][np.where(DataObject[vv] == OLDMDI)] = MDI
# Create a new netCDF file - have tried zlib=True,least_significant_digit=3 (and 1) - no difference
ncfw=Dataset(FileName,'w',format='NETCDF4_CLASSIC') # need to try NETCDF4 and also play with compression but test this first
# Write out the global attributes
if ('description' in GlobAttrObject):
ncfw.description = GlobAttrObject['description']
#print(GlobAttrObject['description'])
if ('File_created' in GlobAttrObject):
ncfw.File_created = GlobAttrObject['File_created']
if ('Title' in GlobAttrObject):
ncfw.Title = GlobAttrObject['Title']
if ('Institution' in GlobAttrObject):
ncfw.Institution = GlobAttrObject['Institution']
if ('History' in GlobAttrObject):
ncfw.History = GlobAttrObject['History']
if ('Licence' in GlobAttrObject):
ncfw.Licence = GlobAttrObject['Licence']
if ('Project' in GlobAttrObject):
ncfw.Project = GlobAttrObject['Project']
if ('Processing_level' in GlobAttrObject):
ncfw.Processing_level = GlobAttrObject['Processing_level']
if ('Acknowledgement' in GlobAttrObject):
ncfw.Acknowledgement = GlobAttrObject['Acknowledgement']
if ('Source' in GlobAttrObject):
ncfw.Source = GlobAttrObject['Source']
if ('Comment' in GlobAttrObject):
ncfw.Comment = GlobAttrObject['Comment']
if ('References' in GlobAttrObject):
ncfw.References = GlobAttrObject['References']
if ('Creator_name' in GlobAttrObject):
ncfw.Creator_name = GlobAttrObject['Creator_name']
if ('Creator_email' in GlobAttrObject):
ncfw.Creator_email = GlobAttrObject['Creator_email']
if ('Version' in GlobAttrObject):
ncfw.Version = GlobAttrObject['Version']
if ('doi' in GlobAttrObject):
ncfw.doi = GlobAttrObject['doi']
if ('Conventions' in GlobAttrObject):
ncfw.Conventions = GlobAttrObject['Conventions']
if ('netcdf_type' in GlobAttrObject):
ncfw.netcdf_type = GlobAttrObject['netcdf_type']
# Loop through and set up the dimension names and quantities
for vv in range(len(DimObject[0])):
ncfw.createDimension(DimObject[0][vv],DimObject[1][vv])
# Go through each dimension and set up the variable and attributes for that dimension if needed
for vv in range(len(DimObject)-2): # ignore first two elements of the list but count all other dictionaries
print(DimObject[vv+2]['var_name'])
# NOt 100% sure this works in a loop with overwriting
# initiate variable with name, type and dimensions
MyVar = ncfw.createVariable(DimObject[vv+2]['var_name'],DimObject[vv+2]['var_type'],DimObject[vv+2]['var_dims'])
# Apply any other attributes
if ('standard_name' in DimObject[vv+2]):
MyVar.standard_name = DimObject[vv+2]['standard_name']
if ('long_name' in DimObject[vv+2]):
MyVar.long_name = DimObject[vv+2]['long_name']
if ('units' in DimObject[vv+2]):
MyVar.units = DimObject[vv+2]['units']
if ('axis' in DimObject[vv+2]):
MyVar.axis = DimObject[vv+2]['axis']
if ('calendar' in DimObject[vv+2]):
MyVar.calendar = DimObject[vv+2]['calendar']
if ('start_year' in DimObject[vv+2]):
MyVar.start_year = DimObject[vv+2]['start_year']
if ('end_year' in DimObject[vv+2]):
MyVar.end_year = DimObject[vv+2]['end_year']
if ('start_month' in DimObject[vv+2]):
MyVar.start_month = DimObject[vv+2]['start_month']
if ('end_month' in DimObject[vv+2]):
MyVar.end_month = DimObject[vv+2]['end_month']
if ('bounds' in DimObject[vv+2]):
MyVar.bounds = DimObject[vv+2]['bounds']
if ('climatology' in DimObject[vv+2]):
MyVar.climatology = DimObject[vv+2]['climatology']
if ('point_spacing' in DimObject[vv+2]):
MyVar.point_spacing = DimObject[vv+2]['point_spacing']
# Provide the data to the variable
if (DimObject[vv+2]['var_name'] == 'time'):
MyVar[:] = TimPoints
if (DimObject[vv+2]['var_name'] == 'bounds_time'):
MyVar[:,:] = TimBounds
if (DimObject[vv+2]['var_name'] == 'month'):
for mm in range(12):
MyVar[mm,:] = stringtoarr(MonthName[mm],10)
if (DimObject[vv+2]['var_name'] == 'climbounds'):
for mm in range(12):
MyVar[mm,0,:] = stringtoarr(ClimBounds[mm,0],10)
MyVar[mm,1,:] = stringtoarr(ClimBounds[mm,1],10)
if (DimObject[vv+2]['var_name'] == 'latitude'):
MyVar[:] = Latitudes
if (DimObject[vv+2]['var_name'] == 'bounds_lat'):
MyVar[:,:] = LatBounds
if (DimObject[vv+2]['var_name'] == 'longitude'):
MyVar[:] = Longitudes
if (DimObject[vv+2]['var_name'] == 'bounds_lon'):
MyVar[:,:] = LonBounds
# Go through each variable and set up the variable attributes
for vv in range(len(AttrObject)): # ignore first two elements of the list but count all other dictionaries
print(AttrObject[vv]['var_name'])
# NOt 100% sure this works in a loop with overwriting
# initiate variable with name, type and dimensions
MyVar = ncfw.createVariable(AttrObject[vv]['var_name'],AttrObject[vv]['var_type'],AttrObject[vv]['var_dims'],fill_value = AttrObject[vv]['_FillValue'])
# Apply any other attributes
if ('standard_name' in AttrObject[vv]):
MyVar.standard_name = AttrObject[vv]['standard_name']
if ('long_name' in AttrObject[vv]):
MyVar.long_name = AttrObject[vv]['long_name']
if ('cell_methods' in AttrObject[vv]):
MyVar.cell_methods = AttrObject[vv]['cell_methods']
if ('comment' in AttrObject[vv]):
MyVar.comment = AttrObject[vv]['comment']
if ('units' in AttrObject[vv]):
MyVar.units = AttrObject[vv]['units']
if ('axis' in AttrObject[vv]):
MyVar.axis = AttrObject[vv]['axis']
if ('add_offset' in AttrObject[vv]):
MyVar.add_offset = AttrObject[vv]['add_offset']
if ('scale_factor' in AttrObject[vv]):
MyVar.scale_factor = AttrObject[vv]['scale_factor']
if ('valid_min' in AttrObject[vv]):
MyVar.valid_min = AttrObject[vv]['valid_min']
if ('valid_max' in AttrObject[vv]):
MyVar.valid_max = AttrObject[vv]['valid_max']
if ('missing_value' in AttrObject[vv]):
MyVar.missing_value = AttrObject[vv]['missing_value']
# if ('_FillValue' in AttrObject[vv]):
# MyVar._FillValue = AttrObject[vv]['_FillValue']
if ('reference_period' in AttrObject[vv]):
MyVar.reference_period = AttrObject[vv]['reference_period']
if ('ancillary_variables' in AttrObject[vv]):
MyVar.ancillary_variables = AttrObject[vv]['ancillary_variables']
# Provide the data to the variable - depending on howmany dimensions there are
if (len(AttrObject[vv]['var_dims']) == 1):
MyVar[:] = DataObject[vv]
if (len(AttrObject[vv]['var_dims']) == 2):
MyVar[:,:] = DataObject[vv]
if (len(AttrObject[vv]['var_dims']) == 3):
MyVar[:,:,:] = DataObject[vv]
ncfw.close()
return # WriteNCCF
def WriteNCCF(FileName, Dates, Latitudes, Longitudes, ClimPoints, DataObject,
DimObject, AttrObject, GlobAttrObject):
''' Sort out the date/times to write out and time bounds '''
''' Sort out clim bounds '''
''' Sort out lat and long bounds '''
''' Convert variables using the obtained scale_factor and add_offset: stored_var=int((var-offset)/scale) '''
''' Write to file, set up given dimensions, looping through all potential variables and their attributes, and then the provided dictionary of global attributes '''
# Sort out date/times to write out
print(Dates)
TimPoints, TimBounds = MakeDaysSince(Dates['StYr'], Dates['StMon'],
Dates['EdYr'], Dates['EdMon'])
nTims = len(TimPoints)
# Sort out clim bounds - paired strings
ClimBounds = np.empty((12, 2), dtype='|S10')
for mm in range(12):
ClimBounds[mm,
0] = str(ClimPoints[0]) + '-' + str(mm + 1) + '-' + str(1)
ClimBounds[mm, 1] = str(ClimPoints[1]) + '-' + str(mm + 1) + '-' + str(
MonthDays[mm])
# Sort out LatBounds and LonBounds
LatBounds = np.empty((len(Latitudes), 2), dtype='float')
LonBounds = np.empty((len(Longitudes), 2), dtype='float')
LatBounds[:, 0] = Latitudes - ((Latitudes[1] - Latitudes[0]) / 2.)
LatBounds[:, 1] = Latitudes + ((Latitudes[1] - Latitudes[0]) / 2.)
LonBounds[:, 0] = Longitudes - ((Longitudes[1] - Longitudes[0]) / 2.)
LonBounds[:, 1] = Longitudes + ((Longitudes[1] - Longitudes[0]) / 2.)
#pdb.set_trace()
# # No need to convert float data using given scale_factor and add_offset to integers - done within writing program (packV = (V-offset)/scale
# # Not sure what this does to float precision though...
# # Change mdi into an integer -999 because these are stored as integers
# for vv in range(len(DataObject)):
# DataObject[vv][np.where(DataObject[vv] == OLDMDI)] = MDI
# Create a new netCDF file - have tried zlib=True,least_significant_digit=3 (and 1) - no difference
ncfw = Dataset(
FileName, 'w', format='NETCDF4_CLASSIC'
) # need to try NETCDF4 and also play with compression but test this first
# Write out the global attributes
if ('description' in GlobAttrObject):
ncfw.description = GlobAttrObject['description']
#print(GlobAttrObject['description'])
if ('File_created' in GlobAttrObject):
ncfw.File_created = GlobAttrObject['File_created']
if ('Title' in GlobAttrObject):
ncfw.Title = GlobAttrObject['Title']
if ('Institution' in GlobAttrObject):
ncfw.Institution = GlobAttrObject['Institution']
if ('History' in GlobAttrObject):
ncfw.History = GlobAttrObject['History']
if ('Licence' in GlobAttrObject):
ncfw.Licence = GlobAttrObject['Licence']
if ('Project' in GlobAttrObject):
ncfw.Project = GlobAttrObject['Project']
if ('Processing_level' in GlobAttrObject):
ncfw.Processing_level = GlobAttrObject['Processing_level']
if ('Acknowledgement' in GlobAttrObject):
ncfw.Acknowledgement = GlobAttrObject['Acknowledgement']
if ('Source' in GlobAttrObject):
ncfw.Source = GlobAttrObject['Source']
if ('Comment' in GlobAttrObject):
ncfw.Comment = GlobAttrObject['Comment']
if ('References' in GlobAttrObject):
ncfw.References = GlobAttrObject['References']
if ('Creator_name' in GlobAttrObject):
ncfw.Creator_name = GlobAttrObject['Creator_name']
if ('Creator_email' in GlobAttrObject):
ncfw.Creator_email = GlobAttrObject['Creator_email']
if ('Version' in GlobAttrObject):
ncfw.Version = GlobAttrObject['Version']
if ('doi' in GlobAttrObject):
ncfw.doi = GlobAttrObject['doi']
if ('Conventions' in GlobAttrObject):
ncfw.Conventions = GlobAttrObject['Conventions']
if ('netcdf_type' in GlobAttrObject):
ncfw.netcdf_type = GlobAttrObject['netcdf_type']
# Loop through and set up the dimension names and quantities
for vv in range(len(DimObject[0])):
ncfw.createDimension(DimObject[0][vv], DimObject[1][vv])
# Go through each dimension and set up the variable and attributes for that dimension if needed
for vv in range(
len(DimObject) - 2
): # ignore first two elements of the list but count all other dictionaries
print(DimObject[vv + 2]['var_name'])
# NOt 100% sure this works in a loop with overwriting
# initiate variable with name, type and dimensions
MyVar = ncfw.createVariable(DimObject[vv + 2]['var_name'],
DimObject[vv + 2]['var_type'],
DimObject[vv + 2]['var_dims'])
# Apply any other attributes
if ('standard_name' in DimObject[vv + 2]):
MyVar.standard_name = DimObject[vv + 2]['standard_name']
if ('long_name' in DimObject[vv + 2]):
MyVar.long_name = DimObject[vv + 2]['long_name']
if ('units' in DimObject[vv + 2]):
MyVar.units = DimObject[vv + 2]['units']
if ('axis' in DimObject[vv + 2]):
MyVar.axis = DimObject[vv + 2]['axis']
if ('calendar' in DimObject[vv + 2]):
MyVar.calendar = DimObject[vv + 2]['calendar']
if ('start_year' in DimObject[vv + 2]):
MyVar.start_year = DimObject[vv + 2]['start_year']
if ('end_year' in DimObject[vv + 2]):
MyVar.end_year = DimObject[vv + 2]['end_year']
if ('start_month' in DimObject[vv + 2]):
MyVar.start_month = DimObject[vv + 2]['start_month']
if ('end_month' in DimObject[vv + 2]):
MyVar.end_month = DimObject[vv + 2]['end_month']
if ('bounds' in DimObject[vv + 2]):
MyVar.bounds = DimObject[vv + 2]['bounds']
if ('climatology' in DimObject[vv + 2]):
MyVar.climatology = DimObject[vv + 2]['climatology']
if ('point_spacing' in DimObject[vv + 2]):
MyVar.point_spacing = DimObject[vv + 2]['point_spacing']
# Provide the data to the variable
if (DimObject[vv + 2]['var_name'] == 'time'):
MyVar[:] = TimPoints
if (DimObject[vv + 2]['var_name'] == 'bounds_time'):
MyVar[:, :] = TimBounds
if (DimObject[vv + 2]['var_name'] == 'month'):
for mm in range(12):
MyVar[mm, :] = stringtoarr(MonthName[mm], 10)
if (DimObject[vv + 2]['var_name'] == 'climbounds'):
for mm in range(12):
MyVar[mm, 0, :] = stringtoarr(ClimBounds[mm, 0], 10)
MyVar[mm, 1, :] = stringtoarr(ClimBounds[mm, 1], 10)
if (DimObject[vv + 2]['var_name'] == 'latitude'):
MyVar[:] = Latitudes
if (DimObject[vv + 2]['var_name'] == 'bounds_lat'):
MyVar[:, :] = LatBounds
if (DimObject[vv + 2]['var_name'] == 'longitude'):
MyVar[:] = Longitudes
if (DimObject[vv + 2]['var_name'] == 'bounds_lon'):
MyVar[:, :] = LonBounds
# Go through each variable and set up the variable attributes
for vv in range(
len(AttrObject)
): # ignore first two elements of the list but count all other dictionaries
print(AttrObject[vv]['var_name'])
# NOt 100% sure this works in a loop with overwriting
# initiate variable with name, type and dimensions
MyVar = ncfw.createVariable(AttrObject[vv]['var_name'],
AttrObject[vv]['var_type'],
AttrObject[vv]['var_dims'],
zlib=True,
fill_value=AttrObject[vv]['_FillValue'])
# Apply any other attributes
if ('standard_name' in AttrObject[vv]):
MyVar.standard_name = AttrObject[vv]['standard_name']
if ('long_name' in AttrObject[vv]):
MyVar.long_name = AttrObject[vv]['long_name']
# Too many issues with CF compliance
# if ('cell_methods' in AttrObject[vv]):
# MyVar.cell_methods = AttrObject[vv]['cell_methods']
if ('comment' in AttrObject[vv]):
MyVar.comment = AttrObject[vv]['comment']
if ('units' in AttrObject[vv]):
MyVar.units = AttrObject[vv]['units']
if ('axis' in AttrObject[vv]):
MyVar.axis = AttrObject[vv]['axis']
# if ('add_offset' in AttrObject[vv]):
# MyVar.add_offset = AttrObject[vv]['add_offset']
#
# if ('scale_factor' in AttrObject[vv]):
# MyVar.scale_factor = AttrObject[vv]['scale_factor']
# if ('valid_min' in AttrObject[vv]):
# MyVar.valid_min = AttrObject[vv]['valid_min']#
#
# if ('valid_max' in AttrObject[vv]):
# MyVar.valid_max = AttrObject[vv]['valid_max']
# if ('missing_value' in AttrObject[vv]):
# MyVar.missing_value = AttrObject[vv]['missing_value']
# if ('_FillValue' in AttrObject[vv]):
# MyVar._FillValue = AttrObject[vv]['_FillValue']
if ('reference_period' in AttrObject[vv]):
MyVar.reference_period = AttrObject[vv]['reference_period']
if ('ancillary_variables' in AttrObject[vv]):
MyVar.ancillary_variables = AttrObject[vv]['ancillary_variables']
# Provide the data to the variable - depending on howmany dimensions there are
if (len(AttrObject[vv]['var_dims']) == 1):
MyVar[:] = DataObject[vv]
if (len(AttrObject[vv]['var_dims']) == 2):
MyVar[:, :] = DataObject[vv]
if (len(AttrObject[vv]['var_dims']) == 3):
MyVar[:, :, :] = DataObject[vv]
ncfw.close()
return # WriteNCCF
def makeFile(mean, cov, dir, types, yyyymmdd, hh, paths, climo, size, t_size, n, aeri_lat, aeri_lon):
priorCDF_filename = dir.strip() + '/Xa_Sa_datafile.55_levels.' + yyyymmdd + '.' + hh + '.' + types[0] + '.' + str(aeri_lat) + '.' + str(aeri_lon) + '.cdf'
print "Saving prior file as: " + priorCDF_filename
data = Dataset(priorCDF_filename, 'w', 'NETCDF3_CLASSIC')
data.Date_created = datetime.strftime(datetime.now(), '%a %b %d %H:%M:%S %Y')
data.Version = 'get_model_prior.py'
data.Machine_used = platform.platform()
data.model = "RUC/RAP"
data.LBL_HOME = climo.LBL_HOME
data.Standard_atmos = climo.Standard_atmos
data.QC_limits_T = climo.QC_limits_T
data.QC_limits_q = climo.QC_limits_q
data.Comment = "Prior generated using model (" + types[0] + ") data."
#Need to include the web links to the data used to produce these files
#Need to include the times used to produce this file.
data.Nsonde = str(n) + ' profiles were included in the computation of this prior dataset.'
data.lat = aeri_lat
data.lon = aeri_lon
data.paths = '; '.join(paths)
data.model_types = '; '.join(types)
data.domain_size = str(2*size) + "x" + str(2*size)
data.temporal_size = t_size
data.grid_spacing = '13 km'
print "Prior generation took place at: ", data.Date_created
data.createDimension('height', len(mean)/2)
data.createDimension('wnum', len(climo.variables['wnum'][:]))
data.createDimension('height2', len(mean))
var = data.createVariable('mean_pressure', 'f4', ('height',))
var[:] = climo.variables['mean_pressure'][:]
var.units = climo.variables['mean_pressure'].units
var.long_name = climo.variables['mean_pressure'].long_name
var = data.createVariable('height', 'f4', ('height',))
var[:] = climo.variables['height'][:]
var.units = climo.variables['height'].units
var.long_name = climo.variables['height'].long_name
var = data.createVariable('mean_temperature', 'f4', ('height',))
var[:] = mean[:55]
var.units = climo.variables['mean_temperature'].units
var.long_name = climo.variables['mean_temperature'].long_name
var = data.createVariable('mean_mixingratio', 'f4', ('height',))
var[:] = mean[55:]
var.units = climo.variables['mean_mixingratio'].units
var.long_name = climo.variables['mean_mixingratio'].long_name
var = data.createVariable('height2', 'f4', ('height2',))
var[:] = climo.variables['height2'][:]
var.units = climo.variables['height2'].units
var.long_name = climo.variables['height2'].long_name
var = data.createVariable('wnum', 'f4', ('wnum',))
var[:] = climo.variables['wnum'][:]
var.units = climo.variables['wnum'].units
var.long_name = climo.variables['wnum'].long_name
var = data.createVariable('delta_od', 'f4', ('wnum',))
var[:] = climo.variables['delta_od'][:]
var.units = climo.variables['delta_od'].units
var.long_name = climo.variables['delta_od'].long_name
var = data.createVariable('radiance_true', 'f4', ('wnum',))
var[:] = climo.variables['radiance_true'][:]
var.units = climo.variables['radiance_true'].units
var.long_name = climo.variables['radiance_true'].long_name
var = data.createVariable('radiance_fast', 'f4', ('wnum',))
var[:] = climo.variables['radiance_fast'][:]
var.units = climo.variables['radiance_fast'].units
var.long_name = climo.variables['radiance_fast'].long_name
var = data.createVariable('mean_prior', 'f4', ('height2',))
var[:] = mean
var.units = climo.variables['mean_prior'].units
var.long_name = climo.variables['mean_prior'].long_name
var = data.createVariable('covariance_prior', 'f4', ('height2','height2',))
var[:] = cov
var.units = climo.variables['covariance_prior'].units
var.long_name = climo.variables['covariance_prior'].long_name
climo.close()
return priorCDF_filename
def write_CRTM_GOCART(dimensions,
variables,
output_gocart,
Release=3,
Version=1):
[
n_Wavelengths, n_Radii, n_Sigma, n_Types, n_RH, n_Legendre_Terms,
n_Phase_Elements, tnsl
] = dimensions
[
Aerosol_Type, Aerosol_Type_Name, Wavelength, Reff, Reff_RH, Rsig, RH,
ke, w, g, pcoeff, growthf
] = variables
# Unit conversion
Reff = Reff * 1e6
Reff_RH = Reff_RH * 1e6
Wavelength = Wavelength * 1e6
# OUTPUT interpolated data to a nc4 file
print('write to: ' + output_gocart)
w_nc_fid = Dataset(output_gocart, 'w', format='NETCDF4')
# write dimensions
w_nc_fid.createDimension('n_Wavelengths', n_Wavelengths)
w_nc_fid.createDimension('n_Radii', n_Radii)
w_nc_fid.createDimension('n_Sigma', n_Sigma)
w_nc_fid.createDimension('n_Types', n_Types)
w_nc_fid.createDimension('n_RH', n_RH)
w_nc_fid.createDimension('n_Legendre_Terms', n_Legendre_Terms)
w_nc_fid.createDimension('n_Phase_Elements', n_Phase_Elements)
w_nc_fid.createDimension('tnsl', tnsl)
# create varaibles
Aerosol_Type_nc = w_nc_fid.createVariable('Aerosol_Type', 'i4',
('n_Types', ))
Aerosol_Type_Name_nc = w_nc_fid.createVariable('Aerosol_Type_Name', 'S1',
('n_Types', 'tnsl'))
Wavelength_nc = w_nc_fid.createVariable('Wavelength', 'f8',
('n_Wavelengths', ))
Reff_nc = w_nc_fid.createVariable('Reff',
'f8', ('n_Types', 'n_Radii'),
fill_value=0.0)
Rsig_nc = w_nc_fid.createVariable('Rsig', 'f8', ('n_Types', 'n_Sigma'))
RH_nc = w_nc_fid.createVariable('RH', 'f8', ('n_RH', ))
ke_nc = w_nc_fid.createVariable(
'ke', 'f8', ('n_Types', 'n_Sigma', 'n_Radii', 'n_RH', 'n_Wavelengths'))
w_nc = w_nc_fid.createVariable(
'w', 'f8', ('n_Types', 'n_Sigma', 'n_Radii', 'n_RH', 'n_Wavelengths'))
g_nc = w_nc_fid.createVariable(
'g', 'f8', ('n_Types', 'n_Sigma', 'n_Radii', 'n_RH', 'n_Wavelengths'))
pcoeff_nc = w_nc_fid.createVariable(
'pcoeff', 'f8', ('n_Phase_Elements', 'n_Legendre_Terms', 'n_Types',
'n_Sigma', 'n_Radii', 'n_RH', 'n_Wavelengths'))
Reff_RH_nc = w_nc_fid.createVariable('Reff_RH',
'f8', ('n_Types', 'n_Radii', 'n_RH'),
fill_value=0.0)
growth_factor_nc = w_nc_fid.createVariable('growth_factor',
'f8',
('n_Types', 'n_Radii', 'n_RH'),
fill_value=0.0)
# write attributes
nc_fid = Dataset('AerosolCoeff.nc4', 'r')
for ncattr in nc_fid.variables['Aerosol_Type'].ncattrs():
Aerosol_Type_nc.setncattr(
ncattr, nc_fid.variables['Aerosol_Type'].getncattr(ncattr))
for ncattr in nc_fid.variables['Aerosol_Type_Name'].ncattrs():
Aerosol_Type_Name_nc.setncattr(
ncattr, nc_fid.variables['Aerosol_Type_Name'].getncattr(ncattr))
for ncattr in nc_fid.variables['Wavelength'].ncattrs():
Wavelength_nc.setncattr(
ncattr, nc_fid.variables['Wavelength'].getncattr(ncattr))
for ncattr in nc_fid.variables['Reff'].ncattrs():
Reff_nc.setncattr(ncattr, nc_fid.variables['Reff'].getncattr(ncattr))
for ncattr in nc_fid.variables['Rsig'].ncattrs():
Rsig_nc.setncattr(ncattr, nc_fid.variables['Rsig'].getncattr(ncattr))
for ncattr in nc_fid.variables['RH'].ncattrs():
RH_nc.setncattr(ncattr, nc_fid.variables['RH'].getncattr(ncattr))
for ncattr in nc_fid.variables['ke'].ncattrs():
ke_nc.setncattr(ncattr, nc_fid.variables['ke'].getncattr(ncattr))
for ncattr in nc_fid.variables['w'].ncattrs():
w_nc.setncattr(ncattr, nc_fid.variables['w'].getncattr(ncattr))
for ncattr in nc_fid.variables['g'].ncattrs():
g_nc.setncattr(ncattr, nc_fid.variables['g'].getncattr(ncattr))
for ncattr in nc_fid.variables['pcoeff'].ncattrs():
pcoeff_nc.setncattr(ncattr,
nc_fid.variables['pcoeff'].getncattr(ncattr))
w_nc_fid.variables[
'Reff'].long_name = 'dry particle effective radius of sizebin'
w_nc_fid.variables['Reff'].units = 'Microns (um)'
w_nc_fid.variables['Reff_RH'].long_name = 'effective radius of size bin'
w_nc_fid.variables['Reff_RH'].units = 'Microns (um)'
w_nc_fid.variables[
'growth_factor'].long_name = 'growth factor = ratio of wet to dry particle radius'
w_nc_fid.variables['growth_factor'].units = "fraction"
nc_fid.close()
# write variables
w_nc_fid.variables['Aerosol_Type'][:] = Aerosol_Type
w_nc_fid.variables['Aerosol_Type_Name'][:] = Aerosol_Type_Name
w_nc_fid.variables['Wavelength'][:] = Wavelength
w_nc_fid.variables['Reff'][:] = Reff
w_nc_fid.variables['Reff_RH'][:] = Reff_RH
w_nc_fid.variables['Rsig'][:] = Rsig
w_nc_fid.variables['RH'][:] = RH
w_nc_fid.variables['ke'][:] = ke
w_nc_fid.variables['w'][:] = w
w_nc_fid.variables['ke'][:] = ke
w_nc_fid.variables['g'][:] = g
w_nc_fid.variables['pcoeff'][:] = pcoeff
w_nc_fid.variables['growth_factor'][:] = growthf
# write global variables
w_nc_fid.Release = np.int32(Release)
w_nc_fid.Version = np.int32(Version)
w_nc_fid.Data_Source = "GOCART-GEOS5"
w_nc_fid.Title = "Aerosol Optical Properties in the infrared and visible spectral region."
w_nc_fid.History = "Combine GOCART LUTs (Cheng DAND, 202011）"
w_nc_fid.Comment = "Based on GOCART LUTs: optics_DU.v15_6.nc, optics_DU.v15_6.nc, optics_NI.v2_6.nc, optics_OC.v1_6.nc, optics_SS.v3_6.nc, optics_SU.v1_6.nc, with delta fit applied"
w_nc_fid.close()
type = "ARM.RUCRAP"
priorCDF_filename = 'Xa_Sa_datafile.55_levels.' + yyyymmdd + '.' + hh + '.' + type + '.cdf'
print priorCDF_filename
stop
data = Dataset(priorCDF_filename, 'w')
data.Date_created = datetime.strftime(datetime.now(), '%a %b %d %H:%M:%S %Y')
data.Version = 'get_RUCRAP_prior.py'
data.Machine_used = 'N/A'
data.LBL_HOME = climo.LBL_HOME
data.Standard_atmos = climo.Standard_atmos
data.QC_limits_T = climo.QC_limits_T
data.QC_limits_q = climo.QC_limits_q
data.Comment = 'Thermodynamic profiles from the ' + type + ' analyses.'
#Need to include the web links to the data used to produce these files
#Need to include the times used to produce this file.
data.Nsonde = str(priors.shape[0]) + ' profiles were included in the computation of this prior dataset.'
data.lat = aeri_lat
data.lon = aeri_lon
data.domain_size = str(2*size) + "x" + str(2*size)
data.grid_spacing = '13 km'
print data.Date_created
data.createDimension('height', len(all_temps.T))
data.createDimension('wnum', len(climo.variables['wnum'][:]))
data.createDimension('height2', len(mean))
var = data.createVariable('mean_pressure', 'f4', ('height',))
def makeFile(mean, cov, dir, types, yyyymmdd, hh, paths, climo, size, t_size,
n, aeri_lat, aeri_lon):
priorCDF_filename = dir.strip(
) + '/Xa_Sa_datafile.55_levels.' + yyyymmdd + '.' + hh + '.' + types[
0] + '.' + str(aeri_lat) + '.' + str(aeri_lon) + '.cdf'
print "Saving prior file as: " + priorCDF_filename
data = Dataset(priorCDF_filename, 'w', 'NETCDF3_CLASSIC')
data.Date_created = datetime.strftime(datetime.now(),
'%a %b %d %H:%M:%S %Y')
data.Version = 'get_model_prior.py'
data.Machine_used = platform.platform()
data.model = "RUC/RAP"
data.LBL_HOME = climo.LBL_HOME
data.Standard_atmos = climo.Standard_atmos
data.QC_limits_T = climo.QC_limits_T
data.QC_limits_q = climo.QC_limits_q
data.Comment = "Prior generated using model (" + types[0] + ") data."
#Need to include the web links to the data used to produce these files
#Need to include the times used to produce this file.
data.Nsonde = str(
n
) + ' profiles were included in the computation of this prior dataset.'
data.lat = aeri_lat
data.lon = aeri_lon
data.paths = '; '.join(paths)
data.model_types = '; '.join(types)
data.domain_size = str(2 * size) + "x" + str(2 * size)
data.temporal_size = t_size
data.grid_spacing = '13 km'
print "Prior generation took place at: ", data.Date_created
data.createDimension('height', len(mean) / 2)
data.createDimension('wnum', len(climo.variables['wnum'][:]))
data.createDimension('height2', len(mean))
var = data.createVariable('mean_pressure', 'f4', ('height', ))
var[:] = climo.variables['mean_pressure'][:]
var.units = climo.variables['mean_pressure'].units
var.long_name = climo.variables['mean_pressure'].long_name
var = data.createVariable('height', 'f4', ('height', ))
var[:] = climo.variables['height'][:]
var.units = climo.variables['height'].units
var.long_name = climo.variables['height'].long_name
var = data.createVariable('mean_temperature', 'f4', ('height', ))
var[:] = mean[:55]
var.units = climo.variables['mean_temperature'].units
var.long_name = climo.variables['mean_temperature'].long_name
var = data.createVariable('mean_mixingratio', 'f4', ('height', ))
var[:] = mean[55:]
var.units = climo.variables['mean_mixingratio'].units
var.long_name = climo.variables['mean_mixingratio'].long_name
var = data.createVariable('height2', 'f4', ('height2', ))
var[:] = climo.variables['height2'][:]
var.units = climo.variables['height2'].units
var.long_name = climo.variables['height2'].long_name
var = data.createVariable('wnum', 'f4', ('wnum', ))
var[:] = climo.variables['wnum'][:]
var.units = climo.variables['wnum'].units
var.long_name = climo.variables['wnum'].long_name
var = data.createVariable('delta_od', 'f4', ('wnum', ))
var[:] = climo.variables['delta_od'][:]
var.units = climo.variables['delta_od'].units
var.long_name = climo.variables['delta_od'].long_name
var = data.createVariable('radiance_true', 'f4', ('wnum', ))
var[:] = climo.variables['radiance_true'][:]
var.units = climo.variables['radiance_true'].units
var.long_name = climo.variables['radiance_true'].long_name
var = data.createVariable('radiance_fast', 'f4', ('wnum', ))
var[:] = climo.variables['radiance_fast'][:]
var.units = climo.variables['radiance_fast'].units
var.long_name = climo.variables['radiance_fast'].long_name
var = data.createVariable('mean_prior', 'f4', ('height2', ))
var[:] = mean
var.units = climo.variables['mean_prior'].units
var.long_name = climo.variables['mean_prior'].long_name
var = data.createVariable('covariance_prior', 'f4', (
'height2',
'height2',
))
var[:] = cov
var.units = climo.variables['covariance_prior'].units
var.long_name = climo.variables['covariance_prior'].long_name
climo.close()
return priorCDF_filename
