def netCDF_empty(self, ncfile_out, stations, nc_in):
# TODO: change date type from f4 to f8 for lat and lon
'''
Creates an empty station file to hold interpolated reults. The number of
stations is defined by the variable stations, variables are determined by
the variable list passed from the gridded original netCDF.
ncfile_out: full name of the file to be created
stations: station list read with common_utils.StationListRead()
variables: variables read from netCDF handle
lev: list of pressure levels, empty is [] (default)
'''
rootgrp = netcdf_base(ncfile_out, len(stations), None,
'hours since 1980-01-01 00:00:00')
station = rootgrp["station"]
latitude = rootgrp["latitude"]
longitude = rootgrp["longitude"]
height = rootgrp["height"]
# assign station characteristics
station[:] = list(stations['station_number'])
latitude[:] = list(stations['latitude_dd'])
longitude[:] = list(stations['longitude_dd'])
height[:] = list(stations['elevation_m'])
# extra treatment for pressure level files
try:
lev = nc_in.variables['level'][:]
logger.info("Creating empty 3D file (has pressure levels)")
level = rootgrp.createDimension('level', len(lev))
level = rootgrp.createVariable('level', 'i4', ('level'))
level.long_name = 'pressure_level'
level.units = 'hPa'
level[:] = lev
except Exception:
logger.info("Creating empty 2D file (without pressure levels)")
lev = []
# remove extra variables
varlist_merra = [str_encode(x) for x in nc_in.variables.keys()]
# create and assign variables based on input file
for n, var in enumerate(varlist_merra):
if variables_skip(var):
continue
logger.debug(f"Add empty variable: {var}")
# extra treatment for pressure level files
if len(lev):
tmp = rootgrp.createVariable(var, 'f4',
('time', 'level', 'station'))
else:
tmp = rootgrp.createVariable(var, 'f4', ('time', 'station'))
tmp.long_name = str_encode(
nc_in.variables[var].long_name) # for merra2
tmp.units = str_encode(nc_in.variables[var].units)
# close the file
rootgrp.close()
logger.debug(f"Created empty netcdf file {ncfile_out}")
def levels2elevation(self, ncfile_in, ncfile_out):
"""
Linear 1D interpolation of pressure level data available for individual
stations to station elevation. Where and when stations are below the
lowest pressure level, they are assigned the value of the lowest
pressure level.
"""
# open file
ncf = nc.MFDataset(ncfile_in, 'r', aggdim='time')
height = ncf.variables['height'][:]
nt = len(ncf.variables['time'][:])
nl = len(ncf.variables['level'][:])
# list variables
varlist = [str_encode(x) for x in ncf.variables.keys()]
for V in [
'time', 'station', 'latitude', 'longitude', 'level', 'height',
'z'
]:
varlist.remove(V)
# === open and prepare output netCDF file ==============================
# dimensions: station, time
# variables: latitude(station), longitude(station), elevation(station)
# others: ...(time, station)
# stations are integer numbers
# create a file (Dataset object, also the root group).
rootgrp = netcdf_base(ncfile_out=ncfile_out,
n_stations=len(height),
n_time=nt,
time_units='hours since 1900-01-01 00:00:0.0')
rootgrp.source = 'ERA-Interim, interpolated (bi)linearly to stations'
time = rootgrp['time']
station = rootgrp['station']
latitude = rootgrp['latitude']
longitude = rootgrp['longitude']
height = rootgrp['height']
# assign base variables
time[:] = ncf.variables['time'][:]
station[:] = ncf.variables['station'][:]
latitude[:] = ncf.variables['latitude'][:]
longitude[:] = ncf.variables['longitude'][:]
height[:] = ncf.variables['height'][:]
# create and assign variables from input file
for var in varlist:
tmp = rootgrp.createVariable(var, 'f4', ('time', 'station'))
tmp.long_name = str_encode(ncf.variables[var].long_name)
tmp.units = str_encode(ncf.variables[var].units)
# add air pressure as new variable
var = 'air_pressure'
varlist.append(var)
tmp = rootgrp.createVariable(var, 'f4', ('time', 'station'))
tmp.long_name = var.encode('UTF8')
tmp.units = 'hPa'.encode('UTF8')
# end file prepation ===================================================
# loop over stations
for n, h in enumerate(height):
# convert geopotential [mbar] to height [m], shape: (time, level)
elevation = ncf.variables['z'][:, :, n] / 9.80665
# TODO: check if height of stations in data range
# difference in elevation, level directly above will be >= 0
elev_diff = elevation - h
# vector of level indices that fall directly above station.
# Apply after ravel() of data.
va = np.argmin(elev_diff + (elev_diff < 0) * 100000, axis=1)
# mask for situations where station is below lowest level
mask = va < (nl - 1)
va += np.arange(elevation.shape[0]) * elevation.shape[1]
# Vector level indices that fall directly below station.
# Apply after ravel() of data.
vb = va + mask # +1 when OK, +0 when below lowest level
wa, wb = self.calculate_weights(elev_diff, va, vb)
#loop over variables and apply interpolation weights
for v, var in enumerate(varlist):
if var == 'air_pressure':
# pressure [Pa] variable from levels, shape: (time, level)
data = np.repeat([ncf.variables['level'][:]],
len(time),
axis=0).ravel()
else:
#read data from netCDF
data = ncf.variables[var][:, :, n].ravel()
ipol = data[va] * wa + data[vb] * wb # interpolated value
rootgrp.variables[var][:, n] = ipol # assign to file
rootgrp.close()
ncf.close()
def levels2elevation(self, ncfile_in, ncfile_out):
"""
Linear 1D interpolation of pressure level data available for individual
stations to station elevation. Where and when stations are below the
lowest pressure level, they are assigned the value of the lowest
pressure level.
"""
# open file
# TODO: check the aggdim does not work
ncf = nc.MFDataset(ncfile_in, 'r', aggdim='time')
height = ncf.variables['height'][:]
nt = len(ncf.variables['time'][:])
nl = len(ncf.variables['level'][:])
# list variables
varlist = [str_encode(x) for x in ncf.variables.keys()]
for V in ['time', 'station', 'latitude', 'longitude',
'level','height','z']:
varlist.remove(V)
if self.ens:
varlist.remove('number')
# === open and prepare output netCDF file ==============================
# dimensions: station, time
# variables: latitude(station), longitude(station), elevation(station)
# others: ...(time, station)
# stations are integer numbers
# create a file (Dataset object, also the root group).
rootgrp = netcdf_base(ncfile_out, len(height), nt,
'hours since 1900-01-01 00:00:0.0', ncf)
if self.ens:
rootgrp.source = 'ERA5 10-member ensemble, interpolated (bi)linearly to stations'
else:
rootgrp.source = 'ERA5, interpolated (bi)linearly to stations'
time = rootgrp['time']
station = rootgrp['station']
latitude = rootgrp['latitude']
longitude = rootgrp['longitude']
height = rootgrp['height']
# assign base variables
time[:] = ncf.variables['time'][:]
station[:] = ncf.variables['station'][:]
latitude[:] = ncf.variables['latitude'][:]
longitude[:] = ncf.variables['longitude'][:]
height[:] = ncf.variables['height'][:]
# create and assign variables from input file
for var in varlist:
if self.ens:
tmp = rootgrp.createVariable(var,
'f4',('time','number','station'))
else:
tmp = rootgrp.createVariable(var,'f4',('time', 'station'))
tmp.long_name = str_encode(ncf.variables[var].long_name)
tmp.units = str_encode(ncf.variables[var].units)
# add air pressure as new variable
var = 'air_pressure'
varlist.append(var)
if self.ens:
tmp = rootgrp.createVariable(var,'f4',('time','number','station'))
else:
tmp = rootgrp.createVariable(var,'f4',('time','station'))
tmp.long_name = var.encode('UTF8')
tmp.units = 'hPa'.encode('UTF8')
# end file prepation ===================================================
# loop over stations
for n, h in enumerate(height):
if self.ens:
num = ncf.variables['number'][:]
for ni in num:
elevation = ncf.variables['z'][:,ni,:,n] / 9.80665
elev_diff, va, vb = self.ele_interpolate(elevation, h, nl)
wa, wb = self.calculate_weights(elev_diff, va, vb)
for v, var in enumerate(varlist):
if var == 'air_pressure':
# pressure [Pa] variable from levels, shape: (time, level)
data = np.repeat([ncf.variables['level'][:]],
len(time),axis=0).ravel()
else:
# read data from netCDF
data = ncf.variables[var][:,ni,:,n].ravel()
ipol = data[va] * wa + data[vb] * wb # interpolated value
rootgrp.variables[var][:,ni,n] = ipol # assign to file
else:
# convert geopotential [mbar] to height [m], shape: (time, level)
elevation = ncf.variables['z'][:,:,n] / 9.80665
elev_diff, va, vb = self.ele_interpolate(elevation, h, nl)
wa, wb = self.calculate_weights(elev_diff, va, vb)
# loop over variables and apply interpolation weights
for v, var in enumerate(varlist):
if var == 'air_pressure':
# pressure [Pa] variable from levels, shape: (time, level)
data = np.repeat([ncf.variables['level'][:]],
len(time),axis=0).ravel()
else:
# read data from netCDF
data = ncf.variables[var][:,:,n].ravel()
ipol = data[va] * wa + data[vb] * wb # interpolated value
rootgrp.variables[var][:,n] = ipol # assign to file
rootgrp.close()
ncf.close()