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compute_deltas_HCLIM_grib.py
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import numpy as np
import os
import yaml
import pandas as pd
import math
import sys
from netCDF4 import Dataset
from tqdm import tqdm
from yaml.loader import SafeLoader
# ------------------------------------------------------------------------------
def is_year_leap(year):
"""
Check if a specific year is leap
Parameters:
year (int) : year of interest
Returns:
Logical : True (False) for leap (non-leap year)
"""
if (year % 400 == 0) and (year % 100 == 0):
return True
elif (year % 4 ==0) and (year % 100 != 0):
return True
else:
return False
def read_namelist(filename):
"""
Read yaml namelist file
Parameters:
filename (str) : path to the namelist file
Returns:
dictionary : dictionary with all the namelist information
"""
with open(filename) as f:
namelist = yaml.load(f, Loader=SafeLoader)
return namelist
def read_file_pandas(filename, col_list, names_list):
"""
Read data from file and transferred it into a dataframe
Parameters:
filename (str) : path to the file of interest
col_list (list of int) : columns to be extracted
names_list(list of str) : names for the columns
Returns:
dataframe : extracted information
"""
df = pd.read_csv(filename, header=None, skipinitialspace = True,
usecols=col_list, names=names_list, sep=" ")
return df
def read_SWL_info(namelist,caselist):
"""
Read specific warming level information from files
Parameters:
namelist (dict): dictionary containing paths and names
caselist (str) : SWL case
Returns:
dictionary : dictionary containing SWL information
"""
dir_file = namelist['SWL_info_dir']
SWL_info = {}
for case in caselist:
name_file = namelist[case]['info_file']
df = read_file_pandas(dir_file+name_file, [2], ['year'])
SWL_info[case] = df.year.values
return SWL_info
def create_workdir(namelist, member=None, cases=None):
"""
Create work directories
Parameters:
namelist (dict): dictionary containing paths and names
member (int) : member number
cases (str) : PGW information
"""
if member == None:
os.system('mkdir -p '+namelist['output_dir']+'tmpDIR')
else:
for case in cases:
os.system('mkdir -p '+namelist['output_dir']+'HCLIM_grib/'+case+'/m'+str(member+101))
def remove_workdir(namelist):
"""
Remove working directory
Parameters:
namelist (dict): dictionary containing path information
"""
os.system('rm -rf '+namelist['output_dir']+'tmpDIR')
def clean_workdir(namelist,member):
"""
Clean working directory by removing all the temporary netCDF files
Parameters:
namelist (dict): dictionary containing path information
"""
os.system('rm -rf '+namelist['output_dir']+'tmpDIR/*m'+str(member+101)+'*')
def extract_LESM_files(namelist, SWL_info, member):
"""
Extract LESM files by remapping to a new domain and save them into the member file.
Remove the 29th February from leap years.
Parameters:
namelist (dict): dictionary containing paths, names and variables names
SWL_info (dict): SWL information
member (int) : member information
"""
main_path = namelist['main_dir']
yearshift = namelist['PGW_years']
# Join all variables except last one
varlist = namelist['varnames_sub'][:]
varlist.remove('var134')
varlist.remove('var157')
listvars = ','.join(varlist)
for case in ['SWL09']: #SWL_info.keys():
yearref=SWL_info[case][member]
yearlist=np.arange(yearref-yearshift+1,yearref+1)
str1 = main_path.replace("CASECASE", str(namelist[case]['case']))
str2 = str1.replace('EXPEXP', namelist[case]['exp'])
path_files = str2.replace('MMM',str(101+member))
for year in yearlist:
for month in range(1,13):
os.system('cdo -s -daymean -remapbil,'+namelist['grid_info']+' -selvar,'+listvars+' -mergetime '+path_files+str(year)+str(month).zfill(2)+'* '
+namelist['output_dir']+'tmpDIR/m'+str(101+member)+str(year)+str(month).zfill(2)+case+'.grb')
os.system('cdo -s mergetime -del29feb '+namelist['output_dir']+'tmpDIR/m'+str(101+member)+'*'+case+'* '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member))
def delta_substract_LESM(namelist, SWL_info, member):
"""
Compute PGW deltas by substracting two SWL
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
member (int) : member number
"""
case0 = list(SWL_info.keys())[0]
for case in list(list(SWL_info.keys())[1:]):
for varname in namelist['varnames_sub']:
if varname == 'var152': continue
if varname == 'var133': continue
os.system('cdo -s sub '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_'+varname+'.grb '
+namelist['output_dir']+'tmpDIR/'+case0+'_m'+str(101+member)+'_'+varname+'.grb '
+namelist['output_dir']+'tmpDIR/'+case+'-'+case0+'_m'+str(101+member)+'_'+varname+'.grb ')
def delta_archive(namelist, SWL_info, member):
"""
Archive PGW delta information to a specific member folder
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
member (int) : member number
"""
case0 = list(SWL_info.keys())[0]
for case in list(list(SWL_info.keys())[1:]):
for varname in namelist['varnames_sub']:
if varname == 'var152': continue
if varname == 'var133': continue
os.system('cdo -s -O ydaymean -del29feb '+namelist['output_dir']+'tmpDIR/'+case+'-'+case0+'_m'+str(101+member)+'_'+varname+'.grb '
+namelist['output_dir']+'HCLIM_grib/PGW'+case[3]+'/m'+str(member+101)+'/'+varname+'_'+str(101+member)
+'_'+case+'-'+case0+'.grb')
def separate_LESM_files_variables(namelist, SWL_info, member):
"""
Separate grib files into a file per variable
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
member (int) : member number
"""
for case in SWL_info.keys():
os.system('cdo -s splitname '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+' '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_')
def transform_lnsp2sp_LESM_files(namelist, SWL_info, member):
"""
Transform logarithm of surface pressure into surface pressure file
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
member (int) : member number
"""
for case in SWL_info.keys():
os.system('cdo -s expr,"var134=exp(var152)" '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var152.grb '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var134.grb.tmp ')
os.system('grib_set -s table2Version=128 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var134.grb.tmp '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var134.grb ')
def create_pres_LESM_files(namelist, SWL_info, member):
"""
Create 3D pressure field using surface pressure and hybrid levels
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
member (int) : member number
"""
for case in SWL_info.keys():
os.system('cdo -s -pressure_fl '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+' '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var54.grb.tmp1 ')
os.system('grib_set -s table2Version=128 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var54.grb.tmp1 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var54.grb.tmp2 ')
os.system('grib_set -s indicatorOfParameter=54 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var54.grb.tmp2 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var54.grb ')
def transform_q2rh_LESM_files(namelist, SWL_info, member):
"""
Transform specific humidity into relative humidity
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
member (int) : member number
"""
for case in SWL_info.keys():
os.system('cdo merge '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var133.grb '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var130.grb '
+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var54.grb '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var157.grb.tmp1 ')
os.system('cdo expr,"var157=0.263*var54*var133/exp((17.67*(var130-273.15))/(var130-29.65))" '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var157.grb.tmp1 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var157.grb.tmp2 ')
os.system('grib_set -s table2Version=128 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var157.grb.tmp2 '+namelist['output_dir']+'tmpDIR/'+case+'_m'+str(101+member)+'_var157.grb ')
def delta_ensemble(namelist, SWL_info):
"""
Compute ensemble of PGW deltas compute as netcdf
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
"""
for case in list(list(SWL_info.keys())[1:]):
for varname in namelist['varnames_sub']:
if varname == 'var152': continue
os.system('cdo -s -O -f nc copy -ensmean -del29feb '+namelist['output_dir']+'HCLIM_grib/PGW'+case[3]+'/m*/'
+varname+'_*.grb '+namelist['output_dir']+'HCLIM_grib/PGW'+case[3]+
'/ensemble/'+varname+'_PGW'+case[3]+'.nc')
def delta_timeinterp(namelist, SWL_info):
"""
Modify time interpolation of ensemble deltas
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
"""
for cases in list(SWL_info.keys())[1:]:
case = 'PGW'+cases[3]
refyear = namelist[case]['refyear']
for varname in namelist['varnames_sub']:
if varname == 'var152': continue
# Create a temporary file
os.system('cdo -s setyear,'+str(refyear)+' '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp0.nc')
os.system('cdo -s settime,12:00:00 '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp0.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp1.nc')
# Select date
os.system('cdo -s seldate,'+str(refyear)+'-01-01T12:00:00 '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp1.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/tmp_jan.nc')
os.system('cdo -s seldate,'+str(refyear)+'-12-31T12:00:00 '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp1.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/tmp_dec.nc')
# Change data
os.system('cdo -s setyear,'+str(refyear+1)+' '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/tmp_jan.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/jan.nc')
os.system('cdo -s setyear,'+str(refyear-1)+' '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/tmp_dec.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/dec.nc')
# merge files
os.system('cdo -s mergetime '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/dec.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp1.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/jan.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp2.nc')
# Interpolate times
os.system('cdo -s inttime,'+str(refyear-1)+'-12-31,12:00:00,3hour '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp2.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp3.nc')
os.system('cdo -s seldate,'+str(refyear)+'-01-01T00:00:00,'+str(refyear+1)+'-01-01T00:00:00 '+
namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp3.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'.nc')
# Clean temporary files
os.system('rm -rf '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/jan.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/dec.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/tmp_jan.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/tmp_dec.nc '+namelist['output_dir']+'HCLIM_grib/'+case+
'/ensemble/'+varname+'_'+case+'_tmp*.nc')
def harmonic_ac_analysis(ts):
"""
Estimation of the harmonics according to formula 12.19 -
12.23 on p. 264 in Storch & Zwiers
Arguments:
ts: a 1-d numpy array of a timeseries
Returns:
hcts: a reconstructed smoothed timeseries
(the more modes are summed the less smoothing)
mean: the mean of the timeseries (needed for reconstruction)
"""
# Substitue nan values with one
ts = np.nan_to_num(ts, nan=1)
#calculate the mean of the timeseries (used for reconstruction)
mean = ts.mean()
lt = len(ts) #how long is the timeseries?
P = lt
#initialize the output array.
#we will use at max 4 modes for reconstruction
#(for peformance reasons, it can be increased)
hcts = np.zeros((4,lt))
timevector=np.arange(1,lt+1,1) #timesteps used in calculation
#a measure that is to check that the performed calculation
# is justified.
q = math.floor(P/2.)
#create the reconstruction timeseries, mode by mode
#(starting at 1 until 5, if one wants more smoothing
#this number can be increased.)
for i in range(1,4):
if i < q: #only if this is true the calculation is valid
#these are the formulas from Storch & Zwiers
bracket = 2.*math.pi*i/P*timevector
a = 2./lt*(ts.dot(np.cos(bracket)))
#dot product (Skalarprodukt) for scalar number output!
b = 2./lt*(ts.dot(np.sin(bracket)))
#calculate the reconstruction time series
hcts[i-1,:] = a * np.cos(bracket) + b * np.sin(bracket)
else: #abort if the above condition is not fulfilled. In this case more programming is needed.
sys.exit('Whooops that should not be the case for a yearly '+
'timeseries! i (reconstruction grade) is larger than '+
'the number of timeseries elements / 2.')
smooths = sum(hcts[0:3,:]) + mean
return smooths
def smooth(x, dim):
"""
Apply smoothing of an annual timeseries
(typically daily resolution) using a spectral filter
(Bosshard et al. 2011).
Parameters:
x (float): variable to smooth
Returns:
float : smoothed variable
"""
if dim == 2:
return smooth_2d(x)
elif dim == 3:
return smooth_3d(x)
def smooth_2d(x):
"""
Apply smoothing of an annual timeseries
(typically daily resolution) using a spectral filter
(Bosshard et al. 2011).
Parameters:
x (float): variable to smooth
Returns:
float : smoothed variable
"""
n_y = x.shape[1]
n_x = x.shape[2]
for jj in range(n_y):
for ii in range(n_x):
x[:,jj,ii] = harmonic_ac_analysis(x[:,jj,ii])
return x
def smooth_3d(x):
"""
Apply smoothing of an annual timeseries
(typically daily resolution) using a spectral filter
(Bosshard et al. 2011).
Parameters:
x (float): variable to smooth
Returns:
float : smoothed variable
"""
n_z = x.shape[1]
n_y = x.shape[2]
n_x = x.shape[3]
for kk in range(n_z):
for jj in range(n_y):
for ii in range(n_x):
x[:,kk,jj,ii] = harmonic_ac_analysis(x[:,kk,jj,ii])
return x
def delta_smooth(namelist, SWL_info):
"""
Modify time interpolation of ensemble deltas
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
"""
for case in list(SWL_info.keys())[1:]:
for varname in namelist['varnames_sub']:
print(varname)
if varname == 'var152': continue
filename = namelist['output_dir']+'HCLIM_grib/PGW'+case[3]+'/ensemble/'+varname+'_PGW'+case[3]+'.nc'
# Apply filter
nc = Dataset(filename,'r+')
try:
var_nc = nc.variables[varname][:,:,:,:]
nc.variables[varname][:,:,:,:] = smooth(var_nc,3)
except:
var_nc = nc.variables[varname][:,:,:]
nc.variables[varname][:,:,:] = smooth(var_nc,2)
nc.close()
def get_varinfo():
varinfo = {}
varinfo['var34'] ={'varname':'delta_sst','long_name':'delta for sea surface temperature', 'units': 'K'}
varinfo['var130'] ={'varname':'delta_t','long_name':'delta for T', 'units': 'K'}
varinfo['var131'] ={'varname':'delta_u','long_name':'delta for zonal velocity', 'units': 'm/s'}
varinfo['var132'] ={'varname':'delta_v','long_name':'delta for meridional velocity', 'units': 'm/s'}
varinfo['var133'] ={'varname':'delta_q','long_name':'delta for specific humidity', 'units': 'kg/kg'}
varinfo['var134'] ={'varname':'delta_sp','long_name':'delta for surface pressure', 'units': 'Pa'}
varinfo['var157'] ={'varname':'delta_r','long_name':'delta for relative humidity', 'units': '%'}
varinfo['var235'] ={'varname':'delta_skt','long_name':'delta for surface temperatures', 'units': 'K'}
return varinfo
def delta_rewritenc(namelist, SWL_info):
"""
Modify the metadata of the netcdf files
Parameters:
namelist (dict): dictionary containing paths and orography files
SWL_info (dict): SWL information
"""
vardict = get_varinfo()
for case in list(SWL_info.keys())[1:]:
for varname in namelist['varnames_sub']:
if varname == 'var152': continue
filein = namelist['output_dir']+'HCLIM_grib/PGW'+case[3]+'/ensemble/'+varname+'_PGW'+case[3]+'.nc'
fileout = namelist['output_dir']+'HCLIM_grib/PGW'+case[3]+'/ensemble/'+vardict[varname]['varname']+'_PGW'+case[3]+'.nc'
nvarname = vardict[varname]['varname']
os.system('ncrename -v '+varname+','+nvarname+' '+filein+' '+fileout)
os.system('ncatted -O -a long_name,'+nvarname+',o,c,"'+vardict[varname]['long_name']+'" -a units,'+nvarname+',o,c,"'+vardict[varname]['units']+'" '+fileout)
# -----------------------------------------------------------------------------
# 1 - Read namelist
namelist = read_namelist('../nam/namelist_deltas_HCLIM_grib.yaml')
# 2 - Read SWL info
SWL_info = read_SWL_info(namelist,['SWL09','SWL20','SWL30'])
# 3 - Create temporary work directory
create_workdir(namelist)
# 4 - Loop over all the members
for member in tqdm(range(namelist['num_members']), desc='Loop over members', leave=True):
# if data already extracted skipped this step
if not namelist['extract_data']: break
# Create member directories
create_workdir(namelist,member,['PGW2','PGW3'])
# Extract data from LESM
extract_LESM_files(namelist, SWL_info, member)
# Separate grib files per variable
separate_LESM_files_variables(namelist, SWL_info, member)
# Create grib file for surface pressure
transform_lnsp2sp_LESM_files(namelist, SWL_info, member)
# Create grib file for pressure at all levels
create_pres_LESM_files(namelist, SWL_info, member)
# Create grib file for relative humidity
transform_q2rh_LESM_files(namelist, SWL_info, member)
# Compute deltas - substraction
delta_substract_LESM(namelist, SWL_info, member)
# Archive delta information
delta_archive(namelist, SWL_info, member)
# Clean working directory
clean_workdir(namelist, member)
# 5 - Compute ensemble of all deltas
delta_ensemble(namelist, SWL_info)
# 6 - Smooth deltas
if namelist['smooth']: delta_smooth(namelist, SWL_info)
# 7 - Adapt deltas -> interpolate in time
delta_timeinterp(namelist, SWL_info)
# 8 - Rewrite metadata in netcdf files
delta_rewritenc(namelist, SWL_info)
# 9 - Remove working directory
remove_workdir(namelist)
# ----------------------------------------------------------------------------------