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write_intermediate_ERA5_CMIP6anom.py
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write_intermediate_ERA5_CMIP6anom.py
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#!/usr/bin/env python
""" write_intermediate_ERA5_CMIP5anom.py
run write_intermediate_ERA5_CMIP5anom.py -s 2007 -e 2007
Authors: Daniel Argueso- Alejandro Di Luca @ CCRC, UNSW. Sydney (Australia)
email: a.diluca@unsw.edu.au
Created: Wed Jun 17 14:08:31 AEST 2015
Modified: March 23 2016
- I added a conversion from relative humidity to specific humidity for the 3-d variable (Alejandro)
- I added a mask to the surface temperature so the output looks the same as the ERA-Interm field (Alejandro)
Modified: May 30 2016
- (Alejandro) I modified the calculation of the specific humidity in two ways:
1) For vertical levels in the stratosphere (p<50 hPa) I assume that the saturation pressure is
zero in the denominator in the calculation of the saturation mixing ratio. Otherwise, saturation
vapor pressure becomes large than the total pressure!!!
2) I set all specific humidity values smaller than zero to zero. Generally there are no values smaller
than zero.
Modified March 27 2018
- Adapted to ERA5 from a version for ERA-Interim (Daniel)
Modified Sept 6 2018 (from Alejandro July 9 2018 ERA-Interim version write_intermediate_ERAI_CMIP5anom.py)
- (Alejandro) Netcdf files in /srv/ccrc/data19/z3393020/ERA-interim_CMIP5anom/ have different dimensions for
files after EIN201401_an_pl.nc (see below).
In files BEFORE EIN201401_an_pl.nc the pressure level variables is called lev
In files AFTER EIN201401_an_pl.nc the pressure level variables is called plev
[z3444417@monsoon ERA-interim_CMIP5anom]$ ncdump -h EIN201401_an_pl.nc | grep plev
plev = 37 ;
double plev(plev) ;
[z3444417@monsoon ERA-interim_CMIP5anom]$ ncdump -h EIN201312_an_pl.nc | grep lev
lev = 37 ;
double lev(lev) ;
lev:standard_name = "air_pressure" ;
lev:long_name = "pressure" ;
The name is not the only difference. The main difference is that in files BEFORE EIN201401_an_pl.nc the levels
were order from the minima to the maxima while in files AFTER EIN201401_an_pl.nc the levels were order from the maximum
to the minimum. So all calculation were wrong using the original script for events after 01-2014
I have now modified the script so it checks the name and order of the pressure vertical levels.
Modified July 7 2021
- Adapted to CMIP6 and ERA5 (Daniel Argüeso)
- Part of the EPICC project
Modified September 15 2023
- Cleaned up the code and added some comments (Daniel Argüeso)
- Added the use of relative humidity from CMIP6 instead of specific humidity (Daniel Argüeso)
- Remove dependencies with external modules (Daniel Argüeso)
"""
import netCDF4 as nc
import numpy as np
from constants import const
import glob as glob
from optparse import OptionParser
import calendar
import outputInter as f90
import datetime as dt
import sys
import matplotlib.pyplot as plt
import copy as cp
import pdb
import os
def checkfile(file_out, overwrite):
"""Checks if the output file exist and whether it should be written or not"""
# ***********************************************************
# BEFORE READING AND PROCESSING THE VARIABLE OF INTEREST CHECK
# IF THE FILE ALREADY EXISTS
# If it does then go to the next one...
fileexist = os.path.exists(file_out)
filewrite = False
if overwrite == "False":
overwrite = False
print(" --> OUTPUT FILE:")
print(" ", file_out)
if fileexist == True:
if overwrite == False:
print(" +++ FILE ALREADY EXISTS +++")
filewrite = False
else:
print(" +++ FILE EXISTS AND WILL BE OVERWRITTEN +++")
filewrite = True
else:
print(" +++ FILE DOES NOT EXISTS YET +++")
filewrite = True
return filewrite
def calc_midmonth(year):
midm_date = []
for month in range(1, 13):
minit = dt.datetime(year, month, 0o1, 00)
if month == 12:
mend = dt.datetime(year + 1, 1, 0o1, 0o1)
else:
mend = dt.datetime(year, month + 1, 0o1, 0o1)
tdiference = (mend - minit).total_seconds() / 2
midm_date = midm_date + [minit + dt.timedelta(seconds=tdiference)]
tdiference = (
dt.datetime(year, 1, 0o1, 0o1) - dt.datetime(year - 1, 12, 0o1, 0o1)
).total_seconds() / 2
midm_date = [
dt.datetime(year - 1, 12, 0o1, 0o1) + dt.timedelta(seconds=tdiference)
] + midm_date
tdiference = (
dt.datetime(year + 1, 2, 0o1, 0o1) - dt.datetime(year + 1, 1, 0o1, 0o1)
).total_seconds() / 2
midm_date = midm_date + [
dt.datetime(year + 1, 1, 0o1, 0o1) + dt.timedelta(seconds=tdiference)
]
return midm_date
def calc_relhum(dewpt, t):
"""Function to calculate relative humidity
from dew point temperature and temperature
"""
relhum = 100.0 * (
np.exp((const.es_Abolton * dewpt) / (const.es_Bbolton + dewpt))
/ np.exp((const.es_Abolton * t) / (const.es_Bbolton + t))
)
return relhum
### Options
parser = OptionParser()
parser.add_option(
"-s",
"--syear",
type="int",
dest="syear",
help="first year to process",
metavar="input argument",
)
parser.add_option(
"-e",
"--eyear",
type="int",
dest="eyear",
help="last year to process",
metavar="input argument",
)
(opts, args) = parser.parse_args()
###
overwrite_file = False
create_figs = False
syear = opts.syear
eyear = opts.eyear
nyears = eyear - syear + 1
smonth = 1
emonth = 1
vars3d = ["hur", "ta", "ua", "va", "zg"]
vars3d_codes = {"hur": "r", "ta": "t", "ua": "u", "va": "v", "zg": "z"}
# vars3d_codes={'hur':'var157','ta':'var130','ua':'var131','va':'var132','zg':'var129'}
vars2d = ["hurs", "tas", "uas", "vas", "ps", "psl", "ts"]
vars2d_codes = {
"dew": "2d",
"tas": "2t",
"uas": "10u",
"vas": "10v",
"ps": "sp",
"psl": "msl",
"ts": "skt",
}
# vars2d_codes={'dew':'var168','tas':'var167','uas':'var165','vas':'var166','ps':'var134','psl':'var151','ts':'var235'}
var_units_era5 = {
"z": "m2 s-2",
"t": "K",
"u": "m s-1",
"v": "m s-1",
"sp": "Pa",
"msl": "Pa",
"ts": "K",
"r": "1",
"10u": "m s-1",
"10v": "m s-1",
"2t": "K",
"2d": "K",
"lsm": "0/1 Flag",
}
nfields3d = len(vars3d)
nfields2d = len(vars2d)
CMIP6anom_dir = "/home/dargueso/BDY_DATA/CMIP6"
ERA5_dir = "/home/dargueso/BDY_DATA/ERA5/ERA5_netcdf"
figs_path = "/home/dargueso/BDY_DATA/CMIP6/Figs"
plvs = [
100000.0,
97500.00,
95000.00,
92500.00,
90000.00,
87500.00,
85000.00,
82500.00,
80000.00,
77500.00,
75000.00,
70000.00,
65000.00,
60000.00,
55000.00,
50000.00,
45000.00,
40000.00,
35000.00,
30000.00,
25000.00,
22500.00,
20000.00,
17500.00,
15000.00,
12500.00,
10000.00,
7000.00,
5000.00,
3000.00,
2000.00,
1000.000,
700.00,
500.00,
300.00,
200.00,
100.00,
]
nlat = 601
nlon = 1200
file_ref = nc.Dataset("%s/era5_daily_sfc_20120101.nc" % (ERA5_dir), "r")
lat = file_ref.variables["lat"][:]
lon = file_ref.variables["lon"][:]
olon, olat = np.meshgrid(lon, lat)
year = syear
month = smonth
day = 1
while year < eyear or (year == eyear and month < emonth):
midmonth = calc_midmonth(year)
print("processing year %s month %02d day %02d" % (year, month, day))
ferapl = nc.Dataset(
"%s/era5_daily_pl_%s%02d%02d.nc" % (ERA5_dir, year, month, day), "r"
)
ferasfc = nc.Dataset(
"%s/era5_daily_sfc_%s%02d%02d.nc" % (ERA5_dir, year, month, day), "r"
)
date_init = dt.datetime(year, month, day, 00)
date_end = dt.datetime(year, month, day, 18)
time_filepl = ferapl.variables["time"]
time_filesfc = ferasfc.variables["time"]
date1 = nc.date2index(date_init, time_filepl, calendar="standard", select="exact")
date2 = nc.date2index(date_end, time_filepl, calendar="standard", select="exact")
ndays = (date_end - date_init).total_seconds() / 86400.0 + 1
nsteps = int((date_end - date_init).total_seconds() / 86400.0 * 4.0 + 1)
vout = {}
print("Looping over timesteps in original ERA5 file")
for nt in range(date1, date2 + 1):
proc_date = nc.num2date(
time_filepl[nt], units=time_filepl.units, calendar="standard"
)
print("processing 3Dvar time: ", proc_date)
Y = str(proc_date.year)
M = str(proc_date.month)
D = str(proc_date.day)
H = str(proc_date.hour)
filedate = proc_date.strftime("%Y-%m-%d_%H-%M-%S")
# CHECK IF THE FILE ALREADY EXISTS
file_out = (
"ERA5:"
+ filedate.split("_")[0]
+ "_"
+ filedate.split("_")[1].split("-")[0]
)
filewrite = checkfile(file_out, overwrite_file)
if filewrite == True:
tdelta = np.asarray(
[
(midmonth[i] - proc_date).total_seconds()
for i in range(len(midmonth))
]
)
tdelta_min = np.argmin(np.abs(tdelta))
if tdelta[tdelta_min] < 0:
i1 = (tdelta_min - 1) % 12
i2 = (tdelta_min) % 12
tdelta_before = np.abs(tdelta[tdelta_min])
tdelta_mid_month = (
midmonth[tdelta_min + 1] - midmonth[tdelta_min]
).total_seconds()
else:
i1 = (tdelta_min - 2) % 12
i2 = (tdelta_min - 1) % 12
tdelta_before = np.abs(tdelta[tdelta_min - 1])
tdelta_mid_month = (
midmonth[tdelta_min] - midmonth[tdelta_min - 1]
).total_seconds()
for var in vars3d:
print("Processing variable %s" % (var))
fanom = nc.Dataset(
"%s/%s_CC_signal_ssp585_2070-2099_1985-2014_pinterp.nc"
% (CMIP6anom_dir, var)
)
var_era = ferapl.variables["%s" % (vars3d_codes[var])][nt, ::-1, :, :]
# anom_units=getattr(fanom.variables["%s" %(var)],'units')
ilon, ilat = np.meshgrid(
fanom.variables["lon"][:], fanom.variables["lat"][:]
)
# Convert geopotential height from m2/s2 to m
if var == "zg":
var_era = var_era / 9.81
var_units_era5["%s" % (vars3d_codes[var])] = "m"
if np.argmin(np.abs(tdelta)) == 0:
var_anom = fanom.variables["%s" % (var)][i1, ::-1, :, :]
else:
var_anom_1 = fanom.variables["%s" % (var)][i1, ::-1, :, :]
var_anom_2 = fanom.variables["%s" % (var)][i2, ::-1, :, :]
var_anom = (
var_anom_1
+ (var_anom_2 - var_anom_1) * (tdelta_before) / tdelta_mid_month
)
# Define the pseudo global warming
temp = var_era + np.nan_to_num(var_anom)
if var == "hur":
temp[temp < 0] = 0 # replace values smaller than zero by zero
temp[temp > 100] = 100
vout[var] = temp
fanom.close()
# -----------------------------------------------------------------------------------------------
# MAKE PLOT
if create_figs == True:
nlev = 10
file_name = {0: "era5", 1: "anom", 2: "pgw"}
for ii in range(3):
if ii == 0:
aa = var_era[nlev, :]
units = var_units_era5["%s" % (vars3d_codes[var])]
if ii == 1:
aa = var_anom[nlev, :]
units = var_units_era5["%s" % (vars3d_codes[var])]
if ii == 2:
aa = vout[var][nlev, :]
figname = figs_path + "%s_lev%s_%s_%s-%s-%s-%s.png" % (
var,
str(nlev),
file_name[ii],
Y,
M,
D,
H,
)
plt.contourf(aa)
plt.colorbar()
plt.title(var + " [" + units + "]")
plt.savefig(figname)
plt.close()
# -----------------------------------------------------------------------------------------------
for var in vars2d:
print("Processing variable %s" % (var))
if var == "hurs":
# Surface relative humidity doesn't exist in original ERA-INt, must be calculated from T2 and DEWPT
dew_era = (
ferasfc.variables[vars2d_codes["dew"]][nt, :, :] - const.tkelvin
)
tas_era = (
ferasfc.variables[vars2d_codes["tas"]][nt, :, :] - const.tkelvin
)
var_era = calc_relhum(dew_era, tas_era)
else:
var_era = ferasfc.variables["%s" % (vars2d_codes[var])][nt, :, :]
fanom = nc.Dataset(
"%s/%s_CC_signal_ssp585_2070-2099_1985-2014.nc"
% (CMIP6anom_dir, var)
)
# if hasattr(fanom.variables["%s" %(var)],'units'):
# anom_units=getattr(fanom.variables["%s" %(var)],'units')
# else:
# if var == 'hurs':
# anom_units=''
# else:
# import pdb; pdb.set_trace()
ilon, ilat = np.meshgrid(
fanom.variables["lon"][:], fanom.variables["lat"][:]
)
if np.min(np.abs(tdelta)) == 0:
var_anom = fanom.variables["%s" % (var)][i1, :, :]
else:
var_anom_1 = fanom.variables["%s" % (var)][i1, :, :]
var_anom_2 = fanom.variables["%s" % (var)][i2, :, :]
var_anom = (
var_anom_1
+ (var_anom_2 - var_anom_1) * (tdelta_before) / tdelta_mid_month
)
# var_anom = interpolate_grid(ilat,ilon,var_anom_c,olat,olon,method='nearest')
# Define the pseudo global warming
vout[var] = var_era + np.nan_to_num(var_anom)
# if var=='ts':
# import pdb; pdb.set_trace()
# vout[var][var_era.mask==True]=
# vout[var][var_era==-9.e+33]=-9.e+33
# -----------------------------------------------------------------------------------------------
# MAKE PLOT
if create_figs == True:
file_name = {0: "era5", 1: "anom", 2: "pgw"}
for ii in range(3):
if ii == 0:
aa = var_era[:]
if var == "hurs":
units = "%"
else:
units = var_units_era5["%s" % (vars2d_codes[var])]
if ii == 1:
aa = var_anom[:]
units = anom_units
if ii == 2:
aa = vout[var][:]
figname = figs_path + "%s_%s_%s-%s-%s-%s.png" % (
var,
file_name[ii],
Y,
M,
D,
H,
)
plt.contourf(aa)
plt.colorbar()
plt.title(var + " [" + units + "]")
plt.savefig(figname)
plt.close()
# -----------------------------------------------------------------------------------------------
fanom.close()
# ###################################################################################################
#################### Writing to WRF intermediate format #############################
filedate = proc_date.strftime("%Y-%m-%d_%H-%M-%S")
fields3d = np.ndarray(
shape=(nfields3d, len(plvs), nlat, nlon), dtype="float32"
) # ,order='Fortran')
fields2d = np.ndarray(
shape=(nfields2d, nlat, nlon), dtype="float32"
) # ,order='Fortran')
startlat = lat[0]
startlon = lon[0]
deltalon = 0.30
deltalat = -0.30
fields3d[0, :, :, :] = np.float32(vout["hur"])
fields3d[1, :, :, :] = np.float32(vout["ta"])
fields3d[2, :, :, :] = np.float32(vout["ua"])
fields3d[3, :, :, :] = np.float32(vout["va"])
fields3d[4, :, :, :] = np.float32(vout["zg"])
fields2d[0, :, :] = np.float32(vout["uas"])
fields2d[1, :, :] = np.float32(vout["vas"])
fields2d[2, :, :] = np.float32(vout["hurs"])
fields2d[3, :, :] = np.float32(vout["ps"])
fields2d[4, :, :] = np.float32(vout["psl"])
fields2d[5, :, :] = np.float32(vout["tas"])
fields2d[6, :, :] = np.float32(vout["ts"])
f90.writeint(
plvs,
fields3d,
fields2d,
filedate,
nlat,
nlon,
startlat,
startlon,
deltalon,
deltalat,
)
# ###################################################################################################
end_date = dt.datetime(year, month, day) + dt.timedelta(days=1)
year = end_date.year
month = end_date.month
day = end_date.day