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model2roms.py
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model2roms.py
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from netCDF4 import Dataset, datetime, date2num,num2date
import numpy as np
import interp2D
import interpolation as interp
import IOwrite
import os
import calendar
import grd
import barotropic
import IOinitial
import IOsubset
import datetimeFunctions
try:
import ESMF
except ImportError:
print("Could not find module ESMF")
pass
__author__ = 'Trond Kristiansen'
__email__ = '[email protected]'
__created__ = datetime(2008, 8, 15)
__modified__ = datetime(2014, 12, 1)
__version__ = "1.5"
__status__ = "Development, modified on 15.08.2008,01.10.2009,07.01.2010, 15.07.2014, 01.12.2014, 07.08.2015"
def VerticalInterpolation(myvar, array1, array2, grdROMS, grdMODEL):
outINDEX_ST = (grdROMS.Nlevels, grdROMS.eta_rho, grdROMS.xi_rho)
outINDEX_U = (grdROMS.Nlevels, grdROMS.eta_u, grdROMS.xi_u)
outINDEX_UBAR = (grdROMS.eta_u, grdROMS.xi_u)
outINDEX_V = (grdROMS.Nlevels, grdROMS.eta_v, grdROMS.xi_v)
outINDEX_VBAR = (grdROMS.eta_v, grdROMS.xi_v)
if myvar in ['salinity', 'temperature']:
print('Start vertical interpolation for %s (dimensions=%s x %s)' % (myvar, grdROMS.xi_rho, grdROMS.eta_rho))
outdata = np.empty((outINDEX_ST), dtype=np.float64, order='Fortran')
outdata = interp.interpolation.dovertinter(np.asarray(outdata, order='Fortran'),
np.asarray(array1, order='Fortran'),
np.asarray(grdROMS.h, order='Fortran'),
np.asarray(grdROMS.z_r, order='Fortran'),
np.asarray(grdMODEL.z_r, order='Fortran'),
int(grdROMS.Nlevels),
int(grdMODEL.Nlevels),
int(grdROMS.xi_rho),
int(grdROMS.eta_rho),
int(grdROMS.xi_rho),
int(grdROMS.eta_rho))
outdata = np.ma.masked_where(abs(outdata) > 1000, outdata)
#import plotData
#for k in xrange(len(grdMODEL.h)-1):
# plotData.contourMap(grdROMS, grdROMS.lon_rho, grdROMS.lat_rho, np.squeeze(outdata[k,:,:]),k, myvar)
return outdata
if myvar == 'vvel':
print('Start vertical interpolation for uvel (dimensions=%s x %s)' % (grdROMS.xi_u, grdROMS.eta_u))
outdataU = np.zeros((outINDEX_U), dtype=np.float64)
outdataUBAR = np.zeros((outINDEX_UBAR), dtype=np.float64)
outdataU = interp.interpolation.dovertinter(np.asarray(outdataU, order='Fortran'),
np.asarray(array1, order='Fortran'),
np.asarray(grdROMS.h, order='Fortran'),
np.asarray(grdROMS.z_r, order='Fortran'),
np.asarray(grdMODEL.z_r, order='Fortran'),
int(grdROMS.Nlevels),
int(grdMODEL.Nlevels),
int(grdROMS.xi_u),
int(grdROMS.eta_u),
int(grdROMS.xi_rho),
int(grdROMS.eta_rho))
outdataU = np.ma.masked_where(abs(outdataU) > 1000, outdataU)
print('Start vertical interpolation for vvel (dimensions=%s x %s)' % (grdROMS.xi_v, grdROMS.eta_v))
outdataV = np.zeros((outINDEX_V), dtype=np.float64)
outdataVBAR = np.zeros((outINDEX_VBAR), dtype=np.float64)
outdataV = interp.interpolation.dovertinter(np.asarray(outdataV, order='Fortran'),
np.asarray(array2, order='Fortran'),
np.asarray(grdROMS.h, order='Fortran'),
np.asarray(grdROMS.z_r, order='Fortran'),
np.asarray(grdMODEL.z_r, order='Fortran'),
int(grdROMS.Nlevels),
int(grdMODEL.Nlevels),
int(grdROMS.xi_v),
int(grdROMS.eta_v),
int(grdROMS.xi_rho),
int(grdROMS.eta_rho))
outdataV = np.ma.masked_where(abs(outdataV) > 1000, outdataV)
z_wu = np.zeros((grdROMS.Nlevels + 1, grdROMS.eta_u, grdROMS.xi_u), dtype=np.float64)
z_wv = np.zeros((grdROMS.Nlevels + 1, grdROMS.eta_v, grdROMS.xi_v), dtype=np.float64)
outdataUBAR = barotropic.velocity.ubar(np.asarray(outdataU, order='Fortran'),
np.asarray(outdataUBAR, order='Fortran'),
np.asarray(grdROMS.z_w, order='Fortran'),
np.asarray(z_wu, order='Fortran'),
grdROMS.Nlevels,
grdROMS.xi_u,
grdROMS.eta_u,
grdROMS.xi_rho,
grdROMS.eta_rho)
outdataUBAR = np.ma.masked_where(abs(outdataUBAR) > 1000, outdataUBAR)
#plotData.contourMap(grdROMS, grdROMS.lon_rho, grdROMS.lat_rho, outdataUBAR,1, "ubar")
outdataVBAR = barotropic.velocity.vbar(np.asarray(outdataV, order='Fortran'),
np.asarray(outdataVBAR, order='Fortran'),
np.asarray(grdROMS.z_w, order='Fortran'),
np.asarray(z_wv, order='Fortran'),
grdROMS.Nlevels,
grdROMS.xi_v,
grdROMS.eta_v,
grdROMS.xi_rho,
grdROMS.eta_rho)
#plotData.contourMap(grdROMS, grdROMS.lon_rho, grdROMS.lat_rho, outdataVBAR,1, "vbar")
outdataVBAR = np.ma.masked_where(abs(outdataVBAR) > 1000, outdataVBAR)
return outdataU, outdataV, outdataUBAR, outdataVBAR
def HorizontalInterpolation(myvar, grdROMS, grdMODEL, data, show_progress, useFilter):
print('Start %s horizontal interpolation for %s' % (grdMODEL.grdType, myvar))
if myvar in ['temperature', 'salinity']:
array1 = interp2D.doHorInterpolationRegularGrid(myvar, grdROMS, grdMODEL, data, show_progress, useFilter)
if myvar in ['ssh', 'ageice', 'uice', 'vice', 'aice', 'hice', 'snow_thick']:
array1 = interp2D.doHorInterpolationSSHRegularGrid(myvar, grdROMS, grdMODEL, data, useFilter)
if myvar in ['uvel', 'vvel']:
array1 = interp2D.doHorInterpolationRegularGrid(myvar, grdROMS, grdMODEL, data, show_progress, useFilter)
return array1
def rotate(grdROMS, grdMODEL, data, u, v):
"""
First rotate the values of U, V at rho points with the angle, and then interpolate
the rho point values to U and V points and save the result
"""
urot = np.zeros((int(grdMODEL.Nlevels), int(grdROMS.eta_rho), int(grdROMS.xi_rho)), np.float64)
vrot = np.zeros((int(grdMODEL.Nlevels), int(grdROMS.eta_rho), int(grdROMS.xi_rho)), np.float64)
urot, vrot = interp.interpolation.rotate(np.asarray(urot, order='Fortran'),
np.asarray(vrot, order='Fortran'),
np.asarray(u, order='Fortran'),
np.asarray(v, order='Fortran'),
np.asarray(grdROMS.angle, order='Fortran'),
int(grdROMS.xi_rho),
int(grdROMS.eta_rho),
int(grdMODEL.Nlevels))
return urot, vrot
def interpolate2UV(grdROMS, grdMODEL, urot, vrot):
Zu = np.zeros((int(grdMODEL.Nlevels), int(grdROMS.eta_u), int(grdROMS.xi_u)), np.float64)
Zv = np.zeros((int(grdMODEL.Nlevels), int(grdROMS.eta_v), int(grdROMS.xi_v)), np.float64)
# Interpolate from RHO points to U and V points for velocities
Zu = interp.interpolation.rho2u(np.asarray(Zu, order='Fortran'),
np.asarray(urot, order='Fortran'),
int(grdROMS.xi_rho),
int(grdROMS.eta_rho),
int(grdMODEL.Nlevels))
#plotData.contourMap(grdROMS,grdMODEL,Zu[0,:,:],"1",'urot')
Zv = interp.interpolation.rho2v(np.asarray(Zv, order='Fortran'),
np.asarray(vrot, order='Fortran'),
int(grdROMS.xi_rho),
int(grdROMS.eta_rho),
int(grdMODEL.Nlevels))
#plotData.contourMap(grdROMS,grdMODEL,Zv[0,:,:],"1",'vrot')
return Zu, Zv
def getTime(dataPath, indatatype, grdROMS, grdMODEL, year, month, day, mytime, firstRun):
"""
Create a date object to keep track of Julian dates etc.
Also create a reference date starting at 1948/01/01.
Go here to check results:http://lena.gsfc.nasa.gov/lenaDEV/html/doy_conv.html
"""
if indatatype == 'SODA':
filename = getSODAfilename(year, month, day, None, dataPath)
if indatatype == 'SODA3':
filename = getSODA3filename(year, month, day, None, dataPath)
if indatatype == 'SODAMONTHLY':
filename = getSODAMONTHLYfilename(year, month, day, None, dataPath)
if indatatype == 'GLORYS':
filename = getGLORYSfilename(year, month, day, "S", dataPath)
if indatatype == 'WOAMONTHLY':
filename = getWOAMONTHLYfilename(year, month, day, "temperature", dataPath)
if indatatype == 'NORESM':
filename = getNORESMfilename(year, month, day, "saln", dataPath)
if indatatype == 'NS8KM':
filename = getNS8KMfilename(year, month, day, "salt", dataPath)
if indatatype == 'NS8KMZ':
filename, readFromOneFile = getNS8KMZfilename(year, month, day, "salt", dataPath)
# Now open the input file and get the time
cdf = Dataset(filename)
if (indatatype)=='NORESM':
jdref = date2num(datetime(1800,1,1),cdf.variables["time"].units,calendar=cdf.variables["time"].calendar)
elif indatatype=='NS8KMZ':
jdref = date2num(datetime(1948,1,1),units="days since 1948-01-01 00:00:00",calendar="standard")
elif (indatatype)=='GLORYS':
jdref = date2num(datetime(1948,1,1),cdf.variables["time_counter"].units,calendar=cdf.variables["time_counter"].calendar)
elif indatatype=='NS8KM':
jdref = date2num(datetime(1948,1,1),cdf.variables["ocean_time"].units,calendar=cdf.variables["ocean_time"].calendar)
elif indatatype=='SODA3':
jdref = date2num(datetime(1948,1,1),units="days since 1948-01-01 00:00:00",calendar="standard")
else:
jdref = date2num(datetime(1948,1,1),cdf.variables["time"].units,calendar=cdf.variables["time"].calendar)
if indatatype == 'SODA':
# Find the day and month that the SODA file respresents based on the year and ID number.
# Each SODA file represents a 5 day average, therefore we let the date we find be the first day
# of those 5 days. Thats the reason we subtract 4 below for day of month.
import date
days = 0.0; month = 1; loop = True
while loop is True:
d = date.NumberDaysMonth(month, year)
if days + d < int(ID) * 5:
days = days + d
month += 1
else:
day = int(int(ID) * 5 - days)
loop = False
mycalendar = cdf.variables["time"].calendar
myunits = cdf.variables["time"].units
currentdate = datetime(year, month, day)
jd = date2num(currentdate,units="days since 1948-01-01 00:00:00",calendar="standard")
if indatatype == 'SODAMONTHLY':
# Find the day and month that the SODAMONTHLY file respresents based on the year and ID number.
# Each SODA file represents a 1 month average.
mycalendar = cdf.variables["time"].calendar
myunits = cdf.variables["time"].units
currentdate = datetime(year, month, day)
jd = date2num(currentdate, myunits, calendar=mycalendar)
if indatatype == 'SODA3':
# Each SODA file represents 12 month averages.
#mycalendar = cdf.variables["time"].calendar
myunits = cdf.variables["time"].units
currentdate = datetime(year, month, day)
jd = date2num(currentdate,units="days since 1948-01-01 00:00:00",calendar="standard")
if indatatype == 'GLORYS':
# Find the day and month that the GLORYS file respresents based on the year and ID number.
# Each file represents a 1 month average.
mycalendar = cdf.variables["time_counter"].calendar
myunits = cdf.variables["time_counter"].units
currentdate = datetime(year, month, day)
jd = date2num(currentdate, myunits, calendar=mycalendar)
if indatatype == 'NS8KM':
# Find the day and month that the GLORYS file respresents based on the year and ID number.
# Each file represents a 1 month average.
mycalendar = cdf.variables["ocean_time"].calendar
myunits = cdf.variables["ocean_time"].units
currentdate = datetime(year, month, day)
jd = date2num(currentdate, myunits, calendar=mycalendar)
if indatatype == 'NS8KMZ':
# Find the day and month that the GLORYS file respresents based on the year and ID number.
# Each file represents a 1 month average.
mycalendar = "gregorian"
refdate = datetime(1948, 1, 1)
currentdate = datetime(year, month, day)
myunits = cdf.variables["time"].units
jd = date2num(currentdate, myunits, calendar="gregorian")
print("Days:", jd, currentdate," year month day ",year, month, day)
if indatatype == 'NORESM':
# Find the day and month that the NORESM file. We need to use the time modules from
# netcdf4 for python as they handle calendars that are no_leap.
# http://www.esrl.noaa.gov/psd/people/jeffrey.s.whitaker/python/netcdftime.html#datetime
mydays = cdf.variables["time"][0]
mycalendar = cdf.variables["time"].calendar
myunits = cdf.variables["time"].units
# Fake the start date first time around
# if (firstRun):
# currentdate = datetime(2006,1,1)
# print "NOTICE!\n First datestamp in result files are hardcoded to %s"%(currentdate)
# else:
currentdate = num2date(mydays, units=myunits, calendar=mycalendar)
jd = date2num(currentdate, myunits, calendar='noleap')
grdROMS.time = (jd - jdref)
grdROMS.reftime = jdref
grdROMS.timeunits=myunits
cdf.close()
print('\nCurrent time of %s file : %s' % (indatatype, currentdate))
def getGLORYSfilename(year, month, day, myvar, dataPath):
# Month indicates month
# myvar:S,T,U,V
print("MYVAR: %s - %s"%(myvar,dataPath))
if (myvar in ['iicevelu', 'iicevelv', 'ileadfra', 'iicethic']):
myvarPrefix = 'icemod'
myvar = "ice"
elif (myvar in ['sossheig']):
if (year == 2014):
myvarPrefix = 'grid2D'
elif (2013 > year >= 2010 and month == 12) or ( 2013 > year >= 2011 ):
myvarPrefix = 'SSH'
elif (year == 2013):
myvarPrefix = 'SSH'
myvar = "ssh"
elif (myvar in ['vozocrtx', 'vomecrty']):
myvar='u-v'
myvarPrefix='gridUV'
elif(myvar in ['votemper']):
myvar='t'
myvarPrefix='gridT'
elif(myvar in ['vosaline']):
myvar='s'
myvarPrefix='gridS'
else:
myvarPrefix = 'grid'+str(myvar.upper())
# GLORYS change the name in the middle of the time-series (on December 2010) and we have to
# account for that
if (year == 2014):
production = "R20151218"
elif (2013 > year >= 2010 and month == 12) or ( 2013 > year >= 2011 ):
production = "R20140520"
elif (year == 2013):
production = "R20141205"
else:
production = "R20130808"
if month < 10: filename = dataPath + 'dataset-global-reanalysis-phys-001-009-ran-fr-glorys2v3-monthly-' + str(
myvar.lower()) + '/GLORYS2V3_ORCA025_' + str(year) + '0' + str(month) + '15_'+str(production)+'_' + str(myvarPrefix) + '.nc'
if month >= 10: filename = dataPath + 'dataset-global-reanalysis-phys-001-009-ran-fr-glorys2v3-monthly-' + str(
myvar.lower()) + '/GLORYS2V3_ORCA025_' + str(year) + str(month) + '15_'+str(production)+'_' + str(myvarPrefix) + '.nc'
print("Filename in: %s"%(filename))
return filename
def getNORESMfilename(year, month, day, myvar, dataPath):
if (myvar=='grid'):
filename = dataPath + 'GRID/NorESM.nc'
#TODO: Fix this hardcoding of grid path
filename = "/work/users/trondk/REGSCEN/GRID/NorESM.nc"
filename = dataPath + 'GRID/NorESM.nc'
else:
if myvar in ['iage', 'uvel', 'vvel', 'aice', 'hi', 'hs']:
filename = dataPath + 'ICE/NRCP45AERCN_f19_g16_CLE_01.cice.h.'+str(year)+'.nc'
else:
if (month < 10):
filename = dataPath + 'OCN/NRCP45AERCN_f19_g16_CLE_01.micom.hm.'+str(year)+'-0'+str(ID)+'.nc'
else:
filename = dataPath + 'OCN/NRCP45AERCN_f19_g16_CLE_01.micom.hm.'+str(year)+'-'+str(ID)+'.nc'
return filename
def getNS8KMfilename(year, month, day, myvar, dataPath):
if (month < 10):
mymonth='0%s'%(month)
else:
mymonth='%s'%(month)
filename = dataPath + str(year)+str(mymonth)+'15_mm-IMR-MODEL-ROMS-NWS-20140430-fv02.1.nc'
return filename, month
def getNS8KMZfilename(year, month, day, myvar, dataPath):
#allInOneFile = '/work/users/trondk/KINO/FORCING/1600M/northsea_8km_z_mean.nc_2010-2013.nc'
allInOneFile = ''
if os.path.exists(allInOneFile):
readFromOneFile = True
print("NOTE ! READING ALL MYOCEAN FORCING DATA FROM ONE FILE")
return allInOneFile, readFromOneFile
else:
readFromOneFile = False
if (month < 10):
mymonth='0%s'%(month)
else:
mymonth='%s'%(month)
filename = dataPath + str(year)+str(mymonth)+'15_mm-IMR-MODEL-ROMS-NWS-20160203-fv02.1.nc'
return filename, readFromOneFile
def getSODAMONTHLYfilename(year, month, day, myvar, dataPath):
if month < 10: filename = dataPath + 'SODA_2.0.2_' + str(year) + '0' + str(month) + '.cdf'
if month >= 10: filename = dataPath + 'SODA_2.0.2_' + str(year) + str(month) + '.cdf'
return filename
def getSODAfilename(year, month, day, myvar, dataPath):
file = "SODA_2.0.2_" + str(year) + "_" + str(month) + ".cdf"
return dataPath + file
def getSODA3filename(year, month, day, myvar, dataPath):
if (myvar in ['cn', 'hi', 'hs']):
file = "soda3.3.1_mn_ice_reg_"+str(year)+".nc"
else:
file = "soda3.3.1_mn_ocean_reg_"+str(year)+".nc"
return dataPath + file
def getWOAMONTHLYfilename(year, month, day, myvar, dataPath):
if myvar == "temperature":
filename = dataPath + 'temperature_monthly_1deg.nc'
elif myvar == "salinity":
filename = dataPath + 'salinity_monthly_1deg.nc'
else:
print("Could not find any input files in folder: %s"%(datapath))
return filename
def get3Ddata(grdROMS, grdMODEL, myvar, indatatype, year, month, day, dataPath):
if myvar == 'temperature': varN = 0;
if myvar == 'salinity': varN = 1;
if myvar == 'uvel': varN = 3;
if myvar == 'vvel': varN = 4;
# The variable splitExtract is defined in IOsubset.py and depends on the orientation
# and indatatype of grid (-180-180 or 0-360). Assumes regular grid.
if grdMODEL.useESMF:
if indatatype == "SODA":
filename = getSODAfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[grdROMS.varNames[varN]][0,:,:,:]
if indatatype == "SODA3":
filename = getSODA3filename(year, month, day, grdROMS.varNames[varN], dataPath)
cdf = Dataset(filename)
print("=>Extracting data for month %s from SODA3 %s "%(month-1,filename))
data = cdf.variables[grdROMS.vars[varN]][month-1,:,:,:]
if indatatype == "SODAMONTHLY":
filename = getSODAMONTHLYfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][:,:,:]
if indatatype == "WOAMONTHLY":
filename = getWOAMONTHLYfilename(year, month, day, myvar, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][month - 1, :,:,:]
if indatatype == "NORESM":
cdf = Dataset(getNORESMfilename(year, month, day, grdROMS.varNames[varN], dataPath))
myunits = cdf.variables[str(grdROMS.varNames[varN])].units
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,:,:,:])
data=np.where(data.mask,grdROMS.fill_value,data)
# data = np.where(abs(data)>=32768 , grdROMS.fill_value, data)
print("Data range", np.min(data),np.max(data))
if indatatype == "NS8KMZ":
filename, readFromOneFile = getNS8KMZfilename(year, month, day, grdROMS.varNames[varN], dataPath)
cdf = Dataset(filename)
print("Reading from one file %s"%(readFromOneFile))
myunits = cdf.variables[str(grdROMS.varNames[varN])].units
if (readFromOneFile):
jdref = date2num(datetime(1948,1,1),cdf.variables["time"].units,calendar=cdf.variables["time"].calendar)
currentdate = datetime(year, month, day, 12)
jd = date2num(currentdate, cdf.variables["time"].units, calendar="gregorian")
print("Days:", jd, currentdate)
timesteps = (cdf.variables["time"][:]).tolist()
timeindex = timesteps.index(jd)
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][timeindex,:,:,:])
else:
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,:,:,:])
print("Range of data",varN, np.min(data),np.max(data))
data=np.where(data.mask,grdROMS.fill_value,data)
if indatatype == "GLORYS":
cdf = Dataset(getGLORYSfilename(year, month, day, grdROMS.varNames[varN], dataPath))
myunits = cdf.variables[str(grdROMS.varNames[varN])].units
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,:,:,:])
data=np.where(data.mask,grdROMS.fill_value,data)
cdf.close()
else:
if grdMODEL.splitExtract is True:
if indatatype == "SODA":
filename = getSODAfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data1 = cdf.variables[grdROMS.varNames[varN]][0, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
data2 = cdf.variables[grdROMS.varNames[varN]][0, :,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])]
if indatatype == "SODA3":
filename = getSODA3filename(year,month,day,grdROMS.varNames[varN],dataPath)
cdf = Dataset(filename)
data1 = cdf.variables[grdROMS.varNames[varN]][month-1, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
data2 = cdf.variables[grdROMS.varNames[varN]][month-1, :,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])]
if indatatype == "SODAMONTHLY":
filename = getSODAMONTHLYfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data1 = cdf.variables[str(grdROMS.varNames[varN])][:,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
data2 = cdf.variables[str(grdROMS.varNames[varN])][:,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])]
if indatatype in ["WOAMONTHLY"]:
filename = getWOAMONTHLYfilename(year, month, day, myvar, dataPath)
cdf = Dataset(filename)
data1 = cdf.variables[str(grdROMS.varNames[varN])][month - 1, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
data2 = cdf.variables[str(grdROMS.varNames[varN])][month - 1, :,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])]
if indatatype == "GLORYS":
cdf = Dataset(getGLORYSfilename(year, month, day, grdROMS.varNames[varN], dataPath))
myunits = cdf.variables[str(grdROMS.varNames[varN])].units
data1 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
data2 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])])
if indatatype == "NORESM":
cdf = Dataset(getNORESMfilename(year, month, day, grdROMS.varNames[varN], dataPath))
myunits = cdf.variables[str(grdROMS.varNames[varN])].units
data1 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
data2 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])])
cdf.close()
data = np.concatenate((data1, data2), axis=2)
else:
if indatatype == "SODA":
filename = getSODAfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][0, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
if indatatype == "SODA3":
filename = getSODA3filename(year,month,day,grdROMS.varNames[varN],dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][month-1, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
if indatatype == "SODAMONTHLY":
filename = getSODAMONTHLYfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][:,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
if indatatype == "WOAMONTHLY":
filename = getWOAMONTHLYfilename(year, month, day, myvar, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][month, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
if indatatype == "GLORYS":
if myvar == 'temperature':
cdf = Dataset(getGLORYSfilename(year, month, day, 'T', dataPath))
myunits = cdf.variables[str(grdROMS.varNames[varN])].units
if myvar == 'salinity': cdf = Dataset(getGLORYSfilename(year, month, day, 'S', dataPath))
if myvar == 'uvel': cdf = Dataset(getGLORYSfilename(year, month, day, 'U', dataPath))
if myvar == 'vvel': cdf = Dataset(getGLORYSfilename(year, month, day, 'V', dataPath))
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
if indatatype == "NORESM":
cdf = Dataset(getNORESMfilename(year, month, day, grdROMS.varNames[varN], dataPath))
myunits = cdf.variables[str(grdROMS.varNames[varN])].units
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
cdf.close()
if myvar == 'temperature' and indatatype in ["NS8KMZ", "GLORYS", "NORESM"]:
if myunits == "degree_Kelvin" or myunits == "K":
if indatatype in ["GLORYS"]:
data = np.where(data <= -32.767, grdROMS.fill_value, data)
data = data - 273.15
if indatatype == "GLORYS":
data = np.where(data <= -32.767, grdROMS.fill_value, data)
data = np.ma.masked_where(data <= grdROMS.fill_value, data)
if __debug__:
print("Data range of %s just after extracting from netcdf file: %s - %s" % (str(grdROMS.varNames[varN]),
data.min(), data.max()))
return data
def get2Ddata(grdROMS, grdMODEL, myvar, indatatype, year, month, day, dataPath):
indexROMS_SSH = (grdROMS.eta_rho, grdROMS.xi_rho)
if indatatype == "NORESM":
if myvar == 'ageice': varN = 5; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'uice': varN = 6; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'vice': varN = 7; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'aice': varN = 8; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'hice': varN = 9; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'snow_thick': varN = 10; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if indatatype == "GLORYS":
if myvar == 'uice': varN = 5; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'vice': varN = 6; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'aice': varN = 7; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'hice': varN = 8; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
print("Current type %s and variable %s"%(indatatype,myvar))
if indatatype == "SODA3":
if myvar == 'aice': varN = 5; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'hice': varN = 6; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'snow_thick': varN = 7; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if myvar == 'ssh': varN = 2; SSHdata = np.zeros((indexROMS_SSH), dtype=np.float64)
if grdMODEL.useESMF:
if indatatype == "SODA":
filename = getSODAfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[grdROMS.varNames[varN]][0,:,:]
if indatatype == "SODA3":
filename = getSODA3filename(year, month, day, grdROMS.varNames[varN], dataPath)
print("Trying to open file %s"%(filename))
cdf = Dataset(filename)
if myvar == 'aice':
# We only extract the first thickness concentration. Need to fix this so all 5 classes can be extracted.
# http://www.atmos.umd.edu/~ocean/index_files/soda3_readme.htm
# hi: sea ice thickness [m ice]
# mi: sea ice mass [kg/m^2]
# hs: snow thickness [m snow]
# {cn1,cn2,cn3,cn4,cn5}: sea ice concentration [0:1] in five ice thickness classes
data = cdf.variables[grdROMS.varNames[varN]][int(month-1),0,:,:]
else:
data = cdf.variables[grdROMS.varNames[varN]][int(month-1),:,:]
if indatatype == "SODAMONTHLY":
filename = getSODAMONTHLYfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][:,:]
if indatatype == "WOAMONTHLY":
filename = getWOAMONTHLYfilename(year, month, day, myvar, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][month - 1, :,:]
if indatatype == "NORESM":
cdf = Dataset(getNORESMfilename(year, month, day, grdROMS.varNames[varN], dataPath))
#myunits = cdf.variables[str(grdROMS.varNames[varN])].units
# For NORESM data are 12 months of data stored in ice files. Use ID as month indicator to get data.
if myvar in ['ageice','uice','vice','aice','hice','snow_thick']:
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][month-1, :,:])
else:
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,:])
data=np.where(data.mask,grdROMS.fill_value,data)
if indatatype == "GLORYS":
cdf = Dataset(getGLORYSfilename(year, month, day, grdROMS.varNames[varN], dataPath))
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0, :,:])
data=np.where(data.mask,grdROMS.fill_value,data)
if indatatype == "NS8KMZ":
filename, readFromOneFile = getNS8KMZfilename(year, month, day, grdROMS.varNames[varN], dataPath)
cdf = Dataset(filename)
print("Reading from file", filename)
if (readFromOneFile):
jdref = date2num(datetime(1948,1,1),cdf.variables["time"].units,calendar=cdf.variables["time"].calendar)
currentdate = datetime(year, month, day, 12)
jd = date2num(currentdate, cdf.variables["time"].units, calendar="gregorian")
timesteps = (cdf.variables["time"][:]).tolist()
timeindex = timesteps.index(jd)
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][timeindex,:,:])
else:
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,:,:])
data=np.where(data.mask,grdROMS.fill_value,data)
print("Extracted raw data: %s min: %s max: %s"%(myvar,np.min(data),np.max(data)))
cdf.close()
else:
if grdMODEL.splitExtract is True:
if indatatype == "SODA":
filename = getSODAfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data1 = cdf.variables[grdROMS.varNames[varN]][0,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
data2 = cdf.variables[grdROMS.varNames[varN]][0,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])]
if indatatype == "SODA3":
filename = getSODA3filename(year,month,day,grdROMS.varNames[varN],dataPath)
cdf = Dataset(filename)
data1 = cdf.variables[grdROMS.varNames[varN]][int(month-1),
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
data2 = cdf.variables[grdROMS.varNames[varN]][int(month-1),
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])]
if indatatype == "SODAMONTHLY":
filename = getSODAMONTHLYfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data1 = cdf.variables[str(grdROMS.varNames[varN])][
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
data2 = cdf.variables[str(grdROMS.varNames[varN])][
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])]
if indatatype == "GLORYS":
cdf = Dataset(getGLORYSfilename(year, month, day, '2D', dataPath))
data1 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
data2 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])])
if indatatype == "NORESM":
cdf = Dataset(getNORESMfilename(year, month, day, grdROMS.varNames[varN], dataPath))
if myvar in ['ageice','uice','vice','aice','hice','snow_thick']:
data1 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][month-1,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
data2 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][month-1,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])])
else:
data1 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
data2 = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,
int(grdMODEL.indices[1, 2]):int(grdMODEL.indices[1, 3]),
int(grdMODEL.indices[1, 0]):int(grdMODEL.indices[1, 1])])
cdf.close()
data = np.concatenate((data1, data2), axis=1)
else:
if indatatype == "SODA":
filename = getSODAfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][0,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
if indatatype == "SODA3":
filename = getSODA3filename(year,month,day,grdROMS.varNames[varN],dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][int(month-1),
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
if indatatype == "SODAMONTHLY":
filename = getSODAMONTHLYfilename(year, month, day, None, dataPath)
cdf = Dataset(filename)
data = cdf.variables[str(grdROMS.varNames[varN])][
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])]
if indatatype == "GLORYS":
cdf = Dataset(getGLORYSfilename(year, month, day, grdROMS.varNames[varN], dataPath))
data = np.squeeze(cdf.variables[str(varNames[varN])][0,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
if indatatype == "NORESM":
cdf = Dataset(getNORESMfilename(year, month, day, grdROMS.varNames[varN], dataPath))
if myvar in ['ageice','uice','vice','aice','hice','snow_thick']:
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][month-1,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
else:
data = np.squeeze(cdf.variables[str(grdROMS.varNames[varN])][0,
int(grdMODEL.indices[0, 2]):int(grdMODEL.indices[0, 3]),
int(grdMODEL.indices[0, 0]):int(grdMODEL.indices[0, 1])])
cdf.close()
# data = np.where(abs(data) == grdMODEL.fill_value, grdROMS.fill_value, data)
#data = np.ma.where(abs(data) > 1000, grdROMS.fill_value, data)
if indatatype == "GLORYS":
data = np.where(data <= -32.767, grdROMS.fill_value, data)
data = np.ma.masked_where(data <= grdROMS.fill_value, data)
if __debug__:
print("Data range of %s just after extracting from netcdf file: %s - %s" % (str(grdROMS.varNames[varN]),
data.min(), data.max()))
return data
def convertMODEL2ROMS(confM2R):
if confM2R.useesmf:
print("\n=>Turning on debugging for ESMF")
#ESMF.Manager(logkind=ESMF.LogKind.MULTI, debug=True)
startdate=confM2R.startdate
dataPath=confM2R.modelpath
# First opening of input file is just for initialization of grid
if confM2R.indatatype == 'SODA':
fileNameIn = getSODAfilename(startdate.year, startdate.month, startdate.day, "salinity", dataPath)
if confM2R.indatatype == 'SODA3':
fileNameIn = getSODA3filename(confM2R.startdate.year, startdate.month, startdate.day, "salinity", dataPath)
if confM2R.indatatype == 'SODAMONTHLY':
fileNameIn = getSODAfilename(startdate.year, startdate.month, startdate.day, "salinity", dataPath)
if confM2R.indatatype == 'NORESM':
fileNameIn = getNORESMfilename(startdate.year, startdate.month, startdate.day, "grid", dataPath)
if confM2R.indatatype == 'WOAMONTHLY':
fileNameIn = getWOAMONTHLYfilename(startdate.year, startdate.month, startdate.day, "temperature", dataPath)
if confM2R.indatatype == 'GLORYS':
fileNameIn = getGLORYSfilename(startdate.year, startdate.month, startdate.day, "S", dataPath)
if confM2R.indatatype == 'GLORYS':
fileNameIn = getGLORYSfilename(startdate.year, startdate.month, startdate.day, "S", dataPath)
if confM2R.indatatype == 'NS8KM':
fileNameIn = getNS8KMfilename(startdate.year, startdate.month, startdate.day, "S", dataPath)
if confM2R.indatatype == 'NS8KMZ':
fileNameIn, readFromOneFile = getNS8KMZfilename(startdate.year, startdate.month, startdate.day, "S", dataPath)
# Finalize creating the model grd object now that we know the filename for input data
print fileNameIn
confM2R.grdMODEL.openNetCDF(fileNameIn)
confM2R.grdMODEL.createObject()
confM2R.grdMODEL.getDims()
if (confM2R.useesmf):
print("=>Creating the interpolation weights and indexes using ESMF (this may take some time....):")
print(" -> regridSrc2Dst at RHO points")
confM2R.grdMODEL.fieldSrc = ESMF.Field(confM2R.grdMODEL.esmfgrid, "fieldSrc", staggerloc=ESMF.StaggerLoc.CENTER)
confM2R.grdMODEL.fieldDst_rho = ESMF.Field(confM2R.grdROMS.esmfgrid, "fieldDst", staggerloc=ESMF.StaggerLoc.CENTER)
confM2R.grdMODEL.regridSrc2Dst_rho = ESMF.Regrid(confM2R.grdMODEL.fieldSrc, confM2R.grdMODEL.fieldDst_rho, regrid_method=ESMF.RegridMethod.BILINEAR, unmapped_action=ESMF.UnmappedAction.IGNORE)
print(" -> regridSrc2Dst at U points")
confM2R.grdMODEL.fieldSrc = ESMF.Field(confM2R.grdMODEL.esmfgrid, "fieldSrc", staggerloc=ESMF.StaggerLoc.CENTER)
confM2R.grdMODEL.fieldDst_u = ESMF.Field(confM2R.grdROMS.esmfgrid_u, "fieldDst_u", staggerloc=ESMF.StaggerLoc.CENTER)
confM2R.grdMODEL.regridSrc2Dst_u = ESMF.Regrid(confM2R.grdMODEL.fieldSrc, confM2R.grdMODEL.fieldDst_u, regrid_method=ESMF.RegridMethod.BILINEAR, unmapped_action=ESMF.UnmappedAction.IGNORE)
print(" -> regridSrc2Dst at V points")
confM2R.grdMODEL.fieldSrc = ESMF.Field(confM2R.grdMODEL.esmfgrid, "fieldSrc", staggerloc=ESMF.StaggerLoc.CENTER)
confM2R.grdMODEL.fieldDst_v = ESMF.Field(confM2R.grdROMS.esmfgrid_v, "fieldDst_v", staggerloc=ESMF.StaggerLoc.CENTER)
confM2R.grdMODEL.regridSrc2Dst_v = ESMF.Regrid(confM2R.grdMODEL.fieldSrc, confM2R.grdMODEL.fieldDst_v, regrid_method=ESMF.RegridMethod.BILINEAR, unmapped_action=ESMF.UnmappedAction.IGNORE)
# Now we want to subset the data to avoid storing more information than we need.
# We do this by finding the indices of maximum and minimum latitude and longitude in the matrixes
if confM2R.subsetindata:
IOsubset.findSubsetIndices(confM2R.grdMODEL, min_lat=confM2R.subset[0], max_lat=confM2R.subset[1],
min_lon=confM2R.subset[2], max_lon=confM2R.subset[3])
print('Initializing done')
print('\n--------------------------')
time = 0; firstRun = True
for year in confM2R.years:
months = datetimeFunctions.createListOfMonths(year,confM2R.startdate,confM2R.enddate,confM2R.isclimatology)
for month in months:
days = datetimeFunctions.createListOfDays(year,month,confM2R.startdate,confM2R.enddate,confM2R.isclimatology,confM2R.timefrequencyofinputdata)
for day in days:
# Get the current date for given timestep
getTime(confM2R.modelpath, confM2R.indatatype, confM2R.grdROMS, confM2R.grdMODEL, year, month, day, time, firstRun)
# Each MODEL file consist only of one time step. Get the subset data selected, and
# store that time step in a new array:
if firstRun is True:
print("=>NOTE! Make sure that these two arrays are in sequential order:")
print("==>myvars: %s" % confM2R.inputdatavarnames)
print("==>varNames %s" % confM2R.globalvarnames)
firstRun = False
if confM2R.subsetindata:
# The first iteration we want to organize the subset indices we want to extract
# from the input data to get the interpolation correct and to function fast
IOsubset.organizeSplit(confM2R.grdMODEL, confM2R.grdROMS)
for myvar in confM2R.globalvarnames:
if myvar in ['temperature', 'salinity', 'uvel', 'vvel']:
data = get3Ddata(confM2R.grdROMS, confM2R.grdMODEL, myvar, confM2R.indatatype, year, month, day, confM2R.modelpath)
if myvar in ['ssh', 'ageice', 'uice', 'vice', 'aice', 'hice','snow_thick']:
data = get2Ddata(confM2R.grdROMS, confM2R.grdMODEL, myvar, confM2R.indatatype, year, month, day, confM2R.modelpath)
# Take the input data and horizontally interpolate to your grid
array1 = HorizontalInterpolation(myvar, confM2R.grdROMS, confM2R.grdMODEL, data, confM2R.showprogress, confM2R.usefilter)
if myvar in ['temperature', 'salinity']:
STdata = VerticalInterpolation(myvar, array1, array1, confM2R.grdROMS, confM2R.grdMODEL)
for dd in range(len(STdata[:,0,0])):
STdata[dd,:,:] = np.where(confM2R.grdROMS.mask_rho == 0, confM2R.grdROMS.fill_value, STdata[dd,:,:])
STdata = np.where(abs(STdata) > 1000, confM2R.grdROMS.fill_value, STdata)
IOwrite.writeClimFile(confM2R.grdROMS, time, confM2R.climname, myvar,confM2R. isclimatology, confM2R.writeice,
confM2R.indatatype, confM2R.myformat, STdata)
if time == confM2R.grdROMS.initTime and confM2R.grdROMS.write_init is True:
IOinitial.createInitFile(confM2R.grdROMS, time, confM2R.initname, myvar, confM2R.writeice,
confM2R.indatatype, confM2R.myformat, STdata)
if myvar in ['ssh', 'ageice', 'aice', 'hice', 'snow_thick']:
SSHdata = array1[0, :, :]
SSHdata = np.where(confM2R.grdROMS.mask_rho == 0, confM2R.grdROMS.fill_value, SSHdata)