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model2roms.py
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from __future__ import print_function
from netCDF4 import Dataset, date2num, num2date
from datetime import datetime, timedelta
import numpy as np
import interp2D
import interpolation as interp
import IOwrite
import os
import barotropic
import IOinitial
import IOsubset
import datetimeFunctions
import forcingFilenames as fc
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(2019, 3, 13)
__version__ = "1.8"
__status__ = "Development, modified on 15.08.2008,01.10.2009,07.01.2010, 15.07.2014, 01.12.2014, 07.08.2015, 08.02.2018, 04.03.2019, 13.03.2019"
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','O3_c','O3_TA','N1_p','N3_n','N5_s','O2_o']:
print('\nStart vertical interpolation for %s (dimensions=%s x %s)' % (myvar, grdROMS.xi_rho, grdROMS.eta_rho))
outdata = np.empty((outINDEX_ST), dtype=np.float, order='Fortran')
outdata = interp.interpolation.dovertinter(np.asarray(outdata, order='F'),
np.asarray(array1, order='F'),
np.asarray(grdROMS.h, order='F'),
np.asarray(grdROMS.z_r, order='F'),
np.asarray(grdMODEL.z_r, order='F'),
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)
# The BCG has to be capped at 0
if myvar in ['O3_c','O3_TA','N1_p','N3_p','N3_n','N5_s','O2_o']:
outdata = np.ma.masked_where(abs(outdata) < 0, outdata)
# import plotData
# for k in range(grdROMS.nlevels):
# plotData.contourMap(grdROMS, grdROMS.lon_rho, grdROMS.lat_rho, np.squeeze(outdata[k,:,:]),k, myvar)
return outdata
if myvar == 'vvel':
print('\nStart vertical interpolation for uvel (dimensions=%s x %s)' % (grdROMS.xi_u, grdROMS.eta_u))
outdataU = np.zeros((outINDEX_U), dtype=np.float)
outdataUBAR = np.zeros((outINDEX_UBAR), dtype=np.float)
outdataU = interp.interpolation.dovertinter(np.asarray(outdataU, order='F'),
np.asarray(array1, order='F'),
np.asarray(grdROMS.h, order='F'),
np.asarray(grdROMS.z_r, order='F'),
np.asarray(grdMODEL.z_r, order='F'),
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('\nStart vertical interpolation for vvel (dimensions=%s x %s)' % (grdROMS.xi_v, grdROMS.eta_v))
outdataV = np.zeros((outINDEX_V), dtype=np.float)
outdataVBAR = np.zeros((outINDEX_VBAR), dtype=np.float)
outdataV = interp.interpolation.dovertinter(np.asarray(outdataV, order='F'),
np.asarray(array2, order='F'),
np.asarray(grdROMS.h, order='F'),
np.asarray(grdROMS.z_r, order='F'),
np.asarray(grdMODEL.z_r, order='F'),
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.float)
z_wv = np.zeros((grdROMS.nlevels + 1, grdROMS.eta_v, grdROMS.xi_v), dtype=np.float)
outdataUBAR = barotropic.velocity.ubar(np.asarray(outdataU, order='F'),
np.asarray(outdataUBAR, order='F'),
np.asarray(grdROMS.z_w, order='F'),
np.asarray(z_wu, order='F'),
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='F'),
np.asarray(outdataVBAR, order='F'),
np.asarray(grdROMS.z_w, order='F'),
np.asarray(z_wv, order='F'),
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 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.float)
vrot = np.zeros((int(grdMODEL.nlevels), int(grdROMS.eta_rho), int(grdROMS.xi_rho)), np.float)
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.float)
Zv = np.zeros((int(grdMODEL.nlevels), int(grdROMS.eta_v), int(grdROMS.xi_v)), np.float)
# 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(confM2R, year, month, day, ntime):
"""
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 confM2R.oceanindatatype == 'SODA':
filename = fc.getSODAfilename(confM2R, year, month, day, None)
if confM2R.oceanindatatype == 'SODA3':
filename = fc.getSODA3filename(confM2R, year, month, day, None)
if confM2R.oceanindatatype == 'SODA3_5DAY':
filename = fc.getSODA3_5DAYfilename(confM2R, year, month, day, None)
if confM2R.oceanindatatype == 'SODAMONTHLY':
filename = fc.getSODAMONTHLYfilename(confM2R, year, month, None)
if confM2R.oceanindatatype == 'GLORYS':
filename = fc.getGLORYSfilename(confM2R, year, month, "S")
if confM2R.oceanindatatype == 'WOAMONTHLY':
filename = fc.getWOAMONTHLYfilename(confM2R, year, month, "temperature")
if confM2R.oceanindatatype == 'NORESM':
filename = fc.getNORESMfilename(confM2R, year, month, "salnlvl")
if confM2R.oceanindatatype == 'NS8KM':
filename = fc.getNS8KMfilename(confM2R, year, month, "salt")
if confM2R.oceanindatatype == 'NS8KMZ':
filename, readFromOneFile = fc.getNS8KMZfilename(confM2R, year, month, "salt")
# Now open the input file and get the time
cdf = Dataset(filename)
if confM2R.oceanindatatype == 'NORESM':
jdref = date2num(datetime(1948, 1, 1), units="days since 1948-01-01 00:00:00", calendar="standard")
elif confM2R.oceanindatatype == 'NS8KMZ':
jdref = date2num(datetime(1948, 1, 1), units="days since 1948-01-01 00:00:00", calendar="standard")
elif confM2R.oceanindatatype == 'GLORYS':
jdref = date2num(datetime(1948, 1, 1), cdf.variables["time_counter"].units,
calendar=cdf.variables["time_counter"].calendar)
elif confM2R.oceanindatatype == 'NS8KM':
jdref = date2num(datetime(1948, 1, 1), cdf.variables["ocean_time"].units,
calendar=cdf.variables["ocean_time"].calendar)
elif confM2R.oceanindatatype == 'SODA3':
jdref = date2num(datetime(1948, 1, 1), units="days since 1948-01-01 00:00:00", calendar="standard")
elif confM2R.oceanindatatype == 'SODA3_5DAY':
jdref = date2num(datetime(1948, 1, 1), units=confM2R.timeobject.units, calendar=confM2R.timeobject.calendar)
else:
jdref = date2num(datetime(1948, 1, 1), cdf.variables["time"].units, calendar=cdf.variables["time"].calendar)
if confM2R.oceanindatatype == '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 confM2R.oceanindatatype == 'SODA3_5DAY':
currentdate = datetime(year, month, day)
myunits=confM2R.timeobject.units
jd = date2num(currentdate, units=confM2R.timeobject.units, calendar=confM2R.timeobject.calendar)
if confM2R.oceanindatatype == 'SODA3':
# Each SODA file represents 12 month averages.
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 confM2R.oceanindatatype == '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 confM2R.oceanindatatype == '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 confM2R.oceanindatatype == '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.
currentdate = datetime(year, month, day)
myunits = cdf.variables["time"].units
jd = date2num(currentdate, myunits, calendar="gregorian")
print("Days:", jd, currentdate, year, month, day)
if confM2R.oceanindatatype == '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"][ntime]
mycalendar = cdf.variables["time"].calendar
myunits = cdf.variables["time"].units
# For NORESM we switch from in-units of 1800-01-01 to outunits of 1948-01-01
currentdate = num2date(mydays, units=myunits, calendar=mycalendar)
jd = date2num(currentdate,'days since 1948-01-01 00:00:00', calendar='noleap')
confM2R.grdROMS.time = (jd - jdref)
confM2R.grdROMS.reftime = jdref
confM2R.grdROMS.timeunits = myunits
cdf.close()
print("-------------------------------")
print('\nCurrent time of %s file : %s' % (confM2R.oceanindatatype, currentdate))
print("-------------------------------")
def get3ddata(confM2R, myvar, year, month, day, timecounter):
varN=confM2R.globalvarnames.index(myvar)
# The variable splitExtract is defined in IOsubset.py and depends on the orientation
# and oceanindatatype of grid (-180-180 or 0-360). Assumes regular grid.
if confM2R.useesmf:
filename = fc.getFilename(confM2R,year,month,day,confM2R.inputdatavarnames[varN])
print(filename,confM2R.inputdatavarnames[varN])
try:
cdf = Dataset(filename)
except:
print("Unable to open input file {}".format(filename))
return
if confM2R.oceanindatatype in ["SODA","SODA3_5DAY"]:
data = cdf.variables[confM2R.inputdatavarnames[varN]][0,:,:,:]
if confM2R.oceanindatatype == "SODA3":
data = cdf.variables[confM2R.inputdatavarnames[varN]][month-1,:,:,:]
if confM2R.oceanindatatype == "SODAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][:, :, :]
if confM2R.oceanindatatype == "WOAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][month-1,:,:,:]
if confM2R.oceanindatatype == "NORESM":
# For NorESM data - all data is in one big file so we need the timecounter to access correct data
myunits = cdf.variables[str(confM2R.inputdatavarnames[varN])].units
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][timecounter,:,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
if confM2R.oceanindatatype == "GLORYS":
myunits = cdf.variables[str(confM2R.inputdatavarnames[varN])].units
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][0,:,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
cdf.close()
if myvar == 'temperature' and confM2R.oceanindatatype in ["NS8KMZ", "GLORYS", "NORESM"]:
if myunits == "degree_Kelvin" or myunits == "K":
if confM2R.oceanindatatype in ["GLORYS"]:
data = np.where(data <= -32.767, confM2R.grdROMS.fillval, data)
data = data - 273.15
if confM2R.oceanindatatype == "GLORYS":
data = np.where(data <= -32.767, confM2R.grdROMS.fillval, data)
data = np.ma.masked_where(data <= confM2R.grdROMS.fillval, data)
if __debug__:
print("Data range of {} just after extracting from netcdf file: {:3.3f}-{:3.3f}".format(str(confM2R.inputdatavarnames[varN]),
float(data.min()), float(data.max())))
return data
def get2ddata(confM2R, myvar, year, month, day, timecounter):
varN=confM2R.globalvarnames.index(myvar)
if confM2R.useesmf:
if confM2R.set2DvarsToZero and confM2R.inputdatavarnames[varN] in ['ageice', 'uice', 'vice', 'aice', 'hice','hs']:
return np.zeros((np.shape(confM2R.grdMODEL.lon)))
else:
filename = fc.getFilename(confM2R,year,month,day,confM2R.inputdatavarnames[varN])
try:
cdf = Dataset(filename)
except:
print("Unable to open input file {}".format(filename))
return
if confM2R.oceanindatatype in ["SODA","SODA3_5DAY"]:
data = cdf.variables[confM2R.inputdatavarnames[varN]][0,:,:]
if confM2R.oceanindatatype == "SODA3":
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[confM2R.inputdatavarnames[varN]][int(month-1),0,:,:]
else:
data = cdf.variables[confM2R.inputdatavarnames[varN]][int(month-1),:,:]
if confM2R.oceanindatatype == "SODAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][:, :]
if confM2R.oceanindatatype == "WOAMONTHLY":
data = cdf.variables[str(confM2R.inputdatavarnames[varN])][month-1,:,:]
if (confM2R.oceanindatatype == "NORESM" and confM2R.set2DvarsToZero is False):
# 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.
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][timecounter,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
if confM2R.oceanindatatype == "GLORYS":
data = np.squeeze(cdf.variables[str(confM2R.inputdatavarnames[varN])][0,:,:])
data = np.where(data.mask, confM2R.grdROMS.fillval, data)
if not confM2R.set2DvarsToZero: cdf.close()
if __debug__ and not confM2R.set2DvarsToZero:
print("Data range of {} just after extracting from netcdf file: {:3.3f}-{:3.3f}".format(str(confM2R.inputdatavarnames[varN]),
float(data.min()), float(data.max())))
return data
def convertMODEL2ROMS(confM2R):
# First opening of input file is just for initialization of grid
filenamein = fc.getFilename(confM2R,confM2R.start_year,confM2R.start_month,confM2R.start_day,None)
# Finalize creating the model grd object now that we know the filename for input data
confM2R.grdMODEL.opennetcdf(filenamein)
confM2R.grdMODEL.createobject(confM2R)
confM2R.grdMODEL.getdims()
# Create the ESMF weights used to do all of the horizontal interpolation
interp2D.setupESMFInterpolationWeights(confM2R)
# 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--------------------------')
print('==> Starting loop over time')
timecounter = 0
firstrun = True
for year in confM2R.years:
months = datetimeFunctions.createlistofmonths(confM2R, year)
for month in months:
days = datetimeFunctions.createlistofdays(confM2R, year, month)
print("days {}".format(days))
for day in days:
# Get the current date for given timestep
getTime(confM2R, year, month, day, timecounter)
# 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:
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','O3_c','O3_TA','N1_p','N3_n','N5_s','O2_o']:
data = get3ddata(confM2R, myvar, year, month, day, timecounter)
if myvar in ['ssh', 'ageice', 'uice', 'vice', 'aice', 'hice', 'snow_thick']:
data = get2ddata(confM2R, myvar, year, month, day, timecounter)
# Take the input data and horizontally interpolate to your grid
array1 = interp2D.dohorinterpolationregulargrid(confM2R, data, myvar)
if myvar in ['temperature','salinity','O3_c','O3_TA','N1_p','N3_n','N5_s','O2_o']:
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.fillval,
STdata[dd, :, :])
STdata = np.where(abs(STdata) > 1000, confM2R.grdROMS.fillval, STdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar, STdata)
if timecounter == confM2R.grdROMS.inittime and confM2R.grdROMS.write_init is True:
IOinitial.createinitfile(confM2R, timecounter, myvar, STdata)
if myvar in ['ssh','ageice','aice','hice','snow_thick']:
SSHdata = array1[0, :, :]
SSHdata = np.where(confM2R.grdROMS.mask_rho == 0, confM2R.grdROMS.fillval, SSHdata)
SSHdata = np.where(abs(SSHdata) > 100, confM2R.grdROMS.fillval, SSHdata)
SSHdata = np.where(abs(SSHdata) == 0, confM2R.grdROMS.fillval, SSHdata)
# Specific for ROMs. We set 0 where we should have fillvalue for ice otherwise ROMS blows up.
SSHdata = np.where(abs(SSHdata) == confM2R.grdROMS.fillval, 0, SSHdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar, SSHdata)
if timecounter == confM2R.grdROMS.inittime:
IOinitial.createinitfile(confM2R, timecounter, myvar, SSHdata)
# The following are special routines used to calculate the u and v velocity
# of ice based on the transport, which is divided by snow and ice thickenss
# and then multiplied by grid size in dx or dy direction (opposite of transport).
if myvar in ['uice','vice']:
SSHdata = array1[0, :, :]
if myvar == "uice": mymask = confM2R.grdROMS.mask_u
if myvar == "vice": mymask = confM2R.grdROMS.mask_v
SSHdata = np.where(mymask == 0, confM2R.grdROMS.fillval, SSHdata)
SSHdata = np.where(abs(SSHdata) > 100, confM2R.grdROMS.fillval, SSHdata)
SSHdata = np.where(abs(SSHdata) == 0, confM2R.grdROMS.fillval, SSHdata)
SSHdata = np.where(abs(SSHdata) == confM2R.grdROMS.fillval, 0, SSHdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar, SSHdata)
if timecounter == confM2R.grdROMS.inittime:
if myvar == 'uice':
IOinitial.createinitfile(confM2R, timecounter, myvar, SSHdata)
if myvar == 'vice':
IOinitial.createinitfile(confM2R, timecounter, myvar, SSHdata)
if myvar == 'uvel':
array2 = array1
if myvar == 'vvel':
urot, vrot = rotate(confM2R.grdROMS, confM2R.grdMODEL, data, array2, array1)
u, v = interpolate2uv(confM2R.grdROMS, confM2R.grdMODEL, urot, vrot)
Udata, Vdata, UBARdata, VBARdata = verticalinterpolation(myvar, u, v, confM2R.grdROMS,
confM2R.grdMODEL)
if myvar == 'vvel':
Udata = np.where(confM2R.grdROMS.mask_u == 0, confM2R.grdROMS.fillval, Udata)
Udata = np.where(abs(Udata) > 1000, confM2R.grdROMS.fillval, Udata)
Vdata = np.where(confM2R.grdROMS.mask_v == 0, confM2R.grdROMS.fillval, Vdata)
Vdata = np.where(abs(Vdata) > 1000, confM2R.grdROMS.fillval, Vdata)
UBARdata = np.where(confM2R.grdROMS.mask_u == 0, confM2R.grdROMS.fillval, UBARdata)
UBARdata = np.where(abs(UBARdata) > 1000, confM2R.grdROMS.fillval, UBARdata)
VBARdata = np.where(confM2R.grdROMS.mask_v == 0, confM2R.grdROMS.fillval, VBARdata)
VBARdata = np.where(abs(VBARdata) > 1000, confM2R.grdROMS.fillval, VBARdata)
IOwrite.writeclimfile(confM2R, timecounter, myvar, Udata, Vdata, UBARdata, VBARdata)
if timecounter == confM2R.grdROMS.inittime:
IOinitial.createinitfile(confM2R, timecounter, myvar, Udata, Vdata, UBARdata, VBARdata)
timecounter+=1