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Commit 529f0301 authored by jelt's avatar jelt
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Add bathy processing files

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SCRIPTS/Generate_NEMO_Forcing.py 0 → 100755
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#
#====================== DOCSTRING ============================
"""
Generate ERA5 atmospheric forcing for NEMO
So far to prduce a year by a year - need to be automated
--------------------------------------------------------------
"""
__author__ = "Nicolas Bruneau"
__copyright__ = "Copyright 2018, NOC"
__email__ = "nibrun@noc.ac.uk"
__date__ = "2018-05"
#====================== USR PARAMS ===========================
Year_init = 2016 ## First year to process
Year_end = 2017 ## Last one [included]
East = 110 ## East Border
West = 60 ## West Border
North = 30 ## North Border
South = 0 ## South Border
path_ERA5 = '/projectsa/NEMO/Forcing/ERA5/' ## ROOT PATH OD ERA5 DATA
path_EXTRACT = './EXTRACTION_AMM7/' ## WHERE TO EXTRACT YOUR REGION
path_FORCING = './FORCING/' ## NEMO FORCING
clean = False ## Clean extraction (longest bit)
sph_ON = True ## Compute specific humidity or not
#================== NEMO DOCUMENTATION =======================
"""
See the manual in section SBC for more details on the way data
are used in NEMO
The time variable from the netcdf is not used
"""
#====================== LOAD MODULES =========================
import os, sys, glob
import subprocess
import numpy as np
import datetime
from netCDF4 import Dataset, MFDataset
import netcdftime
import matplotlib.pyplot as plt
from matplotlib.mlab import griddata
import scipy.spatial.qhull as qhull
#====================== VARIABLE DEF =========================
var_path = { 't2m' : 'INST', \
'msl' : 'INST', \
'u10' : 'INST', \
'v10' : 'INST', \
'strd' : "CUMUL", \
'ssrd' : "CUMUL", \
'sf' : "CUMUL", \
'tp' : "CUMUL", \
}
#var_path = { 'msl' : 'INST', \
# 'u10' : 'INST', \
# 'strd' : "CUMUL", \
# 'ssrd' : "CUMUL", \
# 'sf' : "CUMUL", \
# }
#var_path = { 't2m' : 'INST', \
# 'v10' : 'INST', \
# 'tp' : "CUMUL", \
# }
if sph_ON :
var_path[ 'sp' ] = 'INST'
var_path[ 'd2m' ] = 'INST'
#===================== INTERNAL FCTNS ========================
help_tosec = np.vectorize( lambda x : x.total_seconds() )
def Read_NetCDF( fname, KeyVar ) :
'Read NetCDF file'
if "*" in fname : nc = MFDataset( fname, 'r' )
else : nc = Dataset( fname, 'r' )
## Get time using the time variable
Time_Var = nc.variables[ 'time']
dt = Time_Var[:][1] - Time_Var[:][0]
Time_H = np.arange( Time_Var[:][0], Time_Var[:][0]+dt*Time_Var[:].size, dt )
try : Tref = netcdftime.utime( Time_Var.units, calendar = Time_Var.calendar )
except : Tref = netcdftime.utime( Time_Var.units, calendar = "gregorian" )
Time = Tref.num2date( Time_H )
print "====================++++"
## Get Coordinates
try :
Lon = nc.variables[ 'longitude' ][:]
Lat = nc.variables[ 'latitude' ][:]
LON, LAT = np.meshgrid( Lon, Lat )
except :
LON = nc.variables[ 'lon' ][:]
LAT = nc.variables[ 'lat' ][:]
## Get Variable
dum = nc.variables[ KeyVar ]
Var = dum[:]
ind = ( Var == dum._FillValue )
Var[ind] = np.nan
#Var = Var / dum.scale_factor + dum.add_offset
ind = (np.isnan(Var))
Var[ind] = -9999999
print Time[0], Time[-1], Var.shape, Time.shape, np.sum(ind)
try : return Time, LON, LAT, Var, dum.units, dum.long_name
except : return Time, LON, LAT, Var, dum.units, dum.standard_name
#=================== MANIPULATE NetCDF =======================
def compute_scale_and_offset( Var, n ):
'http://james.hiebert.name/blog/work/2015/04/18/NetCDF-Scale-Factors/'
Vmin = np.nanmin( Var )
Vmax = np.nanmax( Var )
print "scaleoffset", Vmin, Vmax
# stretch/compress data to the available packed range
scale_factor = (Vmax - Vmin) / (2 ** n - 1)
# translate the range to be symmetric about zero
add_offset = Vmin + 2 ** (n - 1) * scale_factor
return scale_factor, add_offset
def Add_Variable( nc, vName, vDim, vVal, long_name=None, units=None, standard_name=None, fill_value=None) :
"Add a variable with its attributes in a netcdf file"
if vName not in ['time','lon','lat',] : fprec = 'i'
else : fprec = 'f8'
if fill_value != None : nc.createVariable( vName, fprec, vDim, fill_value=fill_value, zlib=True, complevel=5 )
else : nc.createVariable( vName, fprec, vDim, zlib=True, complevel=5 )
if long_name != None : nc.variables[ vName ].long_name = long_name
if units != None : nc.variables[ vName ].units = units
if standard_name != None : nc.variables[ vName ].standard_name = standard_name
if vName not in ['time','lon','lat',] :
sc, off = compute_scale_and_offset( vVal, 16 )
nc.variables[ vName ].scale_factor = sc
nc.variables[ vName ].add_offset = off
nc.variables[ vName ][:] = vVal # np.floor((vVal-off)/sc)
def Create_Dimensions( nc, lon_name, nLon, lat_name, nLat ) :
"Create NetCDF dimensions time, nx, ny"
nc.createDimension( lon_name , nLon )
nc.createDimension( lat_name , nLat )
nc.createDimension( 'time' , None )
def Create_NetCDF_core( nc, tDim, tRef, tVal, sDim, sVal_lon, sVal_lat ) :
"Create Lon, Lat and Time variables"
# WRITE TIME INFO
tUnit = "days since {0} UTC".format( tRef.strftime( "%Y-%m-%d %H:%M:%S" ) ); tCal = "standard"
Tref = netcdftime.utime( tUnit, calendar = tCal )
Add_Variable( nc, 'time', ('time'), Tref.date2num( tVal ), \
long_name = "time since {0}".format(tUnit) , \
units = tUnit )
nc.variables['time'].calendar = tCal
#nc.variables['time'].base_date = np.array( [ tRef.year, tRef.month, tRef.day, tRef.hour ] )
# WRITE LON INFO
Add_Variable( nc, 'lon', sDim, sVal_lon, long_name = 'Longitude', \
units = 'degree_east', standard_name = 'longitude' )
# WRITE L INFOT
Add_Variable( nc, 'lat', sDim, sVal_lat, long_name = 'Latitude', \
units = 'degree_north', standard_name = 'latitude' )
def Create_Attributes( nc ) :
"Add some info - I do it at the end as I had issue with not properly readable netcdf if not"
nc.Description = 'ERA5 Atmospheric conditions for AMM15 NEMO3.6'
nc.Author = 'Prepare_ERA5.py'
nc.Created = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
nc.Conventions = "CF-1.0"
nc.close()
#======================= EXTRACTION ==========================
def Extract( fin, fout, clean=True ) :
if clean : os.system( "rm {0}".format( fout ) )
if not os.path.exists( fout ) :
command = "ncks -d latitude,{0},{1} -d longitude,{2},{3} {4} {5}".format( np.float(South), np.float(North),\
np.float(West), np.float(East), fin, fout )
print command
os.system( command )
def datetime_range(start, end, delta):
current = [start, ]
while current[-1] < end:
current.append( current[-1]+delta )
return np.array(current)
#======================= CORE PROGR ==========================
## load NCO
os.system( "module load nco/gcc/4.4.2.ncwa" )
os.system( "mkdir {0} {1}".format( path_EXTRACT, path_FORCING ) )
if West < 0 : West = 360.+West
if East < 0 : East = 360.+East
## Loop over each variable
for iVar, pVar in var_path.iteritems() :
print "================== {0} - {1} ==================".format( iVar, pVar )
##---------- EXTRACT ALL DATA FOR DOMAIN ----------------
for iY in range( Year_init, Year_end+1 ) :
## Files
finput = "{0}/{1}/{2}/{2}_{3}.nc".format( path_ERA5, pVar, iVar, iY )
foutput = "./{2}/{0}_{1}.nc".format( iVar, iY, path_EXTRACT )
## Extract the subdomain
Extract( finput, foutput, clean=clean )
##---------- LOAD FULLL TIME SERIES IN MEMORY -----------
Time, Lon, Lat, dum, Units, Name = Read_NetCDF( "./{1}/{0}_*.nc".format( iVar, path_EXTRACT ), iVar )
dt = Time[1] - Time[0] ## assume to be constant in time
dt2 = datetime.timedelta( seconds=dt.total_seconds() / 2. )
##---------- SOME PREPROCESSING -------------------------
if pVar == 'INST' :
## Add time step for last hour - copy the last input
dumA = np.concatenate( [ dum, dum[-1][np.newaxis,...] ], axis = 0 )
TimeA = np.array( Time.tolist() + [Time[-1],] )
## instantaneous field every hour. we center it in mid-time step (00:30) as it
## is what NEMO assumes according to documentation
dumC = ( dumA[0:-1] + dumA[1::] ) / 2.0
TimeC = TimeA[0:-1] + dt2 ## shift half time step positively due to averaging
suffix = ''
elif pVar == 'CUMUL' :
## Add time step for first 6h - copy the first input
nshift = 6
dumA = np.repeat( dum[None,0,...], nshift, axis=0 )
dumA = np.concatenate( [dumA,dum], axis = 0 )
TimeA = np.array( [ Time[0]-dt*(nshift-i) for i in range(nshift) ] )
TimeA = np.concatenate( [TimeA, Time], axis = 0 )
## Cumulated field comes from forecast and are therefore shifter in time
## we need to account for it properly for the beginning of the time series.
## first value is reapeated
dumC = dumA / dt.total_seconds() ## convert as a rate
TimeC = TimeA - dt2 ## shift half time step negatively as cumulated period
suffix = '/s'
if iVar in [ 'sf', 'tp', ]:
print "convert m to mm for", iVar
dumC *= 1000.
Units = 'mm'
if iVar in [ 'ssrd', 'strd', ]:
dumC[(dumC<0.)] = 0.
print "negative values set to 0", iVar
else :
print "Values in var_path variables are expected to be CUMUL or INST"
print " given {0} for {1}".format( pVar, iVar )
sys.exit()
##---------- OUTPUT A FILE PER YEAR ---------------------
for iY in range( Year_init, Year_end+1 ) :
indT = ( TimeC >= datetime.datetime( iY ,1,1 ) ) \
* ( TimeC < datetime.datetime( iY+1,1,1 ) )
if iVar in [ "d2m", "sp" ] :
Fout = "./{2}/forSPH_ERA5_{0}_y{1}.nc".format( iVar.upper(), iY, path_FORCING )
else : Fout = "./{2}/ERA5_{0}_y{1}.nc".format( iVar.upper(), iY, path_FORCING )
nc = Dataset( Fout, 'w', format='NETCDF4_CLASSIC')
Create_Dimensions ( nc, 'nLon', Lon.shape[1], 'nLat' , Lat.shape[0] )
Create_NetCDF_core( nc, ('time'), TimeC[indT][0], TimeC[indT], ('nLat', 'nLon'), Lon[::-1,:], Lat[::-1,:] )
Add_Variable( nc, iVar.upper(), ( 'time', 'nLat', 'nLon'), dumC[indT,::-1,:], units=Units+suffix, standard_name=Name, fill_value=-999999 )
Create_Attributes( nc )
##---------- PROCESS SPECIFIC HUMIDITY ----------------------
## Compute Specific Humidity according to ECMWF documentation
if sph_ON :
for iY in range( Year_init, Year_end+1 ) :
Time, Lon, Lat, d2m, dUnits, dName = Read_NetCDF( "./{1}/forSPH_ERA5_D2M_y{0}.nc".format( iY, path_FORCING ), 'D2M' )
Time, Lon, Lat, sp , dUnits, dName = Read_NetCDF( "./{1}/forSPH_ERA5_SP_y{0}.nc" .format( iY, path_FORCING ), 'SP' )
esat = 611.21 * np.exp( 17.502 * (d2m-273.16) / (d2m-32.19) )
dyrvap = 287.0597 / 461.5250
dVar = dyrvap * esat / ( sp - (1-dyrvap) * esat)
Units = "1"; Name = "Specific Humidity"
indT = ( Time >= datetime.datetime( iY ,1,1 ) ) \
* ( Time < datetime.datetime( iY+1,1,1 ) )
Fout = "./{1}/ERA5_SPH_y{0}.nc".format( iY, path_FORCING )
nc = Dataset( Fout, 'w', format='NETCDF4_CLASSIC')
Create_Dimensions ( nc, 'nLon', Lon.shape[1], 'nLat' , Lat.shape[0] )
Create_NetCDF_core( nc, ('time'), Time[indT][0], Time[indT], ('nLat', 'nLon'), Lon[:,:], Lat[:,:] )
Add_Variable( nc, "SPH", ( 'time', 'nLat', 'nLon'), dVar[indT,:,:], units=Units, standard_name=Name, fill_value=-999999 )
Create_Attributes( nc )
##---------- WRITE NAMELIST ---------------------------------
print "\n EXAMPLE of core namelist with ERA5 for NEMO"
print " the interpolation weights need to be generated using the usual method"
print " using: scripgrid.exe, scrip.exe, scripshape.exe from TOOLS/WEIGHTS/\n"
print " &namsbc_core"
print " cn_dir='./fluxes_ERA5/',"
print " ln_taudif=.false.,"
print " rn_pfac = 1.0,"
print " rn_vfac = 1.,"
print " rn_zqt = 2.,"
print " rn_zu = 10.,"
print " sn_humi='ERA5_SPH' ,1,'SPH' ,.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','',"
print " sn_prec='ERA5_TP' ,1,'TP' ,.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','',"
print " sn_qlw ='ERA5_STRD',1,'STRD',.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','',"
print " sn_qsr ='ERA5_SSRD',1,'SSRD',.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','',"
print " sn_snow='ERA5_SF' ,1,'SF' ,.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','',"
print " sn_tair='ERA5_T2M' ,1,'T2M' ,.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','',"
print " sn_wndi='ERA5_U10' ,1,'U10' ,.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','U1',"
print " sn_wndj='ERA5_V10' ,1,'V10' ,.true.,.false.,'yearly','weights_era5_amm15_bicubic.nc','V1',"
print " /\n"
sys.exit()
SCRIPTS/bathymetry_fill.m 0 → 100755
View file @ 529f0301
clear all
clc
fname='bathy_meter.nc';
B=ncread(fname,'Bathymetry');
x=ncread(fname,'nav_lon');
y=ncread(fname,'nav_lat');
B(isnan(B))=0;
% make landsea mask
A=-B;
A(A==0)=1;
A(A<0)=0;
% put land border on east coast
A(end,:)=1;
% fill closed spaces
A1=imfill(A,'holes');
B(A1==1)=nan;
% update bathymetry
ncid=netcdf.open(fname,'WRITE');
varid = netcdf.inqVarID(ncid,'Bathymetry');
netcdf.putVar(ncid,varid,B)
SCRIPTS/python_mask.py 0 → 100755
View file @ 529f0301
from netCDF4 import Dataset
import numpy as np
coorfile = './FORCING/ERA5_MSL_y2016.nc' ## One ERA forcing file generated previously
maskfile = 'my_era5_LSM.nc' ## Land Sea Mask from ncks
outfile = 'ERA5_LSM.nc' ## Output file
#----------------------------------------------------------------------------
## READ SRC BATHYMETRY
nc_c = Dataset( coorfile, 'r' )
lon_src = nc_c.variables[ 'lon' ][:]
lat_src = nc_c.variables[ 'lat' ][:]
nc_c.close()
print coorfile, "loaded", lon_src.shape
## READ SRC BATHYMETRY
nc_src = Dataset( maskfile, 'r' )
msk_src = nc_src.variables[ 'lsm' ][0,::-1]
print maskfile, "loaded", msk_src.shape
msk_src[(msk_src==0.)] = -1
## NETCDF OUTPUT
ncout = Dataset( outfile, 'w', format='NETCDF3_CLASSIC' )
ncout.createDimension( 'nlat', msk_src.shape[0] )
ncout.createDimension( 'nlon', msk_src.shape[1] )
lon = ncout.createVariable( 'lon', 'f4', ('nlat', 'nlon',), zlib='True' )
lat = ncout.createVariable( 'lat', 'f4', ('nlat', 'nlon',), zlib='True' )
lon[:] = lon_src; lat[:] = lat_src
bout = ncout.createVariable( "LSM", 'f4', ('nlat','nlon',), zlib='True', fill_value=-999. )
bout[:] = msk_src
ncout.close()
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