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make_pycfsr2frc_one-hourly.py
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make_pycfsr2frc_one-hourly.py
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# %run make_pycfsr2frc_one-hourly.py
'''
===========================================================================
This file is part of py-roms2roms
py-roms2roms is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
py-roms2roms is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with py-roms2roms. If not, see <http://www.gnu.org/licenses/>.
Version 1.0.1
Copyright (c) 2014 by Evan Mason, IMEDEA
Email: [email protected]
===========================================================================
Create a forcing file based on hourly CFSR data
===========================================================================
'''
import netCDF4 as netcdf
import matplotlib.pyplot as plt
import matplotlib.dates as dt
import scipy.spatial as sp
import numpy as np
import numexpr as ne
import time as time
import matplotlib.pyplot as plt
from collections import OrderedDict
#from datetime import datetime
from pycfsr2frc import RomsGrid, RomsData, CfsrData, CfsrGrid, CfsrMask, AirSea
from py_roms2roms import horizInterp
class CfsrDataHourly(RomsData):
'''
CFSR data class (inherits from RomsData class)
'''
def __init__(self, filename, varname, model_type, romsgrd, time_array, masks=None):
'''
'''
super(CfsrDataHourly, self).__init__(filename, model_type)
self.varname = varname
self.time_array = time_array
self._check_product_description()
self.needs_averaging_to_hour = False
self.needs_averaging_to_the_half_hour = False
self._set_averaging_approach()
self._set_start_end_dates()
self._datenum = self._get_time_series()
self._forecast_hour = self.read_nc('forecast_hour', indices='[:]')
if self.needs_averaging_to_the_half_hour:
self._set_averaging_weights()
#self._adjust_datenum()
self._start_averaging = False
self._lon = self.read_nc('lon', indices='[:]')
self._lat = self.read_nc('lat', indices='[:]')
self._lon, self._lat = np.meshgrid(self._lon,
self._lat[::-1])
self._get_metrics()
if masks is not None:
self._select_mask(masks)
self._maskr = self.cfsrmsk._maskr
self.fillmask_cof = self.cfsrmsk.fillmask_cof
else:
self.cfsrmsk = None
#self.fillmask_cof = None
self.romsgrd = romsgrd
self.datain = np.ma.empty(self.lon().shape)
if self.needs_averaging_to_hour:
self.previous_in = np.ma.empty(self.lon().shape)
elif self.needs_averaging_to_the_half_hour:
self.datatmp = np.ma.empty(self.lon().shape)
self.dataout = np.ma.empty(self.romsgrd.lon().size)
self.tind = None
self.tri = None
self.tri_all = None
self.dt = None
self.to_be_downscaled = False
self.needs_all_point_tri = False
def lon(self):
return self._lon
def lat(self):
return self._lat
def maskr(self):
''' Returns mask on rho points
'''
return self._maskr
'''----------------------------------------------------------------------------------
Methods to detect if CFSR instance data are forecasts, forecast averages or
analyses. If a forecast of either type, a second field at tind-1 must be read and
averaged appropriately to ensure all fields are at either 00, 06, 12, 18 hours.
'''
def _check_product_description(self):
''' Returns appropriate string
'''
self.product = self.read_nc_att(self.varname, 'product_description')
def _set_averaging_approach(self):
'''
Order: call after self._check_product_description()
'''
option_1 = ('Forecast', 'Analysis')
option_2 = ('1-hour Average', 'Average (reference date/time to valid date/time)')
if self.product in option_1 :
print '------ product "%s" hence using *valid_date_time*' %self.product
self.needs_averaging_to_hour = False
self.needs_averaging_to_the_half_hour = True
elif self.product in option_2:
print '------ product "%s" hence using *valid_date_time_range*' %self.product
self.needs_averaging_to_hour = True
self.needs_averaging_to_the_half_hour = False
else:
raise Exception, 'Undefined_product'
return self
def _set_averaging_weights(self):
'''
Order: call after self._check_product_description()
'''
basetime = dt.date2num(self.time_array[0])
# Get nearest two indices
inds = np.argsort(np.abs(self._datenum - basetime))[:2]
delta = np.diff(self._datenum[inds.min():inds.max()+1])
diff1 = np.diff([basetime, self._datenum[inds.max()]])
#print diff1, delta
self.time_avg_weights = np.array([diff1, delta - diff1])
self.time_avg_weights /= delta
return self
def print_weights(self):
'''
'''
try:
print '------ averaging weights for *%s* product: %s' %(self.product,
self.time_avg_weights)
except Exception:
print '------ no averaging weights for *%s* product' %self.product
return self
def _adjust_datenum(self):
'''
'''
np.add(self._datenum[:-1] * self.time_avg_weights[0],
self._datenum[1:] * self.time_avg_weights[1], out=self._datenum[:-1])
self._datenum[-1] = self._datenum[-2] + np.diff(self._datenum[:2])
def _get_data_time_average(self):
'''
Order: call after self._set_averaging_weights()
'''
#print self.time_avg_weights[0], self.time_avg_weights[1]
np.add(self.time_avg_weights[0] * self.datatmp,
self.time_avg_weights[1] * self.datain, out=self.datain)
return self
'''----------------------------------------------------------------------------------
'''
def _read_cfsr_frc(self, var, ind):
''' Read CFSR forcing variable (var) at record (ind)
'''
return self.read_nc(var, '[' + str(ind) + ']')[::-1]
def _get_cfsr_data(self, varname):
''' Get CFSR data with explicit variable name
'''
return self._read_cfsr_frc(varname, self.tind)
def _get_cfsr_datatmp(self):
''' Get CFSR data with implicit variable name
'''
#print self.tind, self.tind-1
self.datatmp[:] = self._read_cfsr_frc(self.varname, self.tind-1)
return self
def get_cfsr_data(self):
''' Get CFSR data with implicit variable name
'''
self.datain[:] = self._read_cfsr_frc(self.varname, self.tind)
if self.needs_averaging_to_the_half_hour:
#print 'ssssss'
self._get_cfsr_datatmp()
self._get_data_time_average()
return self
def get_cfsr_data_previous(self):
''' Get CFSR data with implicit variable name
'''
self.previous_in[:] = self._read_cfsr_frc(self.varname, self.tind - 1)
return self
def cfsr_data_subtract_averages(self):
''' Extract one hour average from accumulated averages
'''
self.datain *= self.forecast_hour()
self.previous_in *= (self.forecast_hour() - 1)
self.datain -= self.previous_in
return self
def _check_for_nans(self, message=None):
'''
'''
flat_mask = self.romsgrd.maskr().ravel()
assert not np.any(np.isnan(self.dataout[np.nonzero(flat_mask)])
), 'Nans in self.dataout sea points; hints: %s' %message
self.dataout[:] = np.nan_to_num(self.dataout)
return self
def _interp2romsgrd(self):
'''
'''
ball = self.cfsrmsk.cfsr_ball
interp = horizInterp(self.tri, self.datain.flat[ball])
self.dataout[self.romsgrd.idata()] = interp(self.romsgrd.points)
return self
def interp2romsgrd(self, fillmask=False):
'''
'''
if fillmask:
self.fillmask()
self._interp2romsgrd()
self._check_for_nans()
return self
def _set_start_end_dates(self):
'''
'''
if self.needs_averaging_to_hour:
self._start_date = self.read_nc('valid_date_time_range', '[0]')
self._end_date = self.read_nc('valid_date_time_range', '[-1]')
else:
self._start_date = self.read_nc('valid_date_time', '[0]')
self._end_date = self.read_nc('valid_date_time', '[-1]')
return self
def datenum(self):
return self._datenum
def set_date_index(self, dt):
if self.needs_averaging_to_hour:
try:
#print self.datenum().min(), self.datenum().max()
dt_bool = np.isclose(self.datenum(), dt, rtol=1e-10, atol=1e-8)
self.tind = np.nonzero(dt_bool)[0][0]
except Exception:
raise Exception, 'dt out of range in CFSR file'
else:
return self
else:
tind = np.argsort(np.abs(self._datenum - dt))[0]
if self.tind == tind:
self.tind += 1
else:
self.tind = tind
return self
def _date_from_string(self, date):
'''
Convert CFSR 'valid_date_time' to datenum
Input: date : ndarray (e.g., "'2' '0' '1' '0' '1' '2' '3' '1' '1' '8'")
'''
assert (isinstance(date, np.ndarray) and
date.size == 10), 'date must be size 10 ndarray'
return dt.datetime.datetime(np.int(date.tostring()[:4]),
np.int(date.tostring()[4:6]),
np.int(date.tostring()[6:8]),
np.int(date.tostring()[8:10]))
def _get_time_series(self):
'''
'''
if self.needs_averaging_to_hour:
date_str = self._date_from_string(self._start_date[1])
date_str -= dt.datetime.timedelta(minutes=30)
date_end = self._date_from_string(self._end_date[1])
date_end -= dt.datetime.timedelta(minutes=30)
else:
date_str = self._date_from_string(self._start_date)
date_end = self._date_from_string(self._end_date)
datelength = self.read_dim_size('time')
#print 'date_str, date_end, datelen', date_str, date_end, datelen
datenum = np.linspace(dt.date2num(date_str), dt.date2num(date_end), datelength)
return datenum
def _get_metrics(self):
'''Return array of metrics unique to this grid
(lonmin, lonmax, lon_res, latmin, latmax, lat_res)
where 'res' is resolution in degrees
'''
self_shape = self._lon.shape
lon_range = self.read_nc_att('lon', 'valid_range')
lon_mean, lat_mean = (self._lon.mean().round(2),
self._lat.mean().round(2))
res = np.diff(lon_range) / self._lon.shape[1]
self.metrics = np.hstack((self_shape[0], self_shape[1], lon_mean, lat_mean, res))
return self
def get_delaunay_tri(self):
'''
'''
self.points_all = np.copy(self.points)
self.tri_all = sp.Delaunay(self.points_all)
self.points = np.array([self.points[:,0].flat[self.cfsrmsk.cfsr_ball],
self.points[:,1].flat[self.cfsrmsk.cfsr_ball]]).T
self.tri = sp.Delaunay(self.points)
return self
def _select_mask(self, masks):
'''Loop over list of masks to find which one
has same grid dimensions as self
'''
for each_mask in masks:
if np.alltrue(each_mask.metrics == self.metrics):
self.cfsrmsk = each_mask
#self._maskr = each_mask._get_landmask()
return self
return None
def fillmask(self):
'''Fill missing values in an array with an average of nearest
neighbours
From http://permalink.gmane.org/gmane.comp.python.scientific.user/19610
Order: call after self.get_fillmask_cof()
'''
dist, iquery, igood, ibad = self.fillmask_cof
weight = dist / (dist.min(axis=1)[:,np.newaxis] * np.ones_like(dist))
np.place(weight, weight > 1., 0.)
xfill = weight * self.datain[igood[:,0][iquery], igood[:,1][iquery]]
xfill = (xfill / weight.sum(axis=1)[:,np.newaxis]).sum(axis=1)
self.datain[ibad[:,0], ibad[:,1]] = xfill
return self
def start_averaging(self):
'''
Ensure we start when *forecast_hour* == 1
'''
if self._forecast_hour[self.tind] == 1 or self._start_averaging:
self._start_averaging = True
return True
else:
return False
def forecast_hour(self):
''' Return the current forecast hour
'''
return self._forecast_hour[self.tind]
def check_vars_for_downscaling(self, var_instances):
'''Loop over list of grids to find which have
dimensions different from self
'''
for ind, each_grid in enumerate(var_instances):
if np.any(each_grid.metrics != self.metrics):
each_grid.to_be_downscaled = True
return self
class CfsrPrate(CfsrDataHourly):
'''CFSR Precipitation rate class (inherits from CfsrData class)
Responsible for one variable: 'prate'
'''
def __init__(self, cfsr_dir, prate_file, masks, romsgrd, time_array):
super(CfsrPrate, self).__init__(cfsr_dir + prate_file[0], prate_file[1], 'CFSR',
romsgrd, time_array, masks=masks)
#self._set_start_end_dates()
#self.select_mask(masks)
#self.get_fillmask_cof()
#self._maskr = np.ones(self.lon().shape)
def convert_cmday(self):
datain = self.datain
self.datain[:] = ne.evaluate('datain * 86400 * 0.1')
#self.datain *= 86400 # to days
#self.datain *= 0.1 # to cm
return self
class CfsrSST(CfsrDataHourly):
'''CFSR SST class (inherits from CfsrData class)
Responsible for one variable: 'SST'
'''
def __init__(self, cfsr_dir, sst_file, masks, romsgrd, time_array):
super(CfsrSST, self).__init__(cfsr_dir + sst_file[0], sst_file[1], 'CFSR',
romsgrd, time_array, masks=masks)
self.print_weights()
#self.get_fillmask_cof()
class CfsrRadlw(CfsrDataHourly):
'''CFSR Outgoing longwave radiation class (inherits from CfsrData class)
Responsible for two ROMS bulk variables: 'radlw' and 'radlw_in'
'''
def __init__(self, cfsr_dir, radlw_file, masks, romsgrd, time_array):
super(CfsrRadlw, self).__init__(cfsr_dir + radlw_file['shflux_LW_down'][0],
radlw_file['shflux_LW_down'][1], 'CFSR',
romsgrd, time_array, masks=masks)
self.down_varname = radlw_file['shflux_LW_down'][1]
self.up_varname = radlw_file['shflux_LW_up'][1]
#self.select_mask(masks)
#self.get_fillmask_cof()
self.radlw_datain = np.ma.empty(self.datain.shape)
self.radlw_in_datain = np.ma.empty(self.datain.shape)
self.radlw_dataout = np.ma.empty(self.romsgrd.lon().shape)
self.radlw_in_dataout = np.ma.empty(self.romsgrd.lon().shape)
self.numvars = 2
def _get_radlw(self, lw_up_datain):
# First, get radlw
radlw_in_datain = self.datain
self.radlw_datain[:] = ne.evaluate('lw_up_datain - radlw_in_datain')
#self.radlw_datain -= self.radlw_in_datain
def _get_radlw_in(self, sst_datain):
# Second, get radlw_in
eps, stefan = self.eps, self.Stefan
sst_datain[:] = ne.evaluate('sst_datain**4 * eps * stefan')
#sst_datain **= 4
#sst_datain *= self.eps
#sst_datain *= self.Stefan
radlw_datain = self.radlw_datain
self.radlw_in_datain[:] = ne.evaluate('-(radlw_datain - sst_datain)')
def get_cfsr_radlw_and_radlw_in(self, lw_up_datain, sst_datain):
self._get_radlw(lw_up_datain)
self._get_radlw_in(sst_datain)
return self
def interp2romsgrd(self, fillmask1=False, fillmask2=False):
self.datain[:] = self.radlw_datain
if fillmask1:
self.fillmask()
self._interp2romsgrd()._check_for_nans()
self.radlw_dataout[:] = self.dataout.reshape(self.romsgrd.lon().shape)
self.datain[:] = self.radlw_in_datain
if fillmask2:
self.fillmask()
self._interp2romsgrd()._check_for_nans()
self.radlw_in_dataout[:] = self.dataout.reshape(self.romsgrd.lon().shape)
return self
class CfsrRadlwUp(CfsrDataHourly):
'''
'''
def __init__(self, cfsr_dir, radlw_file, masks, romsgrd, time_array):
super(CfsrRadlwUp, self).__init__(cfsr_dir + radlw_file['shflux_LW_up'][0],
radlw_file['shflux_LW_up'][1], 'CFSR',
romsgrd, time_array, masks=masks)
class CfsrRadSwDown(CfsrDataHourly):
'''CFSR Outgoing longwave radiation class (inherits from CfsrDataHourly class)
Responsible for one ROMS bulk variable: 'radsw'
'''
def __init__(self, cfsr_dir, radsw_file, masks, romsgrd, time_array):
super(CfsrRadSwDown, self).__init__(cfsr_dir + radsw_file[0], radsw_file[1],'CFSR',
romsgrd, time_array, masks=masks)
class CfsrRadSwUp(CfsrDataHourly):
'''
'''
def __init__(self, cfsr_dir, radlw_file, masks, romsgrd, time_array):
super(CfsrRadSwUp, self).__init__(cfsr_dir + radlw_file[0], radlw_file[1],
'CFSR', romsgrd, time_array, masks=masks)
class CfsrRhum(CfsrDataHourly):
'''CFSR relative humidity class (inherits from CfsrData class)
Responsible for one ROMS bulk variable: 'rhum'
'''
def __init__(self, cfsr_dir, rhum_file, masks, romsgrd, time_array):
super(CfsrRhum, self).__init__(cfsr_dir + rhum_file[0], rhum_file[1], 'CFSR',
romsgrd, time_array, masks=masks)
self.print_weights()
#self.get_fillmask_cof()
class CfsrQair(CfsrDataHourly):
'''CFSR qair class (inherits from CfsrData class)
Responsible for one variable: 'qair'
'''
def __init__(self, cfsr_dir, qair_file, masks, romsgrd, time_array):
super(CfsrQair, self).__init__(cfsr_dir + qair_file[0], qair_file[1], 'CFSR',
romsgrd, time_array, masks=masks)
self.print_weights()
class CfsrSat(CfsrDataHourly):
'''CFSR surface air temperature class (inherits from CfsrData class)
Responsible for one ROMS bulk variable: 'tair'
'''
def __init__(self, cfsr_dir, sat_file, masks, romsgrd, time_array):
super(CfsrSat, self).__init__(cfsr_dir + sat_file[0], sat_file[1], 'CFSR',
romsgrd, time_array, masks=masks)
self.print_weights()
class CfsrSap(CfsrDataHourly):
'''CFSR surface air pressure class (inherits from CfsrData class)
Responsible for one variable: 'qair'
'''
def __init__(self, cfsr_dir, sap_file, masks, romsgrd, time_array):
super(CfsrSap, self).__init__(cfsr_dir + sap_file[0], sap_file[1], 'CFSR',
romsgrd, time_array, masks=masks)
self.print_weights()
class CfsrWspd(CfsrDataHourly):
'''CFSR wind speed class (inherits from CfsrData class)
Responsible for four variables: 'uspd', 'vspd', 'sustr' and 'svstr'
Requires: 'qair', sap' and 'rhum'
'''
def __init__(self, cfsr_dir, wspd_file, masks, romsgrd, time_array):
super(CfsrWspd, self).__init__(cfsr_dir + wspd_file['uspd'][0],
wspd_file['uspd'][1], 'CFSR',
romsgrd, time_array, masks=masks)
#self.print_weights()
self.uwnd_varname = wspd_file['uspd'][1]
self.vwnd_varname = wspd_file['vspd'][1]
self.wspd_datain = np.ma.empty(self.lon().shape)
self.uwnd_datain = np.ma.empty(self.lon().shape)
self.vwnd_datain = np.ma.empty(self.lon().shape)
self.ustrs_datain = np.ma.empty(self.lon().shape)
self.vstrs_datain = np.ma.empty(self.lon().shape)
self._rair_datain = np.ma.empty(self.lon().shape)
self.wspd_dataout = np.ma.empty(self.romsgrd.lon().shape)
self.uwnd_dataout = np.ma.empty(self.romsgrd.lon().shape)
self.vwnd_dataout = np.ma.empty(self.romsgrd.lon().shape)
self.ustrs_dataout = np.ma.empty(self.romsgrd.lon().shape)
self.vstrs_dataout = np.ma.empty(self.romsgrd.lon().shape)
def _get_wstrs(self, airsea, rhum_data, sat_data, sap_data, qair_data):
kelvin = self.Kelvin # convert from K to C
sat_data[:] = ne.evaluate('sat_data - kelvin')
sap_data[:] = ne.evaluate('sap_data * 0.01') # convert from Pa to mb
# Smith etal 1988
self._rair_datain[:] = airsea.air_dens(sat_data, rhum_data, sap_data, qair_data)
self.ustrs_datain[:], self.vstrs_datain[:] = airsea.stresstc(self.wspd_datain,
self.uwnd_datain,
self.vwnd_datain,
sat_data,
self._rair_datain)
def _get_wspd(self):
self.uwnd_datain[:] = self._get_cfsr_data(self.uwnd_varname)
self.vwnd_datain[:] = self._get_cfsr_data(self.vwnd_varname)
np.hypot(self.uwnd_datain, self.vwnd_datain, out=self.wspd_datain)
def get_winds(self, airsea, rhum, sat, sap, qair):
self._get_wspd()
self._get_wstrs(airsea, rhum.datain, sat.datain, sap.datain, qair.datain)
def interp2romsgrd(self):
roms_shape = self.romsgrd.lon().shape
self.datain[:] = self.wspd_datain
self._interp2romsgrd()._check_for_nans()
self.wspd_dataout[:] = self.dataout.reshape(roms_shape)
self.datain[:] = self.uwnd_datain
self._interp2romsgrd()._check_for_nans()
self.uwnd_dataout[:] = self.dataout.reshape(roms_shape)
self.datain[:] = self.vwnd_datain
self._interp2romsgrd()._check_for_nans()
self.vwnd_dataout[:] = self.dataout.reshape(roms_shape)
self.datain[:] = self.ustrs_datain
self._interp2romsgrd()._check_for_nans('Nans here could indicate')
self.ustrs_dataout[:] = self.dataout.reshape(roms_shape)
self.datain[:] = self.vstrs_datain
self._interp2romsgrd()._check_for_nans()
self.vstrs_dataout[:] = self.dataout.reshape(roms_shape)
return self
if __name__ == '__main__':
'''
make_pycfsr2frc_one-hourly
Prepare one-hourly interannual ROMS surface forcing with, e.g., CFSv2 data (ds094.0) from
http://rda.ucar.edu/pub/cfsr.html
Concatenate with, e.g.:
ncrcat rh2m.gdas.199912.grb2.nc rh2m.gdas.20????.grb2.nc tmp.nc
and compress output with:
nc3tonc4 tmp.nc REL_HUM_2000s_1hr.nc
CFSR surface data for ROMS forcing are global but subgrids can be
selected. User must supply a list of the files available, pycfsr2frc
will loop through the list, sampling and interpolating each variable.
ROMS needs the following variables:
EP : evaporation - precipitation
Net heat flux
Qnet = SW - LW - LH - SH
where SW denotes net downward shortwave radiation,
LW net downward longwave radiation,
LH latent heat flux,
and SH sensible heat flux
Note that there are dependencies for:
dQdSS <-
CFSR grids, for info see http://rda.ucar.edu/datasets/ds093.2/docs/moly_filenames.html
Horizontal resolution indicator, 4th character of filename:
h - high (0.5-degree) resolution
a - high (0.5-degree) resolution, spl type only
f - full (1.0-degree) resolution
l - low (2.5-degree) resolution
But some files labelled 'l' are in fact 0.3-degree, eg, UWND, VWND...
Notes about the data quality:
1) The 0.3deg flxf06.gdas.DSWRF.SFC.grb2.nc is ugly
Evan Mason, IMEDEA, 2014
'''
#_USER DEFINED VARIABLES_______________________________________
# True for bulk forcing file, else standard dQdSTT forcing file
bulk = True # variables ()
# CFSR information_________________________________
cfsr_version = 'ds094.1_1hourly'
#cfsr_version = 'ds093.1_1hourly'
#domain = 'S0_N50_W-50_E44'
#domain = 'S0_N60_W-50_E44'
domain = 'S27_N48_W-16_E20'
#domain = 'S21_N51_W-30_E45'
#cfsr_dir = '/shared/emason/NCEP-CFSR/%s/%s/' %(cfsr_version, domain)
#cfsr_dir = '/marula/emason/data/NCEP-CFSR/%s/%s/' %(cfsr_version, domain)
#cfsr_dir = '/shared/emason/NCEP-CFSR/%s/%s/' %(cfsr_version, domain)
#cfsr_dir = '/marula/emason/data/NCEP-CFSR/%s/%s/' %(cfsr_version, domain)
cfsr_dir = '/shared/emason/NCEP-CFSR/%s/%s/' %(cfsr_version, domain)
# Filenames and variable names of required CFSR variables
# Note that these files have been prepared by concatenating the files
# dowloaded from CFSR using ncrcat
if cfsr_version in 'ds093.1_1hourly':
SSS_file = ('SALT_1hr.nc', 'SALTY_L160_Avg_1')
swflux_file = ('EMP_6hr.nc', 'EMNP_L1_Avg_1')
prate_file = ('PRATE_1hr.nc', 'PRATE_L1_Avg_1')
shflux_SW_down_file = ('DOWN_SW_1hr.nc', 'DSWRF_L1_Avg_1')
shflux_SW_up_file = ('UP_SW_1hr.nc', 'USWRF_L1_Avg_1')
shflux_LW_down_file = ('DOWN_LW_1hr.nc', 'DLWRF_L1_Avg_1')
shflux_LW_up_file = ('UP_LW_1hr.nc', 'ULWRF_L1_Avg_1')
#shflux_LATENT_HEAT_file = ('HEAT_FLUXES/', 'LHTFL_L1_Avg_1')
#shflux_SENSIBLE_HEAT_file = ('HEAT_FLUXES/', 'SHTFL_L1_Avg_1')
sustr_file = ('WIND_1hr.nc', 'U_GRD_L103')
svstr_file = ('WIND_1hr.nc', 'V_GRD_L103')
SST_file = ('SST_1hr.nc', 'TMP_L1')
sat_file = ('SAT_1hr.nc', 'TMP_L103')
sap_file = ('PRESS_SFC_1hr.nc', 'PRES_L1')
qair_file = ('SPEC_HUM_1hr.nc', 'SPF_H_L103')
rel_hum_file = ('REL_HUM_1hr.nc', 'R_H_L103')
# Filenames of masks for the different grids
# Note: the filenames below are symbolics links to the relevant CFSv2 land cover files
if domain in 'S0_N60_W-50_E44':
mask_one = ('LANDMASK_9x15.nc', 'LAND_L1')
mask_two = ('LANDMASK_11x19.nc', 'LAND_L1')
mask_three = ('LANDMASK_43x73.nc', 'LAND_L1')
mask_four = ('LANDMASK_68x116.nc', 'LAND_L1')
elif domain in 'S21_N51_W-30_E45':
mask_one = ('LANDMASK_12x31.nc', 'LAND_L1')
mask_two = ('LANDMASK_16x41.nc', 'LAND_L1')
mask_three = ('LANDMASK_61x151.nc', 'LAND_L1')
mask_four = ('LANDMASK_96x241.nc', 'LAND_L1')
else:
raise Exception
elif cfsr_version in 'ds094.1_1hourly':
SSS_file = ('SALT_1hr.nc', 'SALTY_L160_Avg_1')
swflux_file = ('EMP_6hr.nc', 'EMNP_L1_Avg_1')
prate_file = ('PRATE_1hr.nc', 'PRATE_L1_Avg_1')
shflux_SW_down_file = ('DOWN_SW_1hr.nc', 'DSWRF_L1_Avg_1')
shflux_SW_up_file = ('UP_SW_1hr.nc', 'USWRF_L1_Avg_1')
shflux_LW_down_file = ('DOWN_LW_1hr.nc', 'DLWRF_L1_Avg_1')
shflux_LW_up_file = ('UP_LW_1hr.nc', 'ULWRF_L1_Avg_1')
#shflux_LATENT_HEAT_file = ('ds094.0_heat_fluxes.nc', 'LHTFL_L1_Avg_1')
#shflux_SENSIBLE_HEAT_file = ('ds094.0_heat_fluxes.nc', 'SHTFL_L1_Avg_1')
sustr_file = ('WIND_1hr.nc', 'U_GRD_L103')
svstr_file = ('WIND_1hr.nc', 'V_GRD_L103')
SST_file = ('SST_1hr.nc', 'TMP_L1')
sat_file = ('SAT_1hr.nc', 'TMP_L103')
sap_file = ('PRESS_SFC_1hr.nc', 'PRES_L1')
qair_file = ('SPEC_HUM_1hr.nc', 'SPF_H_L103')
rel_hum_file = ('REL_HUM_1hr.nc', 'R_H_L103')
# Filenames of masks for the different grids
# Note: the filenames below are symbolics links to the relevant CFSv2 land cover files
mask_one = ('LANDMASK_9x15.nc', 'LAND_L1')
mask_two = ('LANDMASK_22x37.nc', 'LAND_L1')
mask_three = ('LANDMASK_73x43.nc', 'LAND_L1')
mask_four = ('LANDMASK_103x179.nc', 'LAND_L1')
cfsr_masks = OrderedDict([('mask_one', mask_one),
('mask_two', mask_two),
('mask_three', mask_three),
('mask_four', mask_four)])
# ROMS configuration information_________________________________
#roms_dir = '/home/emason/runs2012_tmp/MedSea5_R2.5/'
#roms_dir = '/marula/emason/runs2012/MedSea5_intann_monthly/'
#roms_dir = '/marula/emason/runs2013/na_7pt5km_intann_5day/'
#roms_dir = '/Users/emason/toto/'
#roms_dir = '/marula/emason/runs2013/cb_3km_2013_intann/'
#roms_dir = '/marula/emason/runs2013/AlbSea_1pt25/'
#roms_dir = '/marula/emason/runs2013/cart500/'
#roms_dir = '/marula/emason/runs2012/MedSea_Romain/'
#roms_dir = '/marula/emason/runs2014/MedCan5/'
#roms_dir = '/marula/emason/runs2014/NA75_IA/'
#roms_dir = '/marula/emason/runs2014/nwmed5km/'
#roms_dir = '/marula/emason/runs2014/AlbSea175/'
roms_dir = '/marula/emason/runs2015/AlbSea500/'
#roms_dir = './'
#roms_grd = 'grd_MedSea5_R2.5.nc'
#roms_grd = 'grd_MedSea5.nc'
#roms_grd = 'roms_grd_NA2009_7pt5km.nc'
#roms_grd = 'grd_nwmed_2km.nc'
#roms_grd = 'roms_grd.nc'
#roms_grd = 'cb_2009_3km_grd_smooth.nc'
#roms_grd = 'grd_AlbSea_1pt25.nc'
#roms_grd = 'grd_cart500.nc'
#roms_grd = 'roms_grd_wmed_longterm.nc'
#roms_grd = 'grd_MedCan5.nc'
#roms_grd = 'grd_AlbSea175.nc'
#roms_grd = 'grd_nwmed5km_NARROW_STRAIT.nc'
roms_grd = 'grd_AlbSea500.nc'
# Forcing file
#frc_filename = 'frc_MedSea5_test.nc' # ini filename
#frc_filename = 'frc_MedSea5_1985010100_new.nc'
#frc_filename = 'frc_MedSea5_1985010100_64bit.nc'
#frc_filename = 'blk_MedSea5_1984123001.nc'
#frc_filename = 'blk_NA75_1984123001.nc'
#frc_filename = 'frc_CFSR_NA_7pt5km.nc'
#frc_filename = 'frc_CFSR_NA_7pt5km_UPDATE.nc'
#frc_filename = 'frc_2013_cb3km_CFSR_UPDATE.nc'
#frc_filename = 'frc_AlbSea_1pt25_CFSR_20030101.nc'
#frc_filename = 'frc_cart500.nc'
#frc_filename = 'blk_nwmed5_1992-1993.nc'
#frc_filename = 'blk_nwmed5_1994-1995.nc'
#frc_filename = 'blk_nwmed5_1996-1997.nc'
frc_filename = 'blk_AlbSea500.nc'
# Variable XXX_time/blk_time will be zero at this date
day_zero = '19850101' # string with format YYYYMMDDHH
#day_zero = '20060101' # string with format YYYYMMDDHH
#day_zero = '20140101' # string with format YYYYMMDDHH
# Modify filename
if '_1hr' not in frc_filename:
frc_filename = frc_filename.replace('.nc', '_1hr.nc')
# True if the frc file being prepared is for a downscaled simulation
#downscaled = False
#if downscaled:
# Point to parent directory, where make_pycfsr2frc_one-hourly expects to find
# start_date.mat (created by set_ROMS_interannual_start.py)
#par_dir = '/marula/emason/runs2013/na_7pt5km_intann_5day/'
#par_dir = '/marula/emason/runs2012/MedSea5_intann_monthly/'
# Start and end dates of the ROMS simulation
# must be strings, format 'YYYYMMDDHH'
#start_date = '1985010100'
#end_date = '1987102800'
#start_date = '1992043000'
#end_date = '1992060123'
#start_date = '2011010100'
#end_date = '2012113018'
#start_date = '2006032400'
#end_date = '2006060600'
#start_date = '2007022300'
#end_date = '2007022500'
#start_date = '2007062300'
#end_date = '2007100818'
#start_date = '1995111500'
#end_date = '1991111600'
#end_date = '1998021500'
start_date = '2013123000'
#end_date = '2014010100'
end_date = '2014053118'
cycle_length = np.float(0)
# Option for river runoff climatology
# Note, a precomputed *coast_distances.mat* must be available
# in roms_dir; this is computed using XXXXXX.py
add_dai_runoff = True # True or False
if add_dai_runoff:
dai_file = '/home/emason/matlab/runoff/dai_runoff_mon_-180+180.nc'
#dai_file = '/home/emason/matlab/runoff/dai_runoff_mon_0_360.nc'
dai_ind_min, dai_ind_max = 999, 999 # intentionally give unrealistic initial values
# Interpolation / processing parameters_________________________________
#balldist = 100000. # distance (m) for kde_ball (should be 2dx at least?)
# Filling of landmask options
# Set to True to extrapolate sea data over land
#winds_fillmask = False # 10 m (False recommended)
#sat_fillmask = True # 2 m (True recommended)
#rhum_fillmask = True # 2 m (True recommended)
#qair_fillmask = True # 2 m (True recommended)
#windspd_fillmask = True # surface (True recommended)
fillmask_radlw, fillmask_radlw_in = True, True
sigma_params = dict(theta_s=None, theta_b=None, hc=None, N=None)
#_END USER DEFINED VARIABLES_______________________________________
plt.close('all')
timing_warning = 'bulk_time at tind is different from roms_day'
# This dictionary of CFSR files needs to supply some or all of the surface
# forcing variables variables:
if bulk:
cfsr_files = OrderedDict([
('prate', prate_file),
('radlw', OrderedDict([
('shflux_LW_down', shflux_LW_down_file),
('shflux_LW_up', shflux_LW_up_file)])),
('radsw', OrderedDict([
('shflux_SW_down', shflux_SW_down_file),
('shflux_SW_up', shflux_SW_up_file)])),
('wspd', OrderedDict([
('sat', sat_file),
('SST', SST_file),
('rel_hum', rel_hum_file),
('sap', sap_file),
('qair', qair_file),
('uspd', sustr_file),
('vspd', svstr_file)]))])
else:
cfsr_files = OrderedDict([
('SSS', SSS_file),
('swflux', swflux_file),
('shflux', OrderedDict([
('shflux_SW_down', shflux_SW_down_file),
('shflux_SW_up', shflux_SW_up_file),
('shflux_LW_down', shflux_LW_down_file),
('shflux_LW_up', shflux_LW_up_file),
('shflux_LH', shflux_LATENT_HEAT_file),
('shflux_SH', shflux_SENSIBLE_HEAT_file)])),
('dQdSST', OrderedDict([
('uspd', sustr_file),
('vspd', svstr_file),
('SST', SST_file),
('sat', sat_file),
('rel_hum', rel_hum_file),
('sap', sap_file),
('qair', qair_file)]))])
# Initialise an AirSea object
airsea = AirSea()
dtstrdt = dt.datetime.datetime(np.int(start_date[:4]),
np.int(start_date[4:6]),
np.int(start_date[6:8]),
np.int(start_date[8:10]), 30)
dtenddt = dt.datetime.datetime(np.int(end_date[:4]),
np.int(end_date[4:6]),
np.int(end_date[6:8]),
np.int(end_date[8:10]), 30)
# Number of records at six hourly frequency
delta = dt.datetime.timedelta(hours=1)
#numrec = dt.num2date(dt.drange(dtstrdt, dtenddt, delta))
dtstr, dtend = dt.date2num(dtstrdt), dt.date2num(dtenddt)
day_zero = dt.datetime.datetime(int(day_zero[:4]), int(day_zero[4:6]), int(day_zero[6:]))
day_zero = dt.date2num(day_zero)
time_array = dt.num2date(dt.drange(dtstrdt, dtenddt, delta))
#if downscaled:
#inidate = io.loadmat(par_dir + 'start_date.mat')
#deltaday0 = dtstr - inidate['start_date']
# Instantiate a RomsGrid object
numrec = None
romsgrd = RomsGrid(''.join((roms_dir, roms_grd)), sigma_params, 'ROMS')
romsgrd.create_frc_nc(''.join((roms_dir, frc_filename)), start_date, end_date, numrec,
cycle_length, 'make_pycfsr2frc_one-hourly', bulk)
romsgrd.make_gnom_transform().proj2gnom(ignore_land_points=True).make_kdetree()
# Get all CFSR mask and grid sizes
# Final argument is kde ball distance in metres (should be +/- 2.5 * dx)
# but by calling mask_1.check_ball() to make a figure...
if cfsr_version in 'ds093.1_1hourly':
mask_1 = CfsrMask(cfsr_dir, cfsr_masks['mask_one'], romsgrd, 650000)
mask_2 = CfsrMask(cfsr_dir, cfsr_masks['mask_two'], romsgrd, 110000)
mask_3 = CfsrMask(cfsr_dir, cfsr_masks['mask_three'], romsgrd, 125000)
mask_4 = CfsrMask(cfsr_dir, cfsr_masks['mask_four'], romsgrd, 120000)