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pycfsr2frc.py.orig
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pycfsr2frc.py.orig
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# -*- coding: utf-8 -*-
# %run pycfsr2frc.py
'''
Create a forcing file based on CFSR data
E. Mason IMEDEA 2012
'''
import netCDF4 as netcdf
import pylab as plt
import numpy as np
from scipy import io
import scipy.interpolate as si
import scipy.ndimage as nd
import scipy.spatial as sp
import matplotlib.nxutils as nx
import time
import scipy.interpolate.interpnd as interpnd
#import collections
from mpl_toolkits.basemap import Basemap
from collections import OrderedDict
from datetime import datetime
import calendar as ca
from pyroms2roms import horizInterp
from pyroms2roms import ROMS, debug0, debug1
from pyroms2roms import RomsGrid, RomsData
class RomsGrid(RomsGrid):
'''
Modify the RomsGrid class
'''
def create_frc_nc(self, frcfile, sd, ed, nr, cl, madeby):
'''
Create a new forcing file based on dimensions from grdobj
frcfile : path and name of new frc file
sd : start date2num
ed : end date
nr : no. of records
cl : cycle length
madeby : name of this file
'''
self.frcfile = frcfile
print 'Creating new CFSR forcing file', frcfile
# Global attributes
''' The best choice should be format='NETCDF4', but it will not work with
Sasha's 2008 code (not tested with Roms-Agrif). Therefore I use
format='NETCDF3_64BIT; the drawback is that it is very slow'
'''
#nc = netcdf.Dataset(frcfile, 'w', format='NETCDF3_64BIT')
nc = netcdf.Dataset(frcfile, 'w', format='NETCDF3_CLASSIC')
#nc = netcdf.Dataset(frcfile, 'w', format='NETCDF4')
nc.created = datetime.now().isoformat()
nc.type = 'ROMS interannual forcing file produced by %s.py' %madeby
nc.grd_file = self.romsfile
nc.start_date = sd
nc.end_date = ed
# Dimensions
nc.createDimension('xi_rho', self.lon().shape[1])
nc.createDimension('xi_u', self.lon().shape[1] - 1)
nc.createDimension('eta_rho', self.lon().shape[0])
nc.createDimension('eta_v', self.lon().shape[0] - 1)
nc.createDimension('sms_time', nr)
nc.createDimension('shf_time', nr)
nc.createDimension('swf_time', nr)
nc.createDimension('sst_time', nr)
nc.createDimension('srf_time', nr)
nc.createDimension('sss_time', nr)
nc.createDimension('one', 1)
# Dictionary for the variables
frc_vars = OrderedDict()
frc_vars['sms_time'] = ['time',
'sms_time',
'surface momentum stress time',
'days']
frc_vars['shf_time'] = ['time',
'shf_time',
'surface heat flux time',
'days']
frc_vars['swf_time'] = ['time',
'swf_time',
'surface freshwater flux time',
'days']
frc_vars['sst_time'] = ['time',
'sst_time',
'sea surface temperature time',
'days']
frc_vars['sss_time'] = ['time',
'sss_time',
'sea surface salinity time',
'days']
frc_vars['srf_time'] = ['time',
'srf_time',
'solar shortwave radiation time',
'days']
# Note this could be problematic if scale_cfsr_coads.py adjusts variables
# with different frequencies...
frc_vars['month'] = ['time',
'sst_time',
'not used by ROMS; useful for scale_cfsr_coads.py',
'month of year']
frc_vars['sustr'] = ['u',
'sms_time',
'surface u-momentum stress',
'Newton meter-2']
frc_vars['svstr'] = ['v',
'sms_time',
'surface v-momentum stress',
'Newton meter-2']
frc_vars['shflux'] = ['rho',
'shf_time',
'surface net heat flux',
'Watts meter-2']
frc_vars['swflux'] = ['rho',
'swf_time',
'surface freshwater flux (E-P)',
'centimeters day-1',
'net evaporation',
'net precipitation']
frc_vars['SST'] = ['rho',
'sst_time',
'sea surface temperature',
'Celsius']
frc_vars['SSS'] = ['rho',
'sss_time',
'sea surface salinity',
'PSU']
frc_vars['dQdSST'] = ['rho',
'sst_time',
'surface net heat flux sensitivity to SST',
'Watts meter-2 Celsius-1']
frc_vars['swrad'] = ['rho',
'srf_time',
'solar shortwave radiation',
'Watts meter-2',
'downward flux, heating',
'upward flux, cooling']
for key, value in zip(frc_vars.keys(), frc_vars.values()):
#print key, value
if 'time' in value[0]:
dims = (value[1])
elif 'rho' in value[0]:
dims = (value[1], 'eta_rho', 'xi_rho')
elif 'u' in value[0]:
dims = (value[1], 'eta_rho', 'xi_u')
elif 'v' in value[0]:
dims = (value[1], 'eta_v', 'xi_rho')
else:
error
#print 'key dims',key, dims
nc.createVariable(key, 'f8', dims)
nc.variables[key].long_name = value[2]
nc.variables[key].units = value[3]
if 'time' in key:
nc.variables[key].cycle_length = cl
if 'swrad' in key:
nc.variables[key].positive = value[4]
nc.variables[key].negative = value[5]
if 'swflux' in key:
nc.variables[key].positive = value[4]
nc.variables[key].negative = value[5]
nc.close()
def gc_dist(self, lon1, lat1, lon2, lat2):
'''
Use Haversine formula to calculate distance
between one point and another
'''
r_earth = 6371315. # Mean earth radius in metres (from scalars.h)
dlat = lat2 - lat1
dlon = lon2 - lon1
dang = 2. * np.arcsin(np.sqrt(np.power(np.sin(0.5 * dlat),2) + \
np.cos(lat2) * np.cos(lat1) * np.power(np.sin(0.5 * dlon),2)))
return r_earth * dang # distance
def get_runoff(self, swflux_data, dai_file, mon):
'''
'''
mon -= 1
area = 1. / (self.pm() * self.pn())
dx = 1. / np.mean(self.pm())
cdist = io.loadmat(self.roms_dir + 'coast_distances.mat')
mask = self.mask()
# Exponential decay, runoff forced towards coast. 75km ?
mult = np.exp(-cdist['cdist'] / 150e4)
np.place(mult, mask > 1., 0)
# Read in river data and set data trim
lon0 = np.round(self.lon().min() - 1.0)
lon1 = np.round(self.lon().max() + 1.0)
lat0 = np.round(self.lat().min() - 1.0)
lat1 = np.round(self.lat().max() + 1.0)
nc = netcdf.Dataset(dai_file)
lon_runoff = nc.variables['lon'][:]
lat_runoff = nc.variables['lat'][:]
i0 = np.nonzero(lon_runoff > lon0)[0].min()
i1 = np.nonzero(lon_runoff < lon1)[0].max() + 1
j0 = np.nonzero(lat_runoff > lat0)[0].min()
j1 = np.nonzero(lat_runoff < lat1)[0].max() + 1
flux_unit = 1.1574e-07 # (cm/day)
surf_ro = np.zeros(self.lon().shape)
lon_runoff = lon_runoff[i0:i1]
lat_runoff = lat_runoff[j0:j1]
runoff = nc.variables['runoff'][mon, j0:j1, i0:i1]
nc.close()
lon_runoff, lat_runoff = np.meshgrid(lon_runoff, lat_runoff)
lon_runoff = lon_runoff[runoff >= 1e-5]
lat_runoff = lat_runoff[runoff >= 1e-5]
runoff = runoff[runoff >= 1e-5]
n_runoff = runoff.size
for idx in np.arange(n_runoff):
#print idx, n_runoff
# Find suitable unmasked grid points to distribute run-off
dist = self.gc_dist(np.deg2rad(self.lon()),
np.deg2rad(self.lat()),
np.deg2rad(lon_runoff[idx]),
np.deg2rad(lat_runoff[idx]))
if dist.min() <= 5. * dx:
scale = 150. * 1e3 # scale of Dai data is at 1 degree
bool_mask = np.logical_and(dist < scale, mask > 0)
int_are = np.sum(area[bool_mask])
ave_wgt = np.mean(mult[bool_mask])
surface_flux = 1e6 * runoff[idx] / int_are
surf_ro[bool_mask] = surf_ro[bool_mask] + surface_flux / \
flux_unit * mult[bool_mask] / ave_wgt
return (swflux_data * mask) - (surf_ro * mask)
class CfsrGrid(RomsGrid):
'''
CFSR grid class (inherits from RomsGrid class)
'''
def _lonlat(self):
lon = self.read_nc('lon', indices='[:]')
lat = self.read_nc('lat', indices='[:]')
return np.meshgrid(lon, lat[::-1])
def lon(self):
return self._lonlat()[0]
def lat(self):
return self._lonlat()[1]
def metrics(self):
'''
Return array of metrics unique to this grid
(lonmin, lonmax, lon_res, latmin, latmax, lat_res)
where 'res' is resolution in degrees
'''
met = np.array([])
lon_range = self.read_nc_att('lon', 'valid_range')
lat_range = self.read_nc_att('lat', 'valid_range')
lon_res = np.diff(lon_range) / self.lon().shape[1]
lat_res = np.diff(lat_range) / self.lat().shape[0]
met = np.concatenate((lon_range, lon_res, lat_range, lat_res))
return met
def select_mask(self, grids, masks):
'''
'''
for gind, grid in enumerate(grids):
if np.alltrue(grid.metrics() == self.metrics()):
mask = masks[gind]
try:
return mask.get_mask()
except Exception, err:
print 'Unspecified grid in method "select_mask"'
to_do__make_routine_to_add_new_grid_on_the_fly
def resolution(self):
'''
Return the resolution of the data in degrees
'''
return self.metrics()[-1]
def get_points(self, roms_M):
'''
get points
'''
return self.proj2gnom(roms_M)
def get_points_kdetree(self, roms_M):
'''
Check that no ROMS data points lie outside of the
CFSR domain. If this occurs pycfsr2frc cannot function;
the solution is to obtain a new CFSR file making sure it
covers the child grid...
'''
cfsr_points_all = self.get_points(roms_M) # must use roms_M
cfsr_tri = sp.Delaunay(cfsr_points_all) # triangulate full parent
tn = cfsr_tri.find_simplex(roms_points)
assert not np.any(tn == -1), 'ROMS data points outside CFSR domain detected'
# create cfsr grid KDE tree...
cfsr_tree = sp.KDTree(cfsr_points_all)
# ... in order to get minimum no. indices for interpolation.
return cfsr_tree, cfsr_points_all
def assert_resolution(self, cfsrgrd_tmp, key):
'''
'''
assert self.metrics()[2] <= cfsrgrd_tmp.metrics()[2], \
'Resolution of %s is lower than previous grids, move up in dict "cfsr_files"' %key
class CfsrData(RomsData):
'''
CFSR data class (inherits from RomsData class)
'''
def frc_emp(self, ind):
'''
evaporation minus precipitation in cm/day
'''
return self.read_nc('EMNP_L1', '[' + str(ind) + ']')[::-1]
def frc_sat(self, ind):
'''
air temperature (K)
'''
try:
return self.read_nc('TMP_L103_Avg', '[' + str(ind) + ']')[::-1]
except Exception:
return self.read_nc('TMP_L103', '[' + str(ind) + ']')[::-1]
def frc_sap(self, ind):
'''
surface air pressure (Pa)
'''
try:
return self.read_nc('PRES_L1', '[' + str(ind) + ']')[::-1]
except Exception:
return self.read_nc('PRES_L1_Avg', '[' + str(ind) + ']')[::-1]
def frc_sss(self, ind):
'''
sea surface salinity (kg kg-1)
'''
return self.read_nc('SALTY_L160', '[' + str(ind) + ']')[::-1]
def frc_sst(self, ind):
'''
sea surface temperature (K)
'''
return self.read_nc('TMP_L1', '[' + str(ind) + ']')[::-1]
def frc_uspd(self, ind):
'''
u 10-m wind speed (m s-1)
'''
return self.read_nc('U_GRD_L103_Avg', '[' + str(ind) + ']')[::-1]
def frc_vspd(self, ind):
'''
v 10-m wind speed (m s-1)
'''
return self.read_nc('V_GRD_L103_Avg', '[' + str(ind) + ']')[::-1]
def frc_shf(self, ind):
'''
net surface heat flux (W m-2)
'''
return self.read_nc('LHTFL_L1', '[' + str(ind) + ']')[::-1]
def frc_shflux_SW_up(self, ind):
'''
upward shortwave radiation flux (W m-2)
'''
return self.read_nc('USWRF_L1', '[' + str(ind) + ']')[::-1]
def frc_shflux_SW_down(self, ind):
'''
downward shortwave radiation flux (W m-2)
'''
return self.read_nc('DSWRF_L1', '[' + str(ind) + ']')[::-1]
def frc_shflux_LW_up(self, ind):
'''
upward longwave radiation flux (W m-2)
'''
return self.read_nc('ULWRF_L1', '[' + str(ind) + ']')[::-1]
def frc_shflux_LW_down(self, ind):
'''
downward longwave radiation flux (W m-2)
'''
return self.read_nc('DLWRF_L1', '[' + str(ind) + ']')[::-1]
def frc_shflux_LH(self, ind):
'''
latent heat (W m-2)
'''
return self.read_nc('LHTFL_L1', '[' + str(ind) + ']')[::-1]
def frc_shflux_SH(self, ind):
'''
sensible heat (W m-2)
'''
return self.read_nc('SHTFL_L1', '[' + str(ind) + ']')[::-1]
def frc_qair(self, ind):
'''
sea level specific humidity (kg kg-1)
'''
return self.read_nc('SPF_H_L103', '[' + str(ind) + ']')[::-1]
def frc_rel_hum(self, ind):
'''
relative humidity (%)
'''
return self.read_nc('R_H_L103', '[' + str(ind) + ']')[::-1]
def vd_time(self):
'''
valid date and time as YYYYMMDDHH
'''
return self.read_nc('valid_date_time')
def time(self):
'''
Hours since 1979-01-01 00:00:00.0 +0:00
'''
return self.read_nc('time')
def get_mask(self):
'''
Return a land sea mask
'''
try:
result = self.read_nc('LAND_L1', '[0,::-1]')
return np.abs(result - 1.)
except Exception:
try:
result = self.read_nc('LAND_L1_Avg', '[0,::-1]')
return np.abs(result - 1.)
except Exception:
try:
result = self.read_nc('SALTY_L160', '[0,::-1]')
return np.abs(result.mask - 1.)
except Exception, err:
print 'undefined grid'
def dQdSST(self, sst, sat, rho_air, U, qsea):
'''
Compute the kinematic surface net heat flux sensitivity to the
the sea surface temperature: dQdSST.
Q_model ~ Q + dQdSST * (T_model - SST)
dQdSST = - 4 * eps * stef * T^3 - rho_air * Cp * CH * U
- rho_air * CE * L * U * 2353 * ln (10 * q_s / T^2)
Input parameters:
sst : sea surface temperature (Celsius)
sat : sea surface atmospheric temperature (Celsius)
rho_air : atmospheric density (kilogram meter-3)
U : wind speed (meter s-1)
qsea : sea level specific humidity (kg/kg)
Output:
dqdsst : kinematic surface net heat flux sensitivity to the
the sea surface temperature (Watts meter-2 Celsius-1)
From Roms_tools of Penven etal
'''
# Specific heat of atmosphere.
Cp = 1004.8
# Sensible heat transfer coefficient (stable condition)
Ch = 0.66e-3
# Latent heat transfer coefficient (stable condition)
Ce = 1.15e-3
# Emissivity coefficient
eps = 0.98
# Stefan constant
stef = 5.6697e-8
# SST (Kelvin)
SST = sst + 273.15
# Latent heat of vaporisation (J.kg-1)
L = 2.5008e6 - 2.3e3 * sat
# Infrared contribution
q1 = -4. * stef * np.power(SST, 3)
# Sensible heat contribution
q2 = -rho_air * Cp * Ch * U
# Latent heat contribution
dqsdt = 2353. * np.log(10.) * qsea / np.power(SST, 2)
q3 = -rho_air * Ce * L * U * dqsdt
dQdSST = q1 + q2 + q3
return dQdSST
"""
def get_W3(self, roms_tools_W3, uspd_pts, uspd_mask):
'''
Get monthly point means of Roms_tools W3 over the CFSR domain
'''
"""
class AirSea(object):
'''
'''
def __init__(self):
'''
Constants from:
http://woodshole.er.usgs.gov/operations/sea-mat/air_sea-html/as_consts.html
'''
# ------- physical constants
self.g = 9.8 # acceleration due to gravity [m/s^2]
self.eps_air = 0.62197 # molecular weight ratio (water/air)
self.CtoK = 273.15 # conversion factor for [C] to [K]
self.gas_const_R = 287.04 # gas constant for dry air [J/kg/K]
# ------- meteorological constants
self.z = 10. # default measurement height [m]
self.kappa = 0.4 # von Karman's constant
self.Charnock_alpha = 0.011 # Charnock constant (for determining roughness length
''' at sea given friction velocity), used in Smith
formulas for drag coefficient and also in Fairall
and Edson. use alpha=0.011 for open-ocean and
alpha=0.018 for fetch-limited (coastal) regions.'''
self.R_roughness = 0.11 # limiting roughness Reynolds # for aerodynamically
''' smooth flow'''
# ------ defaults suitable for boundary-layer studies
self.cp = 1004.7 # heat capacity of air [J/kg/K]
self.rho_air = 1.22 # air density (when required as constant) [kg/m^2]
self.Ta = 10. # default air temperature [C]
self.Pa = 1020. # default air pressure for Kinneret [mbars]
self.psych_default = 'screen' # default psychmometer type (see relhumid.m)
# satur. specific humidity coefficient reduced by 2% over salt water
self.Qsat_coeff = 0.98
def viscair(self):
'''
vis=VISCAIR(Ta) computes the kinematic viscosity of dry air as a
function of air temperature following Andreas (1989), CRREL Report
89-11.
INPUT: Ta - air temperature [C]
OUTPUT: vis - air viscosity [m^2/s]
'''
return 1.326e-5 * (1 + 6.542e-3 * self.Ta + 8.301e-6 * np.power(self.Ta, 2) -
4.84e-9 * np.power(self.Ta, 3))
def cdntc(self, sp):
'''
cd, u10 = cdntc(sp, z ,Ta) computes the neutral drag coefficient and
wind speed at 10m given the wind speed and air temperature at height z
following Smith (1988), J. Geophys. Res., 93, 311-326.
INPUT: sp - wind speed [m/s]
self.z - measurement height [m]
self.Ta - air temperature (optional) [C]
OUTPUT: cd - neutral drag coefficient at 10m
u10 - wind speed at 10m [m/s]
'''
sp = np.asarray(sp)
# iteration endpoint
tol = np.asarray(.00001)
visc = self.viscair()
# remove any sp==0 to prevent division by zero
np.place(sp, sp == 0, 0.1)
# initial guess
ustaro = np.zeros(sp.shape)
ustarn = .036 * sp
# iterate to find z0 and ustar
ii = np.abs(ustarn - ustaro) > tol
while np.any(ii):
ustaro = ustarn
z0 = self.Charnock_alpha * np.power(ustaro, 2) / self.g + \
self.R_roughness * visc / ustaro
ustarn = sp * (self.kappa / np.log(self.z / z0))
ii = np.abs(ustarn - ustaro) > tol
sqrcd = self.kappa / np.log((10.) / z0)
cd = np.power(sqrcd, 2)
u10 = ustarn / sqrcd
return cd, u10
def stresstc(self, u, v, Ta=None, rho_air=None):
'''
taux, tauy = stresstc(u, v) computes the neutral wind stress given the
wind speed and air temperature at height z following Smith (1988),
J. Geophys. Res., 93, 311-326. Air temperature and density are optional
inputs.
INPUT: u, v - wind speed components [m/s]
z - measurement height [m]
Ta - air temperature (optional) [C]
rho_air - air density (optional) [kg/m^3]
OUTPUT: taux, tauy - wind stress components [N/m^2]
Adapted from http://woodshole.er.usgs.gov/operations/sea-mat/air_sea-html/as_consts.html
'''
sp = np.hypot(u, v)
if Ta is not None:
self.Ta = Ta
cd, u10 = self.cdntc(sp)
if rho_air is None:
taux = self.rho_air * cd * u * u10
tauy = self.rho_air * cd * v * u10
else:
taux = rho_air * cd * u * u10
tauy = rho_air * cd * v * u10
return taux, tauy
def air_dens(self, Ta, RH, Pa=None, Q=None):
'''
rhoa=AIR_DENS(Ta,RH,Pa) computes the density of moist air.
Air pressure is optional.
INPUT: Ta - air temperature Ta [C]
RH - relative humidity [%]
Pa - air pressure (optional) [mb]
OUTPUT: rhoa - air density [kg/m^3]
'''
if Pa is not None:
self.Pa = Pa # pressure in mb
if Q is None:
Q = (0.01 * RH) * self.qsat(Ta, Pa) # specific humidity of air [kg/kg]
o61 = 1. / self.eps_air - 1. # 0.61 (moisture correction for temp.)
T = Ta + self.CtoK # convert to K
Tv = T * (1. + o61 * Q) # air virtual temperature
rhoa = (100. * self.Pa) / (self.gas_const_R * Tv) # air density [kg/m^3]
return rhoa
def qsat(self, Ta, Pa=None):
'''
QSAT: computes specific humidity at saturation.
q=QSAT(Ta) computes the specific humidity (kg/kg) at saturation at
air temperature Ta (deg C). Dependence on air pressure, Pa, is small,
but is included as an optional input.
INPUT: Ta - air temperature [C]
Pa - (optional) pressure [mb]
OUTPUT: q - saturation specific humidity [kg/kg]
Version 1.0 used Tetens' formula for saturation vapor pressure
from Buck (1981), J. App. Meteor., 1527-1532. This version
follows the saturation specific humidity computation in the COARE
Fortran code v2.5b. This results in an increase of ~5% in
latent heat flux compared to the calculation with version 1.0.
'''
if Pa is not None:
self.Pa = Pa # pressure in mb
# as in Fortran code v2.5b for COARE
ew = 6.1121 * (1.0007 + 3.46e-6 * self.Pa) * np.exp((17.502 * Ta) / \
(240.97 + Ta)) # in mb
q = 0.62197 * (ew / (self.Pa - 0.378 * ew)) # mb -> kg/kg
return q
def delq(self, Ts, Ta, rh, Pa=None):
'''
dq=DELQ(Ts,Ta,rh) computes the specific humidity (kg/kg) difference
between the air (as determined by relative humidty rh and air
temperature Ta measurements) and the sea surface (where q is
assumed to be at 98% saturation at the sea surface temperature Ts).
DELQ uses QSAT based on Tetens' formula for saturation vapor
pressure from Buck (1981), J. App. Meteor., 1527-1532. The
dependence of QSAT on pressure is small (<0.5%) and has been
removed using a mean pressure of 1020 mb.
INPUT: Ts - sea surface temperature [C]
Ta - air temperature [C]
rh - relative humidity [%]
Pa - (optional) pressure [mb]
OUTPUT: dq - air-sea specific humidity difference [kg/kg]
'''
if Pa is not None:
self.Pa = Pa # pressure in mb
dq = 0.01 * rh * self.qsat(Ta, self.Pa) - \
self.Qsat_coeff * self.qsat(Ts, self.Pa)
return dq
if __name__ == '__main__':
'''
pycfsr2frc
Prepare interannual ROMS surface forcing with CFSR data from
http://rda.ucar.edu/pub/cfsr.html
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, 2012
'''
#_USER DEFINED VARIABLES_______________________________________
# CFSR information_________________________________
#cfsr_dir = '/Users/emason/toto/'
#cfsr_dir = '/shared/emason/NCEP-CFSR/'
cfsr_dir = '/shared/emason/NCEP-CFSR/S-14_N56__W-63_E12/'
# Filenames of needed CFSR variables
SSS = 'ocnh01.gdas.SALTY.5m.grb2.nc'
swflux = 'ocnh01.gdas.EMNP.SFC.grb2.nc'
shflux_SW_down = 'flxl01.gdas.DSWRF.SFC.grb2.nc'
shflux_SW_up = 'flxl01.gdas.USWRF.SFC.grb2.nc'
shflux_LW_down = 'flxl01.gdas.DLWRF.SFC.grb2.nc'
shflux_LW_up = 'flxl01.gdas.ULWRF.SFC.grb2.nc'
shflux_LH = 'flxl01.gdas.LHTFL.SFC.grb2.nc'
shflux_SH = 'flxl01.gdas.SHTFL.SFC.grb2.nc'
#sustr = 'flxf01.gdas.UWND.10m.grb2.nc' # use for MedSea
#svstr = 'flxf01.gdas.VWND.10m.grb2.nc' # use for MedSea
sustr = 'flxf01.gdas.WND.10m.grb2.nc' # use for NEA
svstr = 'flxf01.gdas.WND.10m.grb2.nc' # use for NEA
#SST = 'pgbh01.gdas.TMP.SFC.grb2.nc' # use for MedSea
SST = 'ocnh01.gdas.TMP.SFC.grb2.nc' # use for NEA
# Surface air temperature
# file 'flxf01.gdas.TMP.2m.grb2.nc' compares well with Roms_tools sat.cdf
#sat = 'flxf01.gdas.TMP.2m.grb2.nc' # use for MedSea
sat = 'pgbh01.gdas.TMP.2m.grb2.nc' # use for NEA
# Relative humidity
# file 'pgbh01.gdas.R_H.2m.grb2.nc' compares well with Roms_tools rh.cdf
rel_hum = 'pgbh01.gdas.R_H.2m.grb2.nc'
#sap = 'pgbhnl.gdas.PRES.SFC.grb2.nc' # use for MedSea
sap = 'flxf01.gdas.PRES.SFC.grb2.nc' # use for NEA
qair = 'flxf01.gdas.SPF_H.2m.grb2.nc'
# Filenames of masks for the 1.0, 0.5 and 0.3 degree grids
mask10 = 'pgbl01.gdas.LAND.SFC.grb2.nc'
mask05 = 'pgbh01.gdas.LAND.SFC.grb2.nc'
mask03 = 'flxf01.gdas.LAND.SFC.grb2.nc'
mask1_8 = 'flxl01.gdas.LAND.SFC.grb2.nc'
maskocn = 'ocnh01.gdas.SALTY.5m.grb2.nc'
# ROMS configuration information_________________________________
#roms_dir = '/marula/emason/runs2012/MedSea15/'
#roms_dir = '/shared/emason/marula/emason/runs2012/MedSea5/'
#roms_dir = '/home/emason/runs2012_tmp/MedSea5_R2.5/'
#roms_dir = '/marula/emason/runs2013/na_7pt5km_intann_5day/'
#roms_dir = '/Users/emason/toto/'
roms_dir = '/marula/emason/runs2013/cb_3km_2013_intann/'
#roms_grd = 'grd_MedSea5_R2.5.nc'
#roms_grd = 'roms_grd_NA2009_7pt5km.nc'
roms_grd = 'cb_2009_3km_grd_smooth.nc'
# Forcing file
#frc_filename = 'frc_intann_MedSea5.nc' # ini filename
#frc_filename = 'frc_CFSR_NA_7pt5km.nc'
frc_filename = 'frc_2013_cb3km_CFSR.nc'
# True if the frc file being prepared is for a downscaled simulation
downscaled = True
if downscaled:
# Point to parent directory, where pyccmp2frc expects to find
# start_date.mat (created by set_ROMS_interannual_start.py)
par_dir = '/marula/emason/runs2013/na_7pt5km_intann_5day/'
# Start and end dates of the ROMS simulation
# must be strings, format 'YYYYMMDDHH'
#start_date = '1985010100'
#end_date = '2010010100'
start_date = '1997010100'
end_date = '2000123100'
# Flag to 360-day years
make360 = False
cycle_length = 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 of 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'
# interpolation / processing parameters_________________________________
balldist = 250000. # distance (m) for kde_ball (should be 2dx at least?)
# Filling of landmask options
# Set to True to extrapolate sea data over land
wind_fill_mask = False # 10 m
sat_fill_mask = True # 2 m
rel_hum_fill_mask = True # 2 m
qair_fill_mask = True # 2 m
windspd_fill_mask = True # surface
"""
# Use climatological scalar wind from Roms_tools for dQdSST calculation
use_roms_tools_W3 = True
if use_roms_tools_W3:
roms_tools_W3 = '/home/emason/roms/Roms_tools/COADS05/w3.cdf'
"""
#_END USER DEFINED VARIABLES_______________________________________
plt.close('all')
# This dictionary of CFSR files needs to supply some or all of the surface
# forcing variables variables:
cfsr_files = OrderedDict([
('SSS', SSS),
('swflux', swflux),
('shflux', OrderedDict([
('shflux_SW_down', shflux_SW_down),
('shflux_SW_up', shflux_SW_up),
('shflux_LW_down', shflux_LW_down),
('shflux_LW_up', shflux_LW_up),
('shflux_LH', shflux_LH),
('shflux_SH', shflux_SH)])),
('dQdSST', OrderedDict([
('sustr', sustr),
('svstr', svstr),
('SST', SST),
('sat', sat),
('rel_hum', rel_hum),
('sap', sap),
('qair', qair)]))])
'''cfsr_files = OrderedDict([
('SSS', SSS),
('dQdSST', OrderedDict([
('sustr', sustr),
('svstr', svstr),
('SST', SST),
('sat', sat),
('rel_hum', rel_hum),
('sap', sap),
('qair', qair)]))])'''
# masks for the 1.0, 0.5 and 0.3 degree grids
cfsr_masks = OrderedDict([
('mask10', mask10),
('mask05', mask05),
('mask03', mask03),