parser.add_argument('-n',
'--nprocs',
help='Number of processes',
type=int,
default=1)
parser.add_argument('-c',
'--nocompression',
help='do not compress the netcdf file',
action='store_true')
args = parser.parse_args()
modes = args.modes
indexes = args.index
nl = mht.Read_namelist(args.filename)
itot = nl['grid']['itot']
jtot = nl['grid']['jtot']
ktot = nl['grid']['ktot']
starttime = float(
args.starttime) if args.starttime is not None else nl['time']['starttime']
endtime = float(
args.endtime) if args.endtime is not None else nl['time']['endtime']
sampletime = float(
args.sampletime
) if args.sampletime is not None else nl['cross']['sampletime']
if args.modes is None:
modes = list(nl['cross'].keys() & cross_modes)
else:
modes = args.modes
}
# MicroHH data type {f4, f8}
float_type = 'f8'
header('Creating LES input')
# Download the ERA5 data (or check whether it is available local)
download_ERA5(settings)
# Read ERA5 data, and calculate LES forcings, using +/-n_av grid point averages in ERA5
e5 = Read_ERA(settings)
e5.calculate_forcings(n_av=1)
# Read MicroHH namelist and create stretched vertical grid
nl = mht.Read_namelist()
grid = mht.Stretched_grid(kmax=128, nloc1=90, nbuf1=20, dz1=20, dz2=50)
grid.plot()
# Create nudge factor, controlling where nudging is aplied, and time scale
tau_nudge = 10800 # Nudge time scale (s)
nudge_fac = np.ones(grid.z.size) / tau_nudge
# Interpolate ERA5 onto LES grid
def interp_time(z, ze, arr):
out = np.empty((arr.shape[0], z.size))
for i in range(arr.shape[0]):
out[i, :] = np.interp(z, ze[i, :], arr[i, :])
return out