def load_std(E,std_mode = 'NODA',hostname='taurus',verbose=False):
"""
This subroutine returns the standard deviation of whatever variable is given in E['variable'],
for each time given in E['daterange'].
There are several ways to compute the standard deviation, and that's determined by the input
'std_mode':
std_mode='ensemble' simply computes the standard deviation of the DART ensemble
at each time
if you set std_mode to any other string, it looks up the multi-year experiment corresponding
to that string using the subroutine 'std_runs' in the user
module experiment_settings.
In this case, the standard deviation
is computed for each time over several years, rather than an ensemble
"""
if std_mode == 'ensemble' :
# cycle over the dates in the experiment dictionary
# and load the ensemble mean of the corresponding No-assimilation case
# TODO: a subroutine that returns the corresponding NODA experiment for each case
Xlist = []
ECLIM = E.copy()
ECLIM['copystring'] = 'ensemble std'
Xclim,lat,lon,lev,DRnew = DSS.DART_diagn_to_array(ECLIM,hostname=hostname,debug=verbose)
if len(DRnew) != len(ECLIM['daterange']):
print('NOTE: not all requested data were found; returning a revised datarange')
if Xclim is None:
print('Cannot find data for experiment '+E['exp_name'])
return None, None, None, None
if std_mode == 'F_W4_L66' :
# find the corresponding dataset
ff = es.std_runs(std_mode,hostname=hostname,debug=verbose)
# load the desired variables
from netCDF4 import Dataset
f = Dataset(ff,'r')
lat = f.variables['lat'][:]
lon = f.variables['lon'][:]
lev = f.variables['lev'][:]
time = f.variables['time'][:]
variable = E['variable']
if E['variable'] == 'US':
variable = 'U'
if E['variable'] == 'VS':
variable = 'V'
if E['variable'] == 'OLR':
variable = 'FLUT'
VV = f.variables[variable][:]
f.close()
# choose the times corresponding to the daterange in E
d0 = E['daterange'][0].timetuple().tm_yday # day in the year where we start
nT = len(E['daterange'])
df = E['daterange'][nT-1].timetuple().tm_yday # day in the year where we start
# if df<d0, we have to cycle back to the beginning of the year
if df < d0:
day_indices = list(range(d0-1,365))+list(range(0,df))
else:
day_indices = list(range(d0-1,df))
# also choose the lat, lon, and level ranges corresponding to those in E
if E['levrange'] is not None:
if E['levrange'][0] == E['levrange'][1]:
ll = E['levrange'][0]
idx = (np.abs(lev-ll)).argmin()
lev2 = lev[idx]
k1 = idx
k2 = idx
else:
highest_level_index = (np.abs(lev-E['levrange'][1])).argmin()
lowest_level_index = (np.abs(lev-E['levrange'][0])).argmin()
# which index is k1 or k2 depends on the direction of lev
if highest_level_index > lowest_level_index:
k2 = highest_level_index
k1 = lowest_level_index
if highest_level_index < lowest_level_index:
k1 = highest_level_index
k2 = lowest_level_index
lev2 = lev[k1:k2+1]
j2 = (np.abs(lat-E['latrange'][1])).argmin()
j1 = (np.abs(lat-E['latrange'][0])).argmin()
lat2 = lat[j1:j2+1]
i2 = (np.abs(lon-E['lonrange'][1])).argmin()
i1 = (np.abs(lon-E['lonrange'][0])).argmin()
lon2 = lon[i1:i2+1]
if len(VV.shape) == 4:
Xclim = VV[day_indices,k1:k2+1,j1:j2+1,i1:i2+1]
else:
Xclim = VV[day_indices,j1:j2+1,i1:i2+1]
# in this case, we don't need to change the daterange
DRnew = E['daterange']
return Xclim,lat,lon,lev,DRnew
def load_climatology(E,climatology_option = 'NODA',hostname='taurus',verbose=False):
"""
Load a climatology option for a given DART experiment.
The choice of climatology is given by 'climatology_option'. Choices are:
'NODA' (default): take the ensemble mean of the corresponding no-DA experiment as a N-year climatology
'F_W4_L66': CESM-WACCM simulation with observed forcings, 1951-2010 (perfomed by Wuke Wang)
"""
climatology_option_not_found = True
if climatology_option == 'NODA' :
climatology_option_not_found = False
# cycle over the dates in the experiment dictionary
# and load the ensemble mean of the corresponding No-assimilation case
# TODO: a subroutine that returns the corresponding NODA experiment for each case
Xlist = []
ECLIM = E.copy()
ECLIM['exp_name'] = 'W0910_NODA'
ECLIM['diagn'] = 'Prior'
ECLIM['copystring'] = 'ensemble mean'
Xclim,lat,lon,lev,DRnew = DSS.DART_diagn_to_array(ECLIM,hostname=hostname,debug=verbose)
if len(DRnew) != len(ECLIM['daterange']):
print('NOTE: not all requested data were found; returning a revised datarange')
if Xclim is None:
print('Cannot find data for climatology option '+climatology_option+' and experiment '+E['exp_name'])
return None, None, None, None
if climatology_option == 'F_W4_L66' :
from netCDF4 import Dataset
climatology_option_not_found = False
# in this case, load a single daily climatology calculated from this CESM-WACCM simulation
ff = '/data/c1/lneef/CESM/F_W4_L66/atm/climatology/F_W4_L66.cam.h1.1951-2010.daily_climatology.nc'
f = Dataset(ff,'r')
lat = f.variables['lat'][:]
lon = f.variables['lon'][:]
lev = f.variables['lev'][:]
time = f.variables['time'][:]
# load climatology of the desired model variable
variable = E['variable']
if E['variable'] == 'US':
variable = 'U'
if E['variable'] == 'VS':
variable = 'V'
if E['variable'] == 'OLR':
variable = 'FLUT'
VV = f.variables[variable][:]
f.close()
# choose the times corresponding to the daterange in E
d0 = E['daterange'][0].timetuple().tm_yday # day in the year where we start
nT = len(E['daterange'])
df = E['daterange'][nT-1].timetuple().tm_yday # day in the year where we start
# if df<d0, we have to cycle back to the beginning of the year
if df < d0:
day_indices = list(range(d0-1,365))+list(range(0,df))
else:
day_indices = list(range(d0-1,df))
# also choose the lat, lon, and level ranges corresponding to those in E
if E['levrange'] is not None:
if E['levrange'][0] == E['levrange'][1]:
ll = E['levrange'][0]
idx = (np.abs(lev-ll)).argmin()
lev2 = lev[idx]
k1 = idx
k2 = idx
else:
highest_level_index = (np.abs(lev-E['levrange'][1])).argmin()
lowest_level_index = (np.abs(lev-E['levrange'][0])).argmin()
# which index is k1 or k2 depends on the direction of lev
if highest_level_index > lowest_level_index:
k2 = highest_level_index
k1 = lowest_level_index
if highest_level_index < lowest_level_index:
k1 = highest_level_index
k2 = lowest_level_index
lev2 = lev[k1:k2+1]
j2 = (np.abs(lat-E['latrange'][1])).argmin()
j1 = (np.abs(lat-E['latrange'][0])).argmin()
lat2 = lat[j1:j2+1]
i2 = (np.abs(lon-E['lonrange'][1])).argmin()
i1 = (np.abs(lon-E['lonrange'][0])).argmin()
lon2 = lon[i1:i2+1]
if len(VV.shape) == 4:
Xclim = VV[day_indices,k1:k2+1,j1:j2+1,i1:i2+1]
else:
Xclim = VV[day_indices,j1:j2+1,i1:i2+1]
# in this case, we don't need to change the daterange
DRnew = E['daterange']
if climatology_option_not_found:
print('Climatology option '+climatology_option+' has not been coded yet. Returning None for climatology.')
return None, None, None, None
return Xclim,lat,lon,lev,DRnew
