def get_data(nms, pres, year, month, day, concat_dim, subset_dict, verbose):
# Lists of raw and extended variables
ids = list(nms)
ext = []
for var in ['theta', 'theta_e']:
if var in ids:
ext.append(var)
ids.remove(var)
# Read raw data and calculate extended variables
data = read_daily(ids, year, month, day, concat_dim=concat_dim,
subset_dict=subset_dict, verbose=verbose)
if 'theta' in ext:
print_if('Computing potential temperature', verbose)
T = data[get_varname('T')]
data['theta'] = atm.potential_temp(T, pres)
if 'theta_e' in ext:
print_if('Computing equivalent potential temperature', verbose)
T = data[get_varname('T')]
q = data[get_varname('q')]
data['theta_e'] = atm.equiv_potential_temp(T, pres, q)
return data
def calc_fluxes(year, month,
var_ids=['u', 'q', 'T', 'theta', 'theta_e', 'hgt'],
concat_dim='TIME', scratchdir=None, keepscratch=False,
verbose=True):
"""Return the monthly mean of MERRA daily fluxes.
Reads MERRA daily data from OpenDAP urls, computes fluxes, and
returns the monthly mean of the daily variable and its zonal and
meridional fluxes.
Parameters
----------
year, month : int
Numeric year and month (1-12).
var_ids : list of str, optional
IDs of variables to include.
concat_dim : str, optional
Name of dimension for concatenation.
scratchdir : str, optional
Directory path to store temporary files while processing data.
If omitted, the current working directory is used.
keepscratch : bool, optional
If True, scratch files are kept in scratchdir. Otherwise they
are deleted.
verbose : bool, optional
If True, print updates while processing files.
Returns
-------
data : xray.Dataset
Mean of daily data and the mean of the daily zonal fluxes
(u * var) and meridional fluxes (v * var), for each variable
in var_ids.
"""
nms = [get_varname(nm) for nm in atm.makelist(var_ids)]
u_nm, v_nm = get_varname('u'), get_varname('v')
nms.extend([u_nm, v_nm])
if 'theta' in nms:
nms.append(get_varname('T'))
if 'theta_e' in nms:
nms.extend([get_varname('T'), get_varname('q')])
nms = set(nms)
days = range(1, atm.days_this_month(year, month) + 1)
def scratchfile(nm, k, year, month, day):
filestr = '%s_level%d_%d%02d%02d.nc' % (nm, k, year, month, day)
if scratchdir is not None:
filestr = scratchdir + '/' + filestr
return filestr
# Read metadata from one file to get pressure-level array
dataset = 'p_daily'
url = url_list(dataset, return_dict=False)[0]
with xray.open_dataset(url) as ds:
pname = atm.get_coord(ds, 'plev', 'name')
plev = atm.get_coord(ds, 'plev')
# Pressure levels in Pa for theta/theta_e calcs
p_units = atm.pres_units(ds[pname].units)
pres = atm.pres_convert(plev, p_units, 'Pa')
# Get daily data (raw and calculate extended variables)
def get_data(nms, pres, year, month, day, concat_dim, subset_dict, verbose):
# Lists of raw and extended variables
ids = list(nms)
ext = []
for var in ['theta', 'theta_e']:
if var in ids:
ext.append(var)
ids.remove(var)
# Read raw data and calculate extended variables
data = read_daily(ids, year, month, day, concat_dim=concat_dim,
subset_dict=subset_dict, verbose=verbose)
if 'theta' in ext:
print_if('Computing potential temperature', verbose)
T = data[get_varname('T')]
data['theta'] = atm.potential_temp(T, pres)
if 'theta_e' in ext:
print_if('Computing equivalent potential temperature', verbose)
T = data[get_varname('T')]
q = data[get_varname('q')]
data['theta_e'] = atm.equiv_potential_temp(T, pres, q)
return data
# Iterate over vertical levels
for k, p in enumerate(plev):
subset_dict = {pname : (p, p)}
print_if('Pressure-level %.1f' % p, verbose)
files = []
for day in days:
# Read data for this level and day
ds = get_data(nms, pres[k], year, month, day, concat_dim,
subset_dict, verbose)
# Compute fluxes
print_if('Computing fluxes', verbose)
u = ds[get_varname('u')]
v = ds[get_varname('v')]
for nm in var_ids:
var = ds[get_varname(nm)]
varname, attrs, _, _ = atm.meta(var)
u_var = u * var
v_var = v * var
u_var.name = get_varname(u_nm) + '*' + var.name
units = var.attrs['units'] + ' * ' + u.attrs['units']
u_var.attrs['units'] = units
v_var.name = get_varname(v_nm) + '*' + var.name
v_var.attrs['units'] = units
ds[u_var.name] = u_var
ds[v_var.name] = v_var
# Save to temporary scratch file
filenm = scratchfile('fluxes', k, year, month, day)
files.append(filenm)
print_if('Saving to scratch file ' + filenm, verbose)
ds.to_netcdf(filenm)
# Concatenate daily scratch files
ds = atm.load_concat(files)
if not keepscratch:
for f in files:
os.remove(f)
# Compute monthly means
print_if('Computing monthly means', verbose)
if k == 0:
data = ds.mean(dim=concat_dim)
else:
data = xray.concat([data, ds.mean(dim=concat_dim)], dim=pname)
for var in data.data_vars:
data[var].attrs = ds[var].attrs
return data
