def leap_adjust(data, year):
data = atm.squeeze(data)
ndays = 365
if year is not None and atm.isleap(year):
ndays += 1
else:
# Remove NaN for day 366 in non-leap year
data = atm.subset(data, {'day' : (1, ndays)})
return data, ndays
def get_strength_indices(years, mfc, precip, onset, retreat, yearnm='year',
daynm='day', varnm1='MFC', varnm2='PCP'):
"""Return various indices of the monsoon strength.
Inputs mfc and precip are the unsmoothed daily values averaged over
the monsoon area.
"""
ssn = xray.Dataset()
coords = {yearnm : years}
ssn['onset'] = xray.DataArray(onset, coords=coords)
ssn['retreat'] = xray.DataArray(retreat, coords=coords)
ssn['length'] = ssn['retreat'] - ssn['onset']
data_in = {}
if mfc is not None:
data_in[varnm1] = mfc
if precip is not None:
data_in[varnm2] = precip
for key in data_in:
for key2 in ['_JJAS_AVG', '_JJAS_TOT', '_LRS_AVG', '_LRS_TOT']:
ssn[key + key2] = xray.DataArray(np.nan * np.ones(len(years)),
coords=coords)
for key in data_in:
for y, year in enumerate(years):
d1 = int(onset.values[y])
d2 = int(retreat.values[y] - 1)
days_jjas = atm.season_days('JJAS', atm.isleap(year))
data = atm.subset(data_in[key], {yearnm : (year, None)})
data_jjas = atm.subset(data, {daynm : (days_jjas, None)})
data_lrs = atm.subset(data, {daynm : (d1, d2)})
ssn[key + '_JJAS_AVG'][y] = data_jjas.mean(dim=daynm).values
ssn[key + '_LRS_AVG'][y] = data_lrs.mean(dim=daynm).values
ssn[key + '_JJAS_TOT'][y] = ssn[key + '_JJAS_AVG'][y] * len(days_jjas)
ssn[key + '_LRS_TOT'][y] = ssn[key + '_LRS_AVG'][y] * ssn['length'][y]
ssn = ssn.to_dataframe()
return ssn
def season_days(season, year, d_onset, d_retreat):
if season == 'SSN':
days = range(0, d_retreat - d_onset + 1)
else:
days = atm.season_days(season, atm.isleap(year)) - d_onset
return days
return filn
lon1, lon2 = 40, 100
lat1, lat2 = -15, 35
varlist = ['T']
pmin, pmax = 200e2, 600e2
years = range(1979, 2015)
months = [4, 5, 6, 7, 8, 9]
g = atm.constants.g.values
nperday = 8
subset_dict = {'lon' : (lon1, lon2), 'lat' : (lat1, lat2)}
for year in years:
for mon in months:
dayvals = atm.season_days(atm.month_str(mon), atm.isleap(year))
T = merra.read_daily(varlist, year, mon, subset_dict=subset_dict)
# Daily means of 3-hourly data
T = atm.daily_from_subdaily(T, nperday, dayvals=dayvals)
# Vertical integral
Tbar = atm.int_pres(T, pmin=pmin, pmax=pmax)
Tbar = Tbar * g / (pmax - pmin)
Tbar.name='Tbar'
Tbar.attrs['long_name'] = 'Vertical mean atmospheric temperature'
Tbar.attrs['pmin'] = pmin
Tbar.attrs['pmax'] = pmax
# Save to file
atm.save_nc(savefile(year, mon, pmin, pmax), T, Tbar)
