def daystr(day):
day = round(day)
mm, dd = atm.jday_to_mmdd(day)
mon = atm.month_str(mm)
return '%.0f (%s-%.0f)' % (day, mon, dd)
'U200' : 'U', 'U850' : 'U', 'V200' : 'V', 'V850' : 'V'}
data = {}
for nm in datafiles:
print('Loading ' + datafiles[nm])
with xray.open_dataset(datafiles[nm]) as ds:
if 'year' in ds.dims:
ds = ds.mean(dim='year')
data[nm] = ds[keys_dict[nm]].load()
# ENSO indices
enso = pd.read_csv(ensofile, index_col=0)
enso = enso.loc[years]
for key in enso_keys:
if key not in enso.columns:
months = atm.season_months(key)
month_names = [(atm.month_str(m)).capitalize() for m in months]
enso[key] = enso[month_names].mean(axis=1)
enso = enso[enso_keys]
col_names = [enso_nm + ' ' + nm for nm in enso.columns]
enso.columns = col_names
# ----------------------------------------------------------------------
# Daily timeseries
ts = xray.Dataset()
for nm in ['GPCP', 'PRECTOT']:
ts[nm] = atm.mean_over_geobox(data[nm], lat1, lat2, lon1, lon2)
ts['MFC'] = utils.daily_rel2onset(index_all['CHP_MFC']['daily_ts'],
index[ind_nm], npre, npost)
ts['CMFC'] = utils.daily_rel2onset(index_all['CHP_MFC']['tseries'],
index[ind_nm], npre, npost)
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)
# Plots
cmap = 'hot_r'
axlims = (-30, 30, 40, 120)
clim1, clim2 = 0, 80
figsize = (12, 9)
months = [4, 5, 6]
plt.figure(figsize=figsize)
plt.suptitle(suptitle)
# Lat-lon maps
for i, month in enumerate(months):
plt.subplot(2, 2, i + 1)
atm.pcolor_latlon(mld[month-1], axlims=axlims, cmap=cmap)
plt.clim(clim1, clim2)
plt.title(atm.month_str(month))
# Sector mean line plots
latmin, latmax = -30, 30
mldplot = atm.subset(mldbar, {'lat' : (latmin, latmax)})
lat = atm.get_coord(mldplot, 'lat')
plt.subplot(2, 2, 4)
for month in months:
plt.plot(lat, mldplot[month-1], label=atm.month_str(month))
plt.legend()
plt.grid()
plt.title('%d-%d E Mean' % (lon1, lon2))
plt.xlabel('Latitude')
plt.ylabel('MLD (m)')
