import numpy as np import xray import pandas as pd import matplotlib.pyplot as plt import atmos as atm import merra from indices import onset_SJKE, summarize_indices, plot_index_years # ---------------------------------------------------------------------- # Compute SJ indices (Boos and Emmanuel 2009) datadir = atm.homedir() + 'datastore/merra/daily/' years = np.arange(1979, 2015) filestr = 'merra_uv850_40E-120E_60S-60N_' datafiles = [datadir + filestr + '%d.nc' % y for y in years] # Read daily data from each year ds = atm.combine_daily_years(['U', 'V'], datafiles, years) # Remove extra dimension from data u = atm.squeeze(ds['U']) v = atm.squeeze(ds['V']) # Calculate OCI index sjke = onset_SJKE(u, v) # Summary plot and timeseries in individual years summarize_indices(years, sjke['onset']) plot_index_years(sjke, suptitle='SJ', yearnm='Year', daynm='Day')
plt.grid()
plt.legend(fontsize=12)
atm.scatter_matrix(strength)
plt.suptitle('JJAS Monsoon Strength')
saveclose('strength_', isave, exts)
# ----------------------------------------------------------------------
# Histograms of each index
keys = index.keys()
for key in keys:
ind = index[key]
if 'retreat' in ind.keys():
retreat = ind.retreat
else:
retreat = None
indices.summarize_indices(ind.year, ind.onset, retreat, ind.title)
saveclose('onset_retreat_hist_', isave, exts)
# ----------------------------------------------------------------------
# Daily timeseries of each index in each year
keys = index.keys()
# keys = ['OCI', 'TT']
for key in keys:
indices.plot_index_years(index[key], suptitle=key, vertline=True)
saveclose('index_tseries_' + key + '_', isave, exts)
# ----------------------------------------------------------------------
# Compare indices with each other
# keys = ['HOWI_100', 'HOWI_50', 'OCI', 'SJKE', 'TT', 'WLH_CMAP_kmax12',
# 'WLH_CMAP_nroll3', 'WLH_MERRA_PRECIP_nroll7']
suptitle = varname + ' N=%s S=%s' % (str(north), str(south))
tt = onset_TT(T, north=north, south=south)
# Some weirdness going on in 1991, for now just set to NaN
# Troubleshoot later
for nm in ['ttn', 'tts', 'tseries']:
vals = tt[nm].values
vals = np.ma.masked_array(vals, abs(vals) > 1e30).filled(np.nan)
tt[nm].values = vals
# Plot TTN and TTS
plot_tseries_together(tt[['ttn', 'tts']], tt['onset'].values,
suptitle=suptitle, standardize=False)
# Summary plot and timeseries in individual years
summarize_indices(years, tt['onset'])
plt.suptitle(suptitle)
plot_index_years(tt, suptitle=suptitle, yearnm='year', daynm='day')
if save:
atm.savefigs(varname + '_', 'png')
# Plot contour map of pressure-level data
p_plot = 400
T_plot = T_p[p_plot]
y, d = 0, 80
lat = atm.get_coord(T_plot, 'lat')
lon = atm.get_coord(T_plot, 'lon')
axlims = (lat.min(), lat.max(), lon.min(), lon.max())
plt.figure()
atm.pcolor_latlon(T_plot[y, d], axlims=axlims)
def index_tseries(days, ind, ind_roll, titlestr):
plt.plot(days, ind, label='daily')
plt.plot(days, ind_roll, label='%d-day rolling' % nroll)
plt.grid()
plt.legend(loc='lower right')
plt.title(titlestr)
plt.figure(figsize=(12, 10))
plt.subplot(221)
index_tseries(days, ds.howi_clim_norm, ds.howi_clim_norm_roll,
'HOWI ' + yearstr + ' Climatology')
for yr in [0, 1, 2]:
plt.subplot(2, 2, yr + 2)
index_tseries(days, ds.howi_norm[yr], ds.howi_norm_roll[yr],
'HOWI %d' % years[yr])
# ----------------------------------------------------------------------
# Onset and retreat indices
summarize_indices(years, onset, retreat, 'HOWI')
# ----------------------------------------------------------------------
# Plot timeseries of each year
plot_index_years(howi)
# ----------------------------------------------------------------------
# Save figures
if isave:
for ext in exts:
atm.savefigs(namestr, ext)
