def single_WLH(cmap_file, yrmin, yrmax, lat0, lon0, loc_nm, kmax, kann,
onset_min=20):
# Single grid point and single year/climatology
precip = precipdat.read_cmap(cmap_file, yrmin, yrmax)
if yrmax > yrmin:
precip = precip.mean(axis=0)
titlestr = 'CMAP %d-%d' % (yrmin, yrmax)
else:
precip = precip[0]
titlestr = 'CMAP %d' % yrmin
latval, ilat0 = atm.find_closest(precip.lat, lat0)
lonval, ilon0 = atm.find_closest(precip.lon, lon0)
d = 1.25
latstr = atm.latlon_labels(latval+d, 'lat', '%.1f', deg_symbol=False)
lonstr = atm.latlon_labels(lonval+d, 'lon', '%.1f', deg_symbol=False)
titlestr = '%s %s (%s, %s)' % (titlestr, loc_nm, latstr, lonstr)
pcp = precip[:, ilat0, ilon0]
pcp_sm, Rsq = atm.fourier_smooth(pcp, kmax)
pcp_ann, Rsq_ann = atm.fourier_smooth(pcp, kann)
i_onset, i_retreat, i_peak = onset_WLH_1D(pcp_sm, threshold, onset_min)
plot_single_WLH(pcp, pcp_sm, pcp_ann, Rsq, Rsq_ann, i_onset, i_retreat, i_peak, kmax, kann, titlestr)
return pcp, pcp_sm, pcp_ann, Rsq, Rsq_ann, i_onset, i_retreat, i_peak
def all_WLH(cmap_file, yearmin=None, yearmax=None, climatology=False,
kmax=12, threshold=5.0, onset_min=20):
precip = precipdat.read_cmap(cmap_file, yearmin, yearmax)
lat = atm.get_coord(precip, 'lat')
lon = atm.get_coord(precip, 'lon')
years = precip.year
if climatology:
precip = precip.mean(dim='year')
axis = 0
else:
axis = 1
wlh = onset_WLH(precip, axis, kmax, threshold, onset_min)
wlh['precip'] = precip
wlh['lat'] = lat
wlh['lon'] = lon
wlh['years'] = years
wlh['climatology'] = climatology
return wlh
years = xray.DataArray(years, {'year' : years})
index['tseries'] = xray.DataArray(pcp_sm, dims=['year', 'day'],
coords={'year' : years, 'day': days})
index['onset'] = xray.DataArray(d_onset, coords={'year' : years})
index['retreat'] = xray.DataArray(d_retreat, coords={'year' : years})
index.attrs['title'] = titlestr
return index
# Threshold and smoothing parameters
threshold = 5.0
kmax = 12
nroll = {'CMAP' : 3, 'MERRA_MFC' : 7, 'MERRA_PRECIP' : 7}
# Read CMAP pentad precip
cmap = precipdat.read_cmap(cmapfile)
cmapbar = atm.mean_over_geobox(cmap, lat1, lat2, lon1, lon2)
cmapdays = [atm.pentad_to_jday(p, pmin=1) for p in cmap.pentad.values]
# MERRA moisture flux convergence
mfc = atm.combine_daily_years('MFC', mfcfiles, years, yearname='year')
mfcbar = atm.mean_over_geobox(mfc, lat1, lat2, lon1, lon2)
# MERRA precip
subset_dict = {'lon' : (lon1, lon2), 'lat' : (lat1, lat2)}
precip = atm.combine_daily_years('PRECTOT', precipfiles, years, yearname='year',
subset_dict=subset_dict)
precip = atm.precip_convert(precip, precip.attrs['units'], 'mm/day')
precipbar = atm.mean_over_geobox(precip, lat1, lat2, lon1, lon2)
# Compute indices for each dataset
for nm in varnms:
if nm == 'PSI':
nm0 = 'V_sector_%dE-%dE' % (lon1, lon2)
elif nm == 'VFLXLQV':
nm0 = 'VFLXQV'
elif nm == dtheta_nm:
nm0 = theta_nm
else:
nm0 = nm
relfiles[nm] = filestr % (nm0, onset_nm, yearstr)
# ----------------------------------------------------------------------
# Read data and calculate indices
# Precipitation
precip = precipdat.read_cmap(pcpfile, yearmin=min(years), yearmax=max(years))
pcp_box = atm.mean_over_geobox(precip, lat1, lat2, lon1, lon2)
# -- Interpolate to daily resolution
days = np.arange(1, 367)
pcp_i = np.nan * np.ones((len(years), len(days)))
for y, year in enumerate(years):
pcp_i[y] = np.interp(days, pcp_box['day'], pcp_box[y])
coords = {'day' : days, 'year' : years}
pcp = xray.DataArray(pcp_i, dims=['year', 'day'], coords=coords)
# Monsoon onset, retreat indices
index = utils.get_onset_indices(onset_nm, indfiles, years)
mfc = atm.rolling_mean(index['ts_daily'], nroll, center=True)
onset = index['onset']
ssn_length=index['length'].mean(dim='year')
pcp_ann[y], label_ann[y])
else:
plot_single_WLH(pcp[y], pcp_sm[y], label_sm[y], i_onset[y],
i_retreat[y], i_peak[y], titlestr + str(years[y]))
if iplot < 4:
plt.xticks(np.arange(0, 74, 4), [])
else:
plt.xticks(np.arange(0, 74, 4))
plt.xlabel('Pentad')
# Read data and average over box
lon1, lon2 = 60, 100
lat1, lat2 = 10, 30
titlestr = 'CMAP %d-%dE, %d-%dN ' % (lon1, lon2, lat1, lat2)
precip = precipdat.read_cmap(cmap_file)
precipbar = atm.mean_over_geobox(precip, lat1, lat2, lon1, lon2)
nyears, npentad = precipbar.shape
years = precipbar.year.values
nyears = len(years)
pentads = precipbar.pentad
onset = {}
# Threshold for onset criteria
threshold = 5.0
# Smooth with truncated Fourier series
kmax = 12
kann = 4
pcp_sm, Rsq = atm.fourier_smooth(precipbar, kmax)
pcp_ann, Rsq_ann = atm.fourier_smooth(precipbar, kann)
