def run_all_plots():
#************************************************************************
# Global Map
if True:
cube = read_data(
DATALOC +
"GSG_{}annual mean_G_ano_ref1998-2008.txt".format(settings.YEAR))
bounds = [-100, -20, -15, -8, -4, 0, 4, 8, 15, 20, 100]
bounds = [-200, -90, -40, -15, -5, -1, 1, 5, 15, 40, 90, 200]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "SOZ_{}_anoms".format(settings.YEAR), cube,
settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 1998-2008 (DU)")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_SOZ_{}_anoms".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 1998-2008 (DU)",
figtext="(z) Stratospheric (Total Column) Ozone")
#************************************************************************
# Timeseries (plate 1.1)
if True:
# nh = read_ts(DATALOC + "tozave_60N_90N_3_3.dat")
# sh = read_ts(DATALOC + "tozave_90S_60S_10_10.dat")
nh = read_ts(DATALOC + "NH_polar_ozone.txt")
sh = read_ts(DATALOC + "SH_polar_ozone.txt")
fig = plt.figure(figsize=(8, 6))
plt.clf()
plt.plot(nh.times, nh.data, "b-", label="March NH")
plt.plot(sh.times, sh.data, "r-", label="October SH")
plt.ylabel("total Ozone (DU")
plt.title("Polar Ozone")
plt.legend()
plt.savefig(settings.IMAGELOC + "SOZ_ts{}".format(settings.OUTFMT))
return # run_all_plots
def run_all_plots():
#************************************************************************
# Global Map
if True:
bounds = [-1000, -160, -120, -80, -40, 0, 40, 80, 120, 160, 1000]
cube = read_map(DATALOC + "map")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "GLE_{}_anoms".format(settings.YEAR), cube,
settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 1981-2010 (mm year" + r'$^{-1}$' + ")")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_GLE_{}_anoms".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Anomalies from 1981-2010 (mm year" + r'$^{-1}$' + ")",
figtext="(s) Land Evaporation")
# Evaporation Map
if True:
bounds = [-1000, -160, -120, -80, -40, 0, 40, 80, 120, 160, 1000]
cube = read_map(DATALOC + "map_transp")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "GLE_{}_transp".format(settings.YEAR), cube,
settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 1981-2010 (mm year" + r'$^{-1}$' + ")")
print("no other figures for {}'s report - author has made own".format(
settings.YEAR))
sys.exit()
#************************************************************************
# Timeseries figures
if False:
fig, ax1 = plt.subplots(figsize=(8, 5))
globe, NH, SH, SOI = read_time(DATALOC + "timeseries")
utils.plot_ts_panel(ax1, [globe, NH, SH],
"-",
"hydrological",
loc=LEGEND_LOC)
for data in [globe, NH, SH]:
slope, dummy, dummy = utils.median_pairwise_slopes(
data.times, data.data, -99.9, 1.)
fit_years, fit_values = utils.mpw_plot_points(
slope, data.times, data.data)
ax1.plot(fit_years, fit_values, c=settings.COLOURS["hydrological"][data.name], \
lw=2, ls="--")
ax1.set_ylabel("Anomalies (mm year" + r'$^{-1}$' + ")",
fontsize=settings.FONTSIZE)
ax1.set_ylim([-29, 29])
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax2 = ax1.twinx()
interpTimes = np.linspace(SOI.times[0], SOI.times[-1], 1000)
interpData = np.interp(interpTimes, SOI.times, SOI.data)
interpSOI = utils.Timeseries("SOI", interpTimes, interpData)
ax2.fill_between(interpSOI.times, interpSOI.data, where=interpSOI.data >= 0, \
color='b', alpha=0.5, zorder=-1)
ax2.fill_between(interpSOI.times, interpSOI.data, where=interpSOI.data <= 0, \
color='r', alpha=0.5, zorder=-1)
ax2.set_xlim([1979, int(settings.YEAR) + 1])
ax2.set_ylim([-5, 5])
ax2.set_ylabel("SOI", fontsize=settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax2.yaxis.get_major_ticks():
tick.label2.set_fontsize(settings.FONTSIZE)
utils.thicken_panel_border(ax2)
plt.savefig(settings.IMAGELOC + "GLE_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Hoemuller figure
if False:
bounds = [-100, -10, -8, -4, -2, 0, 2, 4, 8, 10, 100]
times, latitudes, indata = read_hovmuller(DATALOC + "latitudinal")
utils.plot_hovmuller(settings.IMAGELOC + "GLE_hovmuller", times, latitudes, indata, \
settings.COLOURMAP_DICT["hydrological"], bounds, \
"Anomaly (mm month"+r'$^{-1}$'+")")
return # run_all_plots
def run_all_plots():
#************************************************************************
# Precipitation Timeseries
if False:
ghcn, gpcc, gpcp, ghcn2, erai, merra2 = read_land(
DATALOC + "Land_insitu_timeseries-1979.dat")
fig = plt.figure(figsize=(8, 5))
ax1 = plt.axes([0.1, 0.1, 0.85, 0.85])
# Land
utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp, ghcn2, erai, merra2],
"-",
"hydrological",
loc=LEGEND_LOC,
bbox=BBOX)
ax1.set_ylim([-60, 100])
ax1.yaxis.set_ticks([-50, -25, 0, 25, 50, 75, 100])
ax1.set_ylabel("Anomaly (mm)", fontsize=settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# ax1.text(0.02, 0.9, "(a) Land in Situ", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC + "PCP_ts{}".format(settings.OUTFMT))
plt.close()
# old 3-panel plot for 2015 report
if False:
fig = plt.figure(figsize=(8, 8))
ax1 = plt.axes([0.11, 0.68, 0.78, 0.3])
ax2 = plt.axes([0.11, 0.38, 0.78, 0.3], sharex=ax1)
ax3 = plt.axes([0.11, 0.08, 0.78, 0.3], sharex=ax1)
# Land
utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp, ghcn2, erai, merra2],
"-",
"hydrological",
loc=LEGEND_LOC,
bbox=BBOX)
# Ocean
ocean_year = read_domain(
DATALOC + "gpcp_v23_globalocean_ann_1979_2018", "GPCP")
o_clim, o_anoms = utils.calculate_climatology_and_anomalies_1d(
ocean_year, 1981, 2010)
ax2.plot(o_anoms.times,
o_anoms.data,
c="b",
ls="-",
label=o_anoms.name,
lw=2)
# Nino and others
land_year = read_domain(DATALOC + "gpcp_v23_globalland_ann_1979_2018",
"GPCP")
l_clim, l_anoms = utils.calculate_climatology_and_anomalies_1d(
land_year, 1981, 2010)
combined_year = read_domain(
DATALOC + "gpcp_v23_globallandocean_ann_1979_2018", "GPCP")
c_clim, c_anoms = utils.calculate_climatology_and_anomalies_1d(
combined_year, 1981, 2010)
nino = read_domain(DATALOC + "nino34_1979_2018_ann_anomaly",
"Nino 3.4")
ax3.plot(o_anoms.times,
o_anoms.data,
c="b",
ls="-",
label="{} Ocean".format(o_anoms.name),
lw=2,
zorder=10)
ax3.plot(l_anoms.times,
l_anoms.data,
c="lime",
ls="-",
label="{} Land".format(l_anoms.name),
lw=2,
zorder=10)
ax3.plot(c_anoms.times,
c_anoms.data,
c="r",
ls="-",
label="{} Land + Ocean".format(c_anoms.name),
lw=2,
zorder=10)
ax3.plot([1960, 1961], [0, 0], c="k", label="Nino 3.4")
ax4 = ax3.twinx()
ax4.plot(nino.times, nino.data, c="k", zorder=1)
ax4.fill_between(nino.times,
nino.data,
0,
color="0.5",
label=nino.name,
zorder=1)
ax4.set_ylim([-1.9, 1.9])
ax3.patch.set_visible(False)
ax3.set_zorder(ax4.get_zorder() + 1)
#*******************
# prettify
minorLocator = MultipleLocator(1)
ax1.set_ylim([-60, 100])
ax1.yaxis.set_ticks([-50, 0, 50, 100])
for ax in [ax2, ax3]:
ax.set_ylim([-50, 50])
ax.axhline(0, c='0.5', ls='--')
ax.legend(loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_ticks_position('left')
utils.thicken_panel_border(ax)
ax2.set_ylabel("Anomaly (mm yr" + r'$^{-1}$' + ")",
fontsize=settings.FONTSIZE)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
ax4.set_xlim([1979, int(settings.YEAR) + 1])
ax4.yaxis.set_label_position("right")
ax3.yaxis.set_tick_params(right=False)
utils.thicken_panel_border(ax4)
for ax in [ax1, ax2, ax3]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax4.yaxis.get_major_ticks():
tick.label2.set_fontsize(settings.FONTSIZE)
# sort labelling
ax1.text(0.02,
0.9,
"(a) Land in Situ",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Ocean",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02,
0.9,
"(c) Globe",
transform=ax3.transAxes,
fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC +
"PCP_ts_3panel{}".format(settings.OUTFMT))
plt.close()
# 3-panel plot for 2019 report
if True:
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 8), sharex=True)
# Land
ghcn, gpcc, gpcp = read_land(DATALOC +
"Land_insitu_timeseries-1979.dat")
utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp],
"-",
"hydrological",
loc=LEGEND_LOC,
bbox=BBOX)
# Ocean
gpcp = read_ocean(DATALOC + "Ocean_insitu_timeseries-1979.dat")
utils.plot_ts_panel(ax2, [gpcp],
"-",
"hydrological",
loc="",
bbox=BBOX)
# Globe
gpcp = read_ocean(DATALOC + "Global_insitu_timeseries-1979.dat")
utils.plot_ts_panel(ax3, [gpcp],
"-",
"hydrological",
loc="",
bbox=BBOX)
#*******************
# prettify
fig.subplots_adjust(right=0.98, bottom=0.08, top=0.98, hspace=0.001)
minorLocator = MultipleLocator(1)
ax1.set_ylim([-60, 100])
ax1.yaxis.set_ticks([-50, 0, 50, 100])
ax2.set_ylim([-29, 43])
ax3.set_ylim([-29, 43])
for ax in [ax1, ax2, ax3]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax2.set_ylabel("Anomaly (mm yr" + r'$^{-1}$' + ")",
fontsize=settings.FONTSIZE)
# sort labelling
ax1.text(0.02,
0.9,
"(a) Land in Situ",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Ocean",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02,
0.9,
"(c) Globe",
transform=ax3.transAxes,
fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC +
"PCP_ts_3panel{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# GPCP map
if True:
cube = read_map(
DATALOC +
"GPCP_{}anomaly_base_1981-2000.txt".format(settings.YEAR))
bounds = [-2000, -400, -300, -200, -100, 0, 100, 200, 300, 400, 2000]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PCP_{}_anoms_gpcp".format(settings.YEAR),
cube, settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 1981-2000 (mm)")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_PCP_{}_anoms_gpcp".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Anomalies from 1981-2000 (mm)",
figtext="(k) Precipitation")
#************************************************************************
# GPCP map - Ocean only
# # Read Oceans
# print "FIX YEAR"
# cube = iris.load(DATALOC + "ocean_precipitation_sotc2015.nc", "anomaly_map")[0]
# # add in the coordinates which haven't been stored sensibly
# for c, coord in enumerate(["latitude", "longitude"]):
# coord_cube = iris.load(DATALOC + "ocean_precipitation_sotc2015.nc", coord)[0]
# if c == 0:
# iris_coord = iris.coords.DimCoord(coord_cube.data[:,0], standard_name=coord, units='degrees')
# elif c == 1:
# iris_coord = iris.coords.DimCoord(coord_cube.data[0], standard_name=coord, units='degrees')
# cube.add_dim_coord(iris_coord,c)
# cube.coord(coord).guess_bounds()
# bounds=[-2000, -400, -300, -200, -100, 0, 100, 200, 300, 400, 2000]
# utils.plot_smooth_map_iris(settings.IMAGELOC + "PCP_{}_anoms_gpcp_ocean".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2000 (mm)")
return # run_all_plots
def run_all_plots():
if True:
# multipanel timeseries
COLOURS = settings.COLOURS["temperature"]
fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6,
figsize=(8, 19),
sharex=True)
# ERA5
era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read(
settings.REANALYSISLOC, "sat", annual=True)
land_era5_clim, land_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_land, 1981, 2010)
ocean_era5_clim, ocean_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_ocean, 1981, 2010)
global_era5_clim, global_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_globe, 1981, 2010)
#*******************
# in situ L+O
noaa, nasa, jma = read_global_t(DATALOC +
"{}_LO.csv".format(IS_timeseries_root))
hadcrut = read_hadcrut_crutem(DATALOC + "hadcrut4.1981-2010.csv")
p0 = ax1.plot(noaa.times,
noaa.data,
c=COLOURS[noaa.name],
ls='-',
label=noaa.name,
lw=LW)
p1 = ax1.plot(nasa.times,
nasa.data,
c=COLOURS[nasa.name],
ls='-',
label=nasa.name,
lw=LW)
# p2 = ax1.plot(jma.times, jma.data, c=COLOURS[jma.name], ls='-', label=jma.name, lw=LW)
p3 = ax1.plot(hadcrut.times,
hadcrut.data,
c=COLOURS[hadcrut.name],
ls='-',
label=hadcrut.name,
lw=LW)
ax1.fill_between(hadcrut.times, hadcrut.lower, hadcrut.upper, \
where=hadcrut.upper > hadcrut.lower, color='0.5', alpha=0.7)
p4 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7)
ax1.axhline(0, c='0.5', ls='--')
ax1.legend([p0[0], p1[0], (p3[0], p4[0])], [noaa.name, nasa.name, hadcrut.name], \
loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax1.text(0.02,
0.9,
"(a) In Situ Land and Ocean",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE)
utils.thicken_panel_border(ax1)
# ax1.yaxis.set_ticks_position('left')
#*******************
# reanalysis L+O
merra = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "LO")
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_tmp2m_global_ts.txt",
"temperature")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "global.2mt.skt.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1981, 2010)
twenty_cr_anoms.zorder = -1
# 2018 no MERRA
utils.plot_ts_panel(ax2, [jra_anoms, global_era5_anoms],
"-",
"temperature",
loc=LEGEND_LOC,
bbox=BBOX)
ax2.text(0.02,
0.9,
"(b) Reanalysis Land and Ocean",
transform=ax2.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# in situ L
noaa, nasa, jma = read_global_t(DATALOC +
"{}_L.csv".format(IS_timeseries_root))
crutem = read_hadcrut_crutem(DATALOC +
"crutem4_new_logo.1981-2010.csv")
p0 = ax3.plot(noaa.times,
noaa.data,
ls='-',
c=COLOURS[noaa.name],
label=noaa.name,
lw=LW)
p1 = ax3.plot(nasa.times,
nasa.data,
ls='-',
c=COLOURS[nasa.name],
label=nasa.name,
lw=LW)
# p2 = ax3.plot(jma.times, jma.data, ls='-', c=COLOURS[jma.name], label=jma.name, lw=LW)
# p3 = ax3.plot(berkeley.times, berkeley.data, ls = '-', c = COLOURS[berkeley.name], label = berkeley.name, lw = LW)
p4 = ax3.plot(crutem.times,
crutem.data,
ls='-',
c=COLOURS[crutem.name],
label=crutem.name,
lw=LW)
ax3.fill_between(crutem.times, crutem.lower, crutem.upper, \
where=crutem.upper > crutem.lower, color='0.5', alpha=0.7)
p5 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7)
ax3.axhline(0, c='0.5', ls='--')
ax3.legend([p0[0], p1[0], (p4[0], p5[0])], [noaa.name, nasa.name, crutem.name], \
loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax3.text(0.02,
0.9,
"(c) In Situ Land only",
transform=ax3.transAxes,
fontsize=settings.FONTSIZE)
utils.thicken_panel_border(ax3)
# ax3.yaxis.set_ticks_position('left')
#*******************
# reanalysis L
merra = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "L")
jra_actual, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_tmp2m_globalland_ts.txt",
"temperature")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "air2mland.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1981, 2010)
twenty_cr_anoms.zorder = -1
# 2018 - No MERRA
utils.plot_ts_panel(ax4, [jra_anoms, land_era5_anoms],
"-",
"temperature",
loc=LEGEND_LOC,
bbox=BBOX)
ax4.text(0.02,
0.9,
"(d) Reanalysis Land only",
transform=ax4.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# in situ O
noaa, nasa, jma = read_global_t(DATALOC +
"{}_O.csv".format(IS_timeseries_root))
hadsst = read_hadcrut_crutem(DATALOC +
"hadsst3_new_logo.1981-2010.csv")
p0 = ax5.plot(noaa.times,
noaa.data,
ls='-',
c=COLOURS[noaa.name],
label=noaa.name,
lw=LW)
p1 = ax5.plot(nasa.times,
nasa.data,
ls='-',
c=COLOURS[nasa.name],
label=nasa.name,
lw=LW)
# p2 = ax5.plot(jma.times, jma.data, ls='-', c=COLOURS[jma.name], label=jma.name, lw=LW)
p3 = ax5.plot(hadsst.times,
hadsst.data,
ls='-',
c=COLOURS[hadsst.name],
label=hadsst.name,
lw=LW)
ax5.fill_between(hadsst.times, hadsst.lower, hadsst.upper, \
where=hadsst.upper > hadsst.lower, color='0.5', alpha=0.7)
p4 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7)
ax5.axhline(0, c='0.5', ls='--')
ax5.legend([p0[0], p1[0], (p3[0], p4[0])], [noaa.name, nasa.name, hadsst.name], \
loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax5.text(0.02,
0.9,
"(e) In Situ Ocean only",
transform=ax5.transAxes,
fontsize=settings.FONTSIZE)
utils.thicken_panel_border(ax5)
# ax5.yaxis.set_ticks_position('left')
#*******************
# reanalysis O
merra = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "O")
jra_actual, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_tmp2m_globalocean_ts.txt",
"temperature")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "airsktocean.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1981, 2010)
twenty_cr_anoms.zorder = -1
# 2018 no MERRA
utils.plot_ts_panel(ax6, [jra_anoms, ocean_era5_anoms],
"-",
"temperature",
loc=LEGEND_LOC,
bbox=BBOX)
ax6.text(0.02,
0.9,
"(f) Reanalysis Ocean only",
transform=ax6.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# prettify
fig.text(0.03,
0.5,
"Anomalies (" + r'$^{\circ}$' + "C)",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
plt.xlim([1900, int(settings.YEAR) + 2])
minorLocator = MultipleLocator(5)
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
ax.set_ylim(YLIM)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax6.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
fig.subplots_adjust(right=0.96, top=0.995, bottom=0.02, hspace=0.001)
plt.savefig(settings.IMAGELOC + "SAT_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# ERA5 Anomaly figure
if True:
# Read in ERA anomalies
cube_list = iris.load(settings.REANALYSISLOC +
"era5_t2m_{}01-{}12_ann_ano.nc".format(
settings.YEAR, settings.YEAR))
for cube in cube_list:
if cube.var_name == "T2M":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "SAT_{}_anoms_era5".format(settings.YEAR),
cube[0],
settings.COLOURMAP_DICT["temperature"],
bounds,
"Anomalies from 1981-2010 (" + r'$^{\circ}$' + "C)",
title="ERA5")
#************************************************************************
# MERRA2 Anomaly figure
if True:
cube_list = iris.load(settings.REANALYSISLOC +
"MERRA-2_SfcAnom_{}.nc".format(settings.YEAR))
for cube in cube_list:
if cube.var_name == "t2ma":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_merra".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="MERRA-2")
#************************************************************************
# HadCRUT4 Anomaly figure
if True:
cube_list = iris.load(DATALOC + "HadCRUT.4.6.0.0.median.nc")
for cube in cube_list:
if cube.var_name == "temperature_anomaly":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
# restrict to 1851 to last full year
date_constraint = utils.periodConstraint(
cube, dt.datetime(1850, 1, 1),
dt.datetime(int(settings.YEAR) + 1, 1, 1))
cube = cube.extract(date_constraint)
# convert to 1981-2010 climatology.
clim_constraint = utils.periodConstraint(cube, dt.datetime(1981, 1, 1),
dt.datetime(2011, 1, 1))
clim_cube = cube.extract(clim_constraint)
clim_data = clim_cube.data.reshape(-1, 12, clim_cube.data.shape[-2],
clim_cube.data.shape[-1])
# more than 15 years present
climatology = np.ma.mean(clim_data, axis=0)
nyears = np.ma.count(clim_data, axis=0)
climatology = np.ma.masked_where(nyears <= 15,
climatology) # Kate keeps GT 15.
# extract final year
final_year_constraint = utils.periodConstraint(
cube, dt.datetime(int(settings.YEAR), 1, 1),
dt.datetime(int(settings.YEAR) + 1, 1, 1))
final_year_cube = cube.extract(final_year_constraint)
final_year_cube.data = final_year_cube.data - climatology
# more than 6 months present
annual_cube = final_year_cube.collapsed(['time'], iris.analysis.MEAN)
nmonths = np.ma.count(final_year_cube.data, axis=0)
annual_cube.data = np.ma.masked_where(nmonths <= 6, annual_cube.data)
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_hadcrut4".format(settings.YEAR), annual_cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="HadCRUT 4.6")
#************************************************************************
# NOAA data Anomaly figure - incl plate 2.1
if True:
cube = read_noaa_mlost(DATALOC + "mlost-box.ytd.12.1981-2010bp.txt",
int(settings.YEAR))
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_SAT_{}_anoms_noaa".format(settings.YEAR), cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \
figtext="(a) Surface Temperature", \
save_netcdf_filename="{}MLOST_for_NOAA_{}.nc".format(DATALOC, dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y")))
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_noaa".format(settings.YEAR), cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="NOAAGlobalTemp")
#************************************************************************
# JRA55 data Anomaly figure
if True:
cube_list = iris.load(settings.REANALYSISLOC +
"jra55_t2m_{}01-{}12_ann_ano.nc".format(
settings.YEAR, settings.YEAR))
for cube in cube_list:
if cube.var_name == "T2M":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_jra55".format(settings.YEAR), cube[0,0], \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="JRA-55")
#************************************************************************
# NASA GISS Anomaly figure
if True:
#cube = read_nasa_giss(DATALOC + "nasa-2015-anomalies-wrt1981-2010bp")
cube = iris.load(DATALOC + "gistemp1200_GHCNv4_ERSSTv5.nc")[0]
# convert to 1981-2010 climatology.
clim_constraint = utils.periodConstraint(cube, dt.datetime(1981, 1, 1),
dt.datetime(2011, 1, 1))
clim_cube = cube.extract(clim_constraint)
clim_data = clim_cube.data.reshape(-1, 12, clim_cube.data.shape[-2],
clim_cube.data.shape[-1])
# more than 15 years present
climatology = np.ma.mean(clim_data, axis=0)
nyears = np.ma.count(clim_data, axis=0)
climatology = np.ma.masked_where(nyears <= 15,
climatology) # Kate keeps GT 15.
# extract final year
final_year_constraint = utils.periodConstraint(cube, dt.datetime(int(settings.YEAR), 1, 1), \
dt.datetime(int(settings.YEAR)+1, 1, 1))
final_year_cube = cube.extract(final_year_constraint)
final_year_cube.data = final_year_cube.data - climatology
# more than 6 months present
annual_cube = final_year_cube.collapsed(['time'], iris.analysis.MEAN)
nmonths = np.ma.count(final_year_cube.data, axis=0)
annual_cube.data = np.ma.masked_where(nmonths <= 6, annual_cube.data)
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_nasa".format(settings.YEAR), annual_cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="NASA GISS")
return # run_all_plots
def run_all_plots():
if True:
#***************
# Figure 1
euro, africa, tibet, canada = read_ts(DATALOC +
"BAMS2020Fig1_data_LSWT.csv")
# euro_fit, africa_fit, tibet_fit, canada_fit = read_ts(DATALOC + "Fig1_lines_LSWT.csv")
euro_fit = utils.Timeseries("Lake", [1994, 2020],
[-0.5136, (2020 - 1994) * 0.0386 - 0.5136])
africa_fit = utils.Timeseries(
"Lake", [1994, 2020], [0.0204, (2020 - 1994) * 0.0036 + 0.0204])
tibet_fit = utils.Timeseries("Lake", [1994, 2020],
[0.0878, (2020 - 1994) * 0.0017 + 0.0878])
canada_fit = utils.Timeseries(
"Lake", [1994, 2020], [-0.2018, (2020 - 1994) * 0.0223 - 0.2018])
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,
figsize=(8, 10),
sharex=True)
#***************
# the timeseries
LEGEND_LOC = ""
utils.plot_ts_panel(ax1, [euro], "-", "temperature", loc=LEGEND_LOC)
ax1.plot(euro_fit.times,
euro_fit.data,
c=settings.COLOURS["temperature"][euro_fit.name],
lw=2,
ls="--")
ax1.text(1995, 0.8, "Europe, 127 lakes", fontsize=settings.FONTSIZE)
utils.plot_ts_panel(ax2, [africa], "-", "temperature", loc=LEGEND_LOC)
ax2.plot(africa_fit.times,
africa_fit.data,
c=settings.COLOURS["temperature"][africa_fit.name],
lw=2,
ls="--")
ax2.text(1995, 0.8, "Africa, 68 lakes", fontsize=settings.FONTSIZE)
utils.plot_ts_panel(ax3, [tibet], "-", "temperature", loc=LEGEND_LOC)
ax3.plot(tibet_fit.times,
tibet_fit.data,
c=settings.COLOURS["temperature"][tibet_fit.name],
lw=2,
ls="--")
ax3.text(1995,
0.8,
"Tibetan Plateau, 106 lakes",
fontsize=settings.FONTSIZE)
utils.plot_ts_panel(ax4, [canada], "-", "temperature", loc=LEGEND_LOC)
ax4.plot(canada_fit.times,
canada_fit.data,
c=settings.COLOURS["temperature"][canada_fit.name],
lw=2,
ls="--")
ax4.text(1995, 0.8, "Canada, 244 lakes", fontsize=settings.FONTSIZE)
# prettify
for ax in [ax1, ax2, ax3, ax4]:
ax.axhline(0, c='0.5', ls='--')
utils.thicken_panel_border(ax)
ax.set_ylim([-1, 1.2])
ax.set_xlim([euro.times[0] - 1, int(settings.YEAR) + 1])
ax.yaxis.set_ticks_position('left')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax4.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
fig.text(0.01,
0.35,
"Anomaly from 1996-2016 (" + r'$^\circ$' + "C)",
fontsize=settings.FONTSIZE,
rotation="vertical")
fig.subplots_adjust(bottom=0.03, right=0.96, top=0.99, hspace=0.001)
plt.savefig(settings.IMAGELOC + "LKT_ts{}".format(settings.OUTFMT))
plt.close()
#***************
# Anomaly Scatter map
if True:
anomalies = read_lakes(DATALOC + "PlateX_data_LSWT.csv")
bounds = [-8, -2, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2, 8]
# bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
lons = np.arange(-90, 120, 30)
lats = np.arange(-180, 210, 30)
dummy = np.ma.zeros((len(lats), len(lons)))
dummy.mask = np.ones(dummy.shape)
cube = utils.make_iris_cube_2d(dummy, lats, lons, "blank", "m")
utils.plot_smooth_map_iris(settings.IMAGELOC + "LKT_anomaly", cube, settings.COLOURMAP_DICT["temperature"], \
bounds, "Anomalies from 1996-2016 ("+r"$^{\circ}$"+"C)", \
scatter=[anomalies[1], anomalies[0], anomalies[2]], figtext="", title="")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LKT_anomaly", cube, settings.COLOURMAP_DICT["temperature"], \
bounds, "Anomalies from 1996-2016 ("+r"$^{\circ}$"+"C)", \
scatter=[anomalies[1], anomalies[0], anomalies[2]], \
figtext="(b) Lake Temperatures", title="")
#***************
# Insets Scatter map
if True:
fig = plt.figure(figsize=(8, 7))
plt.clf()
anomalies = read_lakes(DATALOC + "Fig2_data_LSWT.csv")
bounds = [-8, -2, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2, 8]
# bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
cmap = settings.COLOURMAP_DICT["temperature"]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
this_cmap = copy.copy(cmap)
# first_cube = iris.load(DATALOC + "amaps_1st_quarter_2018_250km.nc")[0]
# third_cube = iris.load(DATALOC + "amaps_3rd_quarter_2018_250km.nc")[0]
# annual_cube = iris.load(DATALOC + "amaps_annual_2018_250km.nc")[0]
cube = iris.load(DATALOC + "lswt_anom_1979_2019.nc")[0]
if settings.OUTFMT in [".eps", ".pdf"]:
if cube.coord("latitude").points.shape[0] > 180 or cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print("Regridding cube for {} output to {} degree resolution".
format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(cube.data.shape))
plot_cube = utils.regrid_cube(cube, regrid_size, regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
else:
plot_cube = copy.deepcopy(cube)
else:
plot_cube = copy.deepcopy(cube)
# make axes by hand
axes = ([0.01, 0.55, 0.59,
0.41], [0.565, 0.45, 0.47,
0.50], [0.01, 0.13, 0.59,
0.41], [0.61, 0.07, 0.38,
0.41], [0.1, 0.1, 0.8, 0.03])
# Europe
ax = plt.axes(axes[0], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
#ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5)
ax.set_extent([-25, 40, 34, 72], cartopy.crs.PlateCarree())
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(a) Europe",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# Africa
ax = plt.axes(axes[1], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
#ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5)
ax.set_extent([-19, 43, -40, 33], cartopy.crs.PlateCarree())
# lat_constraint = utils.latConstraint([25, 90])
# nh_cube = third_cube.extract(lat_constraint)
# lat_constraint = utils.latConstraint([-90, -25])
# sh_cube = first_cube.extract(lat_constraint)
# lat_constraint = utils.latConstraint([-25, 25])
# trop_cube = annual_cube.extract(lat_constraint)
# mesh = iris.plot.pcolormesh(nh_cube, cmap=this_cmap, norm=norm, axes=ax)
# mesh = iris.plot.pcolormesh(trop_cube, cmap=this_cmap, norm=norm, axes=ax)
# mesh = iris.plot.pcolormesh(sh_cube, cmap=this_cmap, norm=norm, axes=ax)
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(b) Africa",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# Canada
ax = plt.axes(axes[2], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
#ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5)
ax.set_extent([-140, -55, 42, 82], cartopy.crs.PlateCarree())
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(c) Canada",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# Tibet
ax = plt.axes(axes[3], projection=cartopy.crs.PlateCarree())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines(resolution="50m")
ax.add_feature(cartopy.feature.BORDERS.with_scale('50m'), linewidth=.5)
ax.set_extent([78, 102, 28, 39], cartopy.crs.PlateCarree())
mesh = iris.plot.pcolormesh(plot_cube,
cmap=this_cmap,
norm=norm,
axes=ax)
plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10)
ax.text(0.05,
1.05,
"(d) Tibetan Plateau",
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
utils.thicken_panel_border(ax)
# colourbar
cb = plt.colorbar(mesh,
cax=plt.axes(axes[4]),
orientation='horizontal',
ticks=bounds[1:-1],
drawedges=True)
# prettify
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in',
size=0)
cb.set_label(label="Anomalies from 1996-2016 (" + r"$^{\circ}$" + "C)",
fontsize=settings.FONTSIZE)
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
plt.savefig(settings.IMAGELOC +
"LKT_Regions_scatter_map{}".format(settings.OUTFMT))
plt.close()
def run_all_plots():
#************************************************************************
# Timeseries
data = read_binary_ts(DATALOC + "TimeSeries_faparanomaliesglobal_bams_v{}_C6_2020.bin".format(int(settings.YEAR)-1))
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.13, 0.07, 0.8, 0.86])
COLOURS = settings.COLOURS["land_surface"]
for dataset in data:
print(dataset.name)
ls = "--"
if dataset.name.split(" ")[-1] == "Smoothed":
ls = "-"
ax.plot(dataset.times, dataset.data, c=COLOURS[dataset.name], ls=ls, label=dataset.name, lw=LW)
ax.axhline(0, c='0.5', ls='--')
utils.thicken_panel_border(ax)
ax.legend(loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
#*******************
# prettify
fig.text(0.01, 0.5, "Anomaly (FAPAR)", va='center', rotation='vertical', fontsize=settings.FONTSIZE)
plt.xlim([1998-1, int(settings.YEAR)+2])
plt.ylim([-0.022, None])
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "FPR_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Hovmullers
data = read_binary(DATALOC + "Hovmuller_Global_lat_fapar1998_2010_bams_trois_C6.eps_{}.bin".format(int(settings.YEAR)+1))
# reshape - from Readme
data = data.reshape(360, DURATION)
data = np.ma.masked_where(data == 0., data)
lats = np.arange(-90, 90, 0.5)
bounds = [-20, -0.04, -0.03, -0.02, -0.01, 0, 0.01, 0.02, 0.03, 0.04, 20]
utils.plot_hovmuller(settings.IMAGELOC + "FPR_hovmuller", times, lats, data, settings.COLOURMAP_DICT["phenological"], bounds, "Anomaly (FAPAR)")
#************************************************************************
# Anomalies
data = read_binary(DATALOC + "DataXFigure1fapar1998_2010_bams_trois_C6_v2.eps_v2020.bin")
data = data.reshape(360, 720)
data = np.ma.masked_where(data == -100, data) # land/ocean mask
data = np.ma.masked_where(data <= -9999., data) # missing data
data = np.ma.masked_where(np.logical_and(data > -0.001, data < 0.001), data) # missing data
lons = np.arange(-180, 180, 0.5)
lats = np.arange(-90, 90, 0.5)
cube = utils.make_iris_cube_2d(data, lats, lons, "FAPAR", "%")
bounds = [-20, -0.04, -0.03, -0.02, -0.01, 0, 0.01, 0.02, 0.03, 0.04, 20]
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_FPR_{}".format(settings.YEAR), cube, settings.COLOURMAP_DICT["phenological"], bounds, "Anomalies from 1998-{} (FAPAR)".format(2010), figtext="(ae) Fraction of Absorbed Photosynthetically Active Radiation")
utils.plot_smooth_map_iris(settings.IMAGELOC + "FPR_{}".format(settings.YEAR), cube, settings.COLOURMAP_DICT["phenological"], bounds, "Anomalies from 1998-{} (FAPAR)".format(2010))
return # run_all_plots
def run_all_plots():
#*********************************************
# Timeseries plot
if False:
(hadisdhLQ, hadcruhLQ, hadcruhextLQ, daiLQ, eraiLQ, era5LQ, merraLQ, jraLQ, era5_mskLQ, merra_mskLQ, cr20LQ) = \
read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "q", "L")
(hadisdhMQ, hadcruhMQ, daiMQ, nocsMQ, hoapsMQ, eraiMQ, era5MQ, merraMQ, jraMQ, cr20MQ) = \
read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "q", "M")
(hadisdhLR, hadcruhLR, hadcruhextLR, daiLR, eraiLR, era5LR, merraLR, jraLR, era5_mskLR, merra_mskLR, cr20LR) = \
read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "rh", "L")
(hadisdhMR, hadcruhMR, daiMR, nocsMR, hoapsMR, eraiMR, era5MR, merraMR, jraMR, cr20MR) = \
read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "rh", "M")
COLOURS = settings.COLOURS["hydrological"]
fig = plt.figure(figsize=(14, 12))
# manually set up the 8 axes
w = 0.45 # width
h = 0.24 # height
c = 0.53 # centre line of plots
ax1 = plt.axes([c-w, 0.99-h, w, h])
ax2 = plt.axes([c, 0.99-h, w, h])
ax3 = plt.axes([c-w, 0.99-(2*h), w, h], sharex=ax1)
ax4 = plt.axes([c, 0.99-(2*h), w, h], sharex=ax2)
ax5 = plt.axes([c-w, 0.99-(3*h), w, h], sharex=ax1)
ax6 = plt.axes([c, 0.99-(3*h), w, h], sharex=ax2)
ax7 = plt.axes([c-w, 0.99-(4*h), w, h], sharex=ax1)
ax8 = plt.axes([c, 0.99-(4*h), w, h], sharex=ax2)
if int(settings.YEAR) < 2019:
# in situ
utils.plot_ts_panel(ax1, [hadisdhLQ, hadcruhLQ, hadcruhextLQ, daiLQ, era5_mskLQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax3, [hadisdhMQ, hadcruhMQ, daiMQ, nocsMQ, hoapsMQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax5, [hadisdhLR, hadcruhLR, hadcruhextLR, daiLR, era5_mskLR], "-", "hydrological", loc="")
utils.plot_ts_panel(ax7, [hadisdhMR, hadcruhMR, daiMR], "-", "hydrological", loc="")
# reanalyses
utils.plot_ts_panel(ax2, [eraiLQ, era5LQ, merraLQ, jraLQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax4, [eraiMQ, era5MQ, merraMQ, jraMQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax6, [eraiLR, era5LR, jraLR], "-", "hydrological", loc="")
utils.plot_ts_panel(ax8, [eraiMR, era5MR, jraMR], "-", "hydrological", loc="")
else:
# in situ
utils.plot_ts_panel(ax1, [hadisdhLQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax3, [hadisdhMQ, nocsMQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax5, [hadisdhLR], "-", "hydrological", loc="")
utils.plot_ts_panel(ax7, [hadisdhMR], "-", "hydrological", loc="")
# hadisdh uncertainties in due course
# reanalyses
utils.plot_ts_panel(ax2, [era5LQ, merraLQ, jraLQ, cr20LQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax4, [era5MQ, merraMQ, jraMQ, cr20MQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax6, [era5LR, jraLR, cr20LR], "-", "hydrological", loc="")
utils.plot_ts_panel(ax8, [era5MR, jraMR, cr20MR], "-", "hydrological", loc="")
# prettify
ax1.set_xlim([1957, int(settings.YEAR)+2])
ax2.set_xlim([1957, int(settings.YEAR)+2])
ax1.set_xticklabels(["", "1960", "1970", "1980", "1990", "2000", "2010", ""])
for ax in [ax1, ax2, ax3, ax4]:
ax.set_ylim([-0.39, 0.8])
for ax in [ax5, ax6, ax7, ax8]:
ax.set_ylim([-1.8, 1.5])
for ax in [ax1, ax3, ax5, ax7]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax7.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax8.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.setp([a.get_xticklabels() for a in fig.axes[:-2]], visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.setp(ax4.get_yticklabels(), visible=False)
plt.setp(ax6.get_yticklabels(), visible=False)
plt.setp(ax8.get_yticklabels(), visible=False)
ax1.text(0.02, 0.85, "(a) In Situ Land q", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
ax2.text(0.02, 0.85, "(b) Reanalyses Land q", transform=ax2.transAxes, fontsize=settings.FONTSIZE)
ax3.text(0.02, 0.85, "(c) In Situ Ocean q", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
ax4.text(0.02, 0.85, "(d) Reanalyses Ocean q", transform=ax4.transAxes, fontsize=settings.FONTSIZE)
ax5.text(0.02, 0.85, "(e) In Situ Land RH", transform=ax5.transAxes, fontsize=settings.FONTSIZE)
ax6.text(0.02, 0.85, "(f) Reanalyses Land RH", transform=ax6.transAxes, fontsize=settings.FONTSIZE)
ax7.text(0.02, 0.85, "(g) In Situ Ocean RH", transform=ax7.transAxes, fontsize=settings.FONTSIZE)
ax8.text(0.02, 0.85, "(h) Reanalyses Ocean RH", transform=ax8.transAxes, fontsize=settings.FONTSIZE)
plt.figtext(0.01, 0.75, "Specific Humidity (g kg"+r'$^{-1}$'+")", va='center', rotation='vertical', fontsize=settings.FONTSIZE)
plt.figtext(0.01, 0.25, "Relative Humidity (%rh)", va='center', rotation='vertical', fontsize=settings.FONTSIZE)
fig.subplots_adjust(right=0.98, top=0.95, bottom=0.05, hspace=0.001)
plt.savefig(settings.IMAGELOC + "HUM_ts{}".format(settings.OUTFMT))
plt.close()
#*********************************************
# Timeseries uncertainty plot
if True:
(hadisdhLQ, hadisdhLQ_l, hadisdhLQ_u, era5LQ, merraLQ, jraLQ, era5_mskLQ, merra_mskLQ, cr20LQ) = \
read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "q", "L")
(hadisdhMQ, hadisdhMQ_l, hadisdhMQ_u, nocsMQ, era5MQ, merraMQ, jraMQ, cr20MQ) = \
read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "q", "M")
(hadisdhLR, hadisdhLR_l, hadisdhLR_u, era5LR, merraLR, jraLR, era5_mskLR, merra_mskLR, cr20LR) = \
read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "rh", "L")
(hadisdhMR, hadisdhMR_l, hadisdhMR_u, nocsMR, era5MR, merraMR, jraMR, cr20MR) = \
read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "rh", "M")
COLOURS = settings.COLOURS["hydrological"]
fig = plt.figure(figsize=(14, 12))
# manually set up the 8 axes
w = 0.45 # width
h = 0.24 # height
c = 0.53 # centre line of plots
ax1 = plt.axes([c-w, 0.99-h, w, h])
ax2 = plt.axes([c, 0.99-h, w, h])
ax3 = plt.axes([c-w, 0.99-(2*h), w, h], sharex=ax1)
ax4 = plt.axes([c, 0.99-(2*h), w, h], sharex=ax2)
ax5 = plt.axes([c-w, 0.99-(3*h), w, h], sharex=ax1)
ax6 = plt.axes([c, 0.99-(3*h), w, h], sharex=ax2)
ax7 = plt.axes([c-w, 0.99-(4*h), w, h], sharex=ax1)
ax8 = plt.axes([c, 0.99-(4*h), w, h], sharex=ax2)
# in situ
utils.plot_ts_panel(ax1, [hadisdhLQ], "-", "hydrological", loc="",)
ax1.fill_between(hadisdhLQ.times, hadisdhLQ_u.data, hadisdhLQ_l.data, color='0.8', label="")
utils.plot_ts_panel(ax3, [hadisdhMQ, nocsMQ], "-", "hydrological", loc="")
ax3.fill_between(hadisdhMQ.times, hadisdhMQ_u.data, hadisdhMQ_l.data, color='0.8', label="")
utils.plot_ts_panel(ax5, [hadisdhLR], "-", "hydrological", loc="")
ax5.fill_between(hadisdhLR.times, hadisdhLR_u.data, hadisdhLR_l.data, color='0.8', label="")
utils.plot_ts_panel(ax7, [hadisdhMR], "-", "hydrological", loc="")
ax7.fill_between(hadisdhMR.times, hadisdhMR_u.data, hadisdhMR_l.data, color='0.8', label="")
# reanalyses
utils.plot_ts_panel(ax2, [era5LQ, merraLQ, jraLQ, cr20LQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax4, [era5MQ, merraMQ, jraMQ, cr20MQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX)
utils.plot_ts_panel(ax6, [era5LR, jraLR, cr20LR], "-", "hydrological", loc="")
utils.plot_ts_panel(ax8, [era5MR, jraMR, cr20MR], "-", "hydrological", loc="")
# fix legend
unc_patch = ax1.fill(np.NaN, np.NaN, '0.8', zorder = 1, alpha=0.7)
for ax in [ax1, ax3]:
lines = []
labels = []
for line in ax.get_lines():
if line.get_label() == "HadISDH":
lines += [(line, unc_patch[0])]
labels += [line.get_label()]
else:
lines += [line]
labels += [line.get_label()]
ax.legend(lines, labels, \
loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
# prettify
ax1.set_xlim([1957, int(settings.YEAR)+2])
ax2.set_xlim([1957, int(settings.YEAR)+2])
ax1.set_xticklabels(["", "1960", "1970", "1980", "1990", "2000", "2010", ""])
for ax in [ax1, ax2, ax3, ax4]:
ax.set_ylim([-0.39, 0.8])
for ax in [ax5, ax6, ax7, ax8]:
ax.set_ylim([-1.8, 1.5])
for ax in [ax1, ax3, ax5, ax7]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax7.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax8.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.setp([a.get_xticklabels() for a in fig.axes[:-2]], visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.setp(ax4.get_yticklabels(), visible=False)
plt.setp(ax6.get_yticklabels(), visible=False)
plt.setp(ax8.get_yticklabels(), visible=False)
ax1.text(0.02, 0.85, "(a) In Situ Land q", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
ax2.text(0.02, 0.85, "(b) Reanalyses Land q", transform=ax2.transAxes, fontsize=settings.FONTSIZE)
ax3.text(0.02, 0.85, "(c) In Situ Ocean q", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
ax4.text(0.02, 0.85, "(d) Reanalyses Ocean q", transform=ax4.transAxes, fontsize=settings.FONTSIZE)
ax5.text(0.02, 0.85, "(e) In Situ Land RH", transform=ax5.transAxes, fontsize=settings.FONTSIZE)
ax6.text(0.02, 0.85, "(f) Reanalyses Land RH", transform=ax6.transAxes, fontsize=settings.FONTSIZE)
ax7.text(0.02, 0.85, "(g) In Situ Ocean RH", transform=ax7.transAxes, fontsize=settings.FONTSIZE)
ax8.text(0.02, 0.85, "(h) Reanalyses Ocean RH", transform=ax8.transAxes, fontsize=settings.FONTSIZE)
plt.figtext(0.01, 0.75, "Specific Humidity (g kg"+r'$^{-1}$'+")", va='center', rotation='vertical', fontsize=settings.FONTSIZE)
plt.figtext(0.01, 0.25, "Relative Humidity (%rh)", va='center', rotation='vertical', fontsize=settings.FONTSIZE)
fig.subplots_adjust(right=0.98, top=0.95, bottom=0.05, hspace=0.001)
plt.savefig(settings.IMAGELOC + "HUM_ts_unc{}".format(settings.OUTFMT))
plt.close()
input("stOP")
#*********************************************
# Map plots
## RH
bounds = [-50, -12, -9, -6, -3, 0, 3, 6, 9, 12, 50]
# RH HadISDH
if False:
cube = read_maps(DATALOC + "HUMrh_anomalymap_HADISDHland{}.txt".format(settings.YEAR), "HadISDH RH", None, footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_hadisdh_land", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="")
# RH HadISDH Land and Marine
if True:
cube = read_maps(DATALOC + "HUMrh_anomalymap_HADISDH{}.txt".format(settings.YEAR), "HadISDH RH", None, footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_RH_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (%rh)", figtext="(h) Surface Relative Humidity", title="")
# RH ERA
if True:
cube = read_maps(DATALOC + "HUMrh_anomalymap_ERA5{}.txt".format(settings.YEAR), "ERA-I RH", None, footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_era5", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="")
# utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_RH_era5", cube, settings.COLOURMAP_DICT["hydrological"], \
# bounds, "Anomalies from 1981-2010 (%rh)", figtext="(o) Surface Relative Humidity", title="")
# RH MERRA
if True:
cube = read_maps(DATALOC + "HUMrh_anomalymap_MERRA2{}.txt".format(settings.YEAR), "MERRA2 RH", None, footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_merra", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="")
## Q
bounds = [-20., -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 20]
# q HadISDH
if False:
cube = read_maps(DATALOC + "HUMq_anomalymap_HADISDHland{}.txt".format(settings.YEAR), "HadISDH q", "g/kg", footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_hadisdh_land", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="")
# utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_q_hadisdh_land", cube, settings.COLOURMAP_DICT["hydrological"], \
# bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="(n) Surface Specific Humidity", title="")
# q HadISDH Land and Marine
if True:
cube = read_maps(DATALOC + "HUMq_anomalymap_HADISDH{}.txt".format(settings.YEAR), "HadISDH q", "g/kg", footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_q_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="(g) Surface Specific Humidity", title="")
# q ERA
if True:
cube = read_maps(DATALOC + "HUMq_anomalymap_ERA5{}.txt".format(settings.YEAR), "ERA-I q", "g/kg", footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_era5", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="")
# MERRA?
if True:
cube = read_maps(DATALOC + "HUMq_anomalymap_MERRA2{}.txt".format(settings.YEAR), "MERRA2 q", "g/kg", footer=True)
utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_merra", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="")
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries - 2016
if False:
grasp, erai, era_presat, merra, jra55 = read_uaw_ts(DATALOC +
"20N-40N300.nc",
smooth=True)
qbo = read_QBO(DATALOC + "qbo_1908_2015_REC_ERA40_ERAINT.txt")
fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5,
figsize=(8, 12),
sharex=True)
# Observations
utils.plot_ts_panel(ax1, [grasp],
"-",
"circulation",
loc=LEGEND_LOC,
ncol=2,
extra_labels=[" (0.02)"])
# Reanalyses
utils.plot_ts_panel(
ax2, [erai, era_presat, jra55, merra],
"-",
"circulation",
loc=LEGEND_LOC,
ncol=2,
extra_labels=[" (-0.20)", " (0.33)", " (-0.13)", " (-0.07)"])
grasp, erai, era_presat, merra, jra55 = read_uaw_ts(DATALOC +
"10S-10N50.nc",
smooth=False)
# Observations
utils.plot_ts_panel(ax3, [qbo, grasp],
"-",
"circulation",
loc=LEGEND_LOC,
ncol=2,
extra_labels=["", " (-0.31)"])
ax3.set_ylabel("Zonal Anomaly (m s" + r'$^{-1}$' + ")",
fontsize=settings.FONTSIZE)
# Reanalyses
utils.plot_ts_panel(
ax4, [erai, era_presat, jra55, merra],
"-",
"circulation",
loc=LEGEND_LOC,
ncol=2,
extra_labels=[" (-0.33)", " (-0.16)", " (-0.37)", " (0.30)"])
fig.subplots_adjust(left=0.11, right=0.99, top=0.99, hspace=0.001)
# turn on 4th axis ticks
for tick in ax4.get_xticklabels():
tick.set_visible(True)
# delete the 5th axis and recreate - to break the sharex link
fig.delaxes(ax5)
ax5 = fig.add_subplot(515)
pos = ax5.get_position()
new_pos = [pos.x0, pos.y0 - 0.05, pos.width, pos.height]
ax5.set_position(new_pos)
# Obs & Reanalyses
utils.plot_ts_panel(ax5, [grasp, erai, jra55, merra],
"-",
"circulation",
loc=LEGEND_LOC,
ncol=2)
# sort formatting
for ax in [ax4, ax5]:
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax2, ax3, ax4, ax5]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# x + y limit
ax1.set_xlim([1930, 2017.9])
ax1.set_ylim([-13, 6])
ax1.yaxis.set_ticks([-10, -5, 0])
ax2.set_ylim([-3.8, 3.8])
ax2.yaxis.set_ticks([-2, 0, 2, 4])
ax3.set_ylim([-34, 24])
ax4.set_ylim([-34, 24])
ax5.set_ylim([-34, 24])
ax5.set_xlim([2000, 2017.9])
# sort labelling
ax1.text(0.02,
0.87,
"(a) Observations 20" + r'$^\circ$' + " - 40" + r'$^\circ$' +
"N 300hPa",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.87,
"(b) Reanalyses 20" + r'$^\circ$' + " - 40" + r'$^\circ$' +
"N 300hPa",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02,
0.87,
"(c) Observations & Reconstructions 10" + r'$^\circ$' +
"S - 10" + r'$^\circ$' + "N 50hPa",
transform=ax3.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax4.text(0.02,
0.87,
"(d) Reanalyses 10" + r'$^\circ$' + "S - 10" + r'$^\circ$' +
"N 50hPa",
transform=ax4.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax5.text(0.02,
0.87,
"(e) Observations & Reanalyses 10" + r'$^\circ$' + "S - 10" +
r'$^\circ$' + "N 50hPa",
transform=ax5.transAxes,
fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC + "UAW_ts{}".format(settings.OUTFMT))
#************************************************************************
# Timeseries - 2018
if True:
plt.figure(figsize=(8, 5))
plt.clf()
ax = plt.axes([0.12, 0.10, 0.87, 0.87])
# Globe
# grasp, erai, cera, merra, jra55 = read_uaw_ts(DATALOC + "Globe850.nc", annual=True)
era5, erai, merra, jra55 = read_uaw_ts(DATALOC + "Globe850_v2.nc",
annual=True)
utils.plot_ts_panel(ax, [merra, erai, era5, jra55], "-", "circulation", \
loc=LEGEND_LOC, ncol=2, extra_labels=[" (0.03)", " (0.07)", \
" (0.03)", " (0.06)"])
# sort formatting
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# x + y limit
ax.set_xlim([1958, int(settings.YEAR) + 0.9])
ax.set_ylim([-0.39, 1.0])
ax.yaxis.set_ticks_position('left')
ax.set_ylabel("Wind Anomaly (m s" + r'$^{-1}$' + ")",
fontsize=settings.LABEL_FONTSIZE)
# # sort labelling
ax.text(0.02,
0.87,
"Globe 850hPa",
transform=ax.transAxes,
fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC +
"UAW_globe_ts{}".format(settings.OUTFMT))
#*******
# Tropics timeseries
if False:
fig = plt.figure(figsize=(8, 5))
plt.clf()
ax = plt.axes([0.10, 0.10, 0.87, 0.87])
# 10N to 10S
grasp, erai, cera, merra, jra55 = read_uaw_ts(DATALOC + "10S-10N50.nc")
utils.plot_ts_panel(ax, [merra, erai, jra55, grasp], "-", "circulation",\
loc=LEGEND_LOC, ncol=2, extra_labels=[" (0.17)", " (-0.40)", \
" (-0.51)", " (-0.30)"])
# sort formatting
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# x + y limit
ax.set_xlim([2000, int(settings.YEAR) + 2])
ax.set_ylim([-28, 18])
ax.yaxis.set_ticks_position('left')
ax.set_ylabel("Wind Anomaly (m s" + r'$^{-1}$' + ")",
fontsize=settings.LABEL_FONTSIZE)
# # sort labelling
ax.text(0.02, 0.87, "10"+r'$^\circ$'+"S - 10"+r'$^\circ$'+"N 50hPa", \
transform=ax.transAxes, fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC +
"UAW_tropics_ts{}".format(settings.OUTFMT))
#************************************************************************
# Global Map - ERA5 Anomaly figure
if True:
# Read in ERA5 anomalies
# IRIS doesn't like the Conventions attribute
ncfile = ncdf.Dataset(DATALOC + "ERA5_850_u.nc", 'r')
var = ncfile.variables["u"][:] # this is a masked array
lons = ncfile.variables["longitude"][:]
lats = ncfile.variables["latitude"][:]
ncfile.close()
# monthly data, so take mean
print("not complete year used in 2019")
mean = np.mean(var[7:], axis=0)
cube = utils.make_iris_cube_2d(mean, lats, lons, "UAW_ANOM", "m/s")
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_UAW_{}_anoms_era5".format(settings.YEAR), \
cube, settings.COLOURMAP_DICT["circulation"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")", \
figtext="(w) Upper Air (850-hPa) Eastward Winds (ASOND)")
utils.plot_smooth_map_iris(settings.IMAGELOC + "UAW_{}_anoms_era5".format(settings.YEAR), \
cube, settings.COLOURMAP_DICT["circulation"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")")
#************************************************************************
# QBO plot - https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html
if False:
levels = np.array([70., 50., 40., 30., 20., 15., 10.])
times = []
dttimes = []
data = np.zeros((levels.shape[0], 13))
factor = 0.1
j = 0
with open(DATALOC + "qbo.dat", "r") as infile:
for line in infile:
line = line.split()
if len(line) > 0:
# get current year
try:
if int(line[1][:2]) >= int(settings.YEAR[-2:]) and int(
line[1][:2]) <= int(settings.YEAR[-2:]) + 1:
month = int(line[1][-2:])
times += [month]
dttimes += [
dt.datetime(int(settings.YEAR), month, 1)
]
data[:, j] = [float(i) * factor for i in line[2:]]
j += 1
except ValueError:
pass
data = np.array(data)
times = np.array(times)
times[-1] += 12
# And now plot
cmap = settings.COLOURMAP_DICT["circulation"]
bounds = [
-100., -45., -30., -15., -10., -5., 0., 5., 10., 15., 30., 45., 100
]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
fig = plt.figure(figsize=(8, 8))
plt.clf()
ax = plt.axes([0.12, 0.07, 0.8, 0.9])
times, levels = np.meshgrid(times, levels)
con = plt.contourf(times,
levels,
data,
bounds,
cmap=cmap,
norm=norm,
vmax=bounds[-1],
vmin=bounds[1])
plt.ylabel("Pressure (hPa)", fontsize=settings.FONTSIZE)
plt.xlabel(settings.YEAR, fontsize=settings.FONTSIZE)
plt.xticks(times[0], [dt.datetime.strftime(d, "%b") for d in dttimes],
fontsize=settings.FONTSIZE * 0.8)
plt.xlim([1, 13])
plt.ylim([70, 10])
ax.set_yscale("log", subsy=[])
plt.gca().yaxis.set_major_locator(
matplotlib.ticker.MultipleLocator(10))
plt.gca().yaxis.set_minor_locator(matplotlib.ticker.NullLocator())
plt.gca().yaxis.set_major_formatter(
matplotlib.ticker.ScalarFormatter())
plt.yticks(np.arange(70, 0, -10),
["{}".format(l) for l in np.arange(70, 0, -10)],
fontsize=settings.FONTSIZE)
# colourbar and prettify
cb = plt.colorbar(con, orientation='horizontal', pad=0.1, fraction=0.05, aspect=30, \
ticks=bounds[1:-1], label="zonal wind (m/s)", drawedges=True)
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE * 0.6,
direction='in')
cb.set_label(label="zonal wind (m/s)",
fontsize=settings.FONTSIZE * 0.6)
# cb.outline.set_color('k')
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
utils.thicken_panel_border(ax)
plt.savefig(settings.IMAGELOC +
"UAW_QBO_levels{}".format(settings.OUTFMT))
#************************************************************************
# https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/singapore2019.dat
if True:
levels = []
times = np.arange(1, 13, 1)
data = []
factor = 0.1
j = 0
with open(DATALOC + "singapore{}.dat".format(settings.YEAR),
"r") as infile:
read = False
for line in infile:
line = line.split()
if len(line) == 0:
continue
if line[0] == "hPa":
read = True
continue
elif read:
if len(line) > 0:
levels += [int(line[0])]
data += [[float(l) * 0.1 for l in line[1:]]]
else:
continue
# convert ot arrays and reorder
levels = np.array(levels)
levels = levels[::-1]
data = np.array(data)
data = data[::-1, :]
# And now plot
cmap = settings.COLOURMAP_DICT["circulation"]
bounds = [
-100., -45., -30., -15., -10., -5., 0., 5., 10., 15., 30., 45., 100
]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
fig = plt.figure(figsize=(8, 8))
plt.clf()
ax = plt.axes([0.12, 0.07, 0.85, 0.9])
times, levels = np.meshgrid(times, levels)
con = plt.contourf(times,
levels,
data,
bounds,
cmap=cmap,
norm=norm,
vmax=bounds[-1],
vmin=bounds[1])
plt.ylabel("Pressure (hPa)", fontsize=settings.FONTSIZE)
plt.xlabel(settings.YEAR, fontsize=settings.FONTSIZE)
dttimes = [
dt.datetime(int(settings.YEAR), m + 1, 1) for m in range(12)
]
plt.xticks(times[0], [dt.datetime.strftime(d, "%b") for d in dttimes],
fontsize=settings.FONTSIZE)
plt.xlim([1, 12])
plt.ylim([100, 10])
ax.set_yscale("log", subsy=[])
plt.gca().yaxis.set_major_locator(
matplotlib.ticker.MultipleLocator(10))
plt.gca().yaxis.set_minor_locator(matplotlib.ticker.NullLocator())
plt.gca().yaxis.set_major_formatter(
matplotlib.ticker.ScalarFormatter())
plt.yticks(np.arange(100, 0, -10),
["{}".format(l) for l in np.arange(100, 0, -10)],
fontsize=settings.FONTSIZE)
# colourbar and prettify
cb = plt.colorbar(con, orientation='horizontal', pad=0.1, fraction=0.05, aspect=30, \
ticks=bounds[1:-1], drawedges=True)
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.set_label(label="zonal wind (m/s)", fontsize=settings.FONTSIZE)
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in')
cb.set_label(label="zonal wind (m/s)", fontsize=settings.FONTSIZE)
# cb.outline.set_color('k')
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
utils.thicken_panel_border(ax)
plt.savefig(settings.IMAGELOC + "UAW_levels{}".format(settings.OUTFMT))
#************************************************************************
# 200hPa winds in 1980 and 2018
if False:
cube_list = iris.load(DATALOC + "ws200_spread_197901_201801.nc")
names = np.array([str(cube.var_name) for cube in cube_list])
# hard coded labels
mu = {"1980": "1.8", "2018": "1.0"}
rms = {"1980": "2.0", "2018": "1.1"}
label = {"1980": "(a)", "2018": "(b)"}
bounds = [0, 0.5, 1, 1.5, 2, 2.5, 3.0, 100]
for name in names:
print(name)
cube_index, = np.where(names == name)
cube = cube_list[cube_index[0]]
year = name.split("_")[-1][:4]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "UAW_200hPa_Jan{}".format(year),
cube,
plt.cm.BuPu,
bounds,
"m/s",
figtext="{} January {}, mean={}, RMS={}".format(
label[year], year, mu[year], rms[year]))
#************************************************************************
# Plots
if False:
label = {"1980": "(c)", "2018": "(d)"}
for year in ["1980", "2018"]:
cube_list = iris.load(DATALOC + "v200_zonal_{}01.nc".format(year))
for cube in cube_list:
if cube.var_name == "products":
names = cube
elif cube.var_name == "v200_array":
data_array = cube
latitudes = data_array.coord("latitude").points
plt.figure()
ax = plt.axes([0.13, 0.13, 0.85, 0.85])
COLOURS = {
"ERA5 ens. mean": "red",
"ERA5 ensemble": "orange",
"ERA5 HRES": "orange",
"JRA55": "c",
"MERRA-2": "m",
"ERA-Interim": "orange"
}
for name, data in zip(names.data, data_array.data):
str_name = "".join(str(name.compressed(), "latin-1").rstrip())
# manually fix names
if str_name == "ERAI":
str_name = "ERA-Interim"
elif str_name == "ERA5 ens mean":
str_name = "ERA5 ens. mean"
if str_name[:3] == "mem":
plt.plot(latitudes[1:], data[1:], c="orange")
elif str_name == "ERA-Interim":
plt.plot(latitudes[1:],
data[1:],
c=COLOURS[str_name],
label=str_name,
lw=2,
ls="--")
else:
plt.plot(latitudes[1:],
data[1:],
c=COLOURS[str_name],
label=str_name,
lw=2)
plt.legend(loc="upper right", ncol=1, frameon=False)
plt.xlabel("Latitude", fontsize=settings.FONTSIZE * 0.8)
plt.ylabel("m/s", fontsize=settings.FONTSIZE * 0.8)
plt.text(0.03,
0.92,
"{} January {}".format(label[year], year),
transform=ax.transAxes,
fontsize=settings.FONTSIZE * 0.8)
plt.xlim([-90, 90])
plt.xticks(np.arange(-90, 120, 30))
plt.ylim([-1, 4])
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE * 0.8)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE * 0.8)
utils.thicken_panel_border(ax)
plt.savefig(settings.IMAGELOC +
"UAW_200hPa_Jan{}_ts{}".format(year, settings.OUTFMT))
return
def run_all_plots():
# # CEI timeseries
if True:
# from https://www.ncdc.noaa.gov/extremes/cei/graph/us/03-05/4 (Spring, Step4 indicator)
cei = read_cei("CEI_step4_figure_data.csv")
smoothed = binomialfilter(cei.data, -99.9, 9, pad=False)
smoothed = np.ma.masked_where(smoothed == -99.9, smoothed)
fig = plt.figure(figsize=(8, 5))
plt.clf()
ax1 = plt.axes([0.11, 0.08, 0.86, 0.90])
ax1.bar(cei.times,
cei.data,
color="g",
label=cei.name,
align="center",
width=1,
edgecolor="darkgreen")
ax1.plot(cei.times, smoothed, "r", lw=LW)
ax1.plot([cei.times[0], cei.times[-1]],
[np.mean(cei.data), np.mean(cei.data)],
"k",
lw=1)
ax1.set_xlim([1910, int(settings.YEAR) + 2])
ax1.set_ylabel("%", fontsize=settings.FONTSIZE)
minorLocator = MultipleLocator(1)
ax1.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax1)
ax1.yaxis.set_tick_params(right=False)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# ax1.text(0.02, 0.9, "(e)", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "PEX_CEI_ts{}".format(settings.OUTFMT))
plt.close()
#*************************
# GHCNDEX indices
rank_bounds = [-4.5, -3.5, -2.5, -1.5, 1.5, 2.5, 3.5, 4.5]
if True:
for index in ETCCDI_INDICES:
cube_list = iris.load(
DATALOC +
"GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc".
format(index,
int(settings.YEAR) + 1))
names = np.array([cube.name() for cube in cube_list])
#*************
# plot annual map
selected_cube, = np.where(names == "Ann")
total_cube = cube_list[selected_cube[0]]
total_cube.coord('latitude').guess_bounds()
total_cube.coord('longitude').guess_bounds()
anoms = copy.deepcopy(total_cube)
anoms = ApplyClimatology(anoms)
# select the year to plot
years = GetYears(total_cube)
loc, = np.where(years == SELECTED_YEAR)
# sort the bounds and colourbars
if index in ["Rx1day"]:
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["Rx5day"]:
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R10mm"]:
bounds = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R20mm"]:
bounds = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R95p"]:
bounds = [0, 25, 50, 75, 100, 125, 150, 175, 200, 225]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["PRCPTOT"]:
bounds = [0, 25, 50, 75, 100, 125, 150, 200, 300, 400]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_ghcndex".format(index, settings.YEAR),
total_cube[loc[0]],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="GHCNDEX {} - {}".format(index, ETCCDI_LABELS[index]))
# sort the bounds and colourbars
if index in ["Rx1day"]:
bounds = [-100, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 100]
bounds = [
-100, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 100
]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["Rx5day"]:
bounds = [
-100, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 100
]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["R10mm"]:
bounds = [-20, -8, -6, -4, -2, 0, 2, 4, 6, 8, 20]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["R20mm"]:
bounds = [-20, -4, -3, -2, -1, 0, 1, 2, 3, 4, 20]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["R95p"]:
bounds = [
-1000, -100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100,
1000
]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["PRCPTOT"]:
bounds = [
-1000, -150, -80, -60, -40, -20, 0, 20, 40, 60, 80, 150,
1000
]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_anoms_ghcndex".format(index, settings.YEAR),
anoms[loc[0]],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="GHCNDEX {} - {}".format(index, ETCCDI_LABELS[index]))
if index == "Rx1day":
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_PEX_{}_{}_anoms_ghcndex".format(
index, settings.YEAR),
anoms[loc[0]],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
figtext="(k) Maximum 1 Day Precipitation Amount")
rank_cube = get_ranks(anoms)
plot_rank_map(
settings.IMAGELOC +
"PEX_{}_{}_rank_ghcndex".format(index, settings.YEAR),
rank_cube[loc[0]],
cmap,
rank_bounds,
"Rank",
title="{} - {}".format(index, ETCCDI_UNITS[index]))
#*************
# plot season maps (2x2)
if index in ["Rx1day", "Rx5day"]:
for sc, cube in enumerate([total_cube, anoms]):
season_list = []
for season in SEASONS:
# extract each month
month_data = []
months = SEASON_DICT[season]
for month in months:
selected_cube, = np.where(names == month)
cube = cube_list[selected_cube[0]]
if month == "Dec":
# need to extract from previous year - cheat by rolling data around
cube.data = np.roll(cube.data, 1, axis=0)
cube.data.mask[
0, :, :] = True # and mask out the previous years'
month_data += [cube.data]
# finished getting all months, make a dummy cube to populate
month_data = np.ma.array(month_data)
season_cube = copy.deepcopy(cube)
# take appropriate seasonal value
season_cube.data = np.ma.max(month_data, axis=0)
# mask if fewer that 2 months present
nmonths_locs = np.ma.count(month_data, axis=0)
season_cube.data = np.ma.masked_where(
nmonths_locs < 2, season_cube.data)
# make anomalies
if sc == 1:
season_cube = ApplyClimatology(season_cube)
# fix for plotting
season_cube.coord('latitude').guess_bounds()
season_cube.coord('longitude').guess_bounds()
# select the year to plot
years = GetYears(cube)
loc, = np.where(years == SELECTED_YEAR)
# add to list
season_list += [season_cube[loc[0]]]
# sort the bounds and colourbars
if sc == 0:
if index in ["Rx1day", "Rx5day"]:
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
# pass to plotting routine
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"PEX_{}_{}_seasons_ghcndex".format(
index, settings.YEAR),
season_list,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
shape=(2, 2),
title=SEASONS,
figtext=["(a)", "(b)", "(c)", "(d)"],
figtitle="{} - {}".format(index,
ETCCDI_LABELS[index]))
elif sc == 1:
cmap = settings.COLOURMAP_DICT["hydrological"]
if index in ["Rx1day"]:
bounds = [
-100, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10,
100
]
elif index in ["Rx5day"]:
bounds = [
-100, -20, -16, -12, -8, -4, 0, 4, 8, 12, 16,
20, 100
]
# pass to plotting routine
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"PEX_{}_{}_anoms_seasons_ghcndex".format(
index, settings.YEAR),
season_list,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
shape=(2, 2),
title=SEASONS,
figtext=["(a)", "(b)", "(c)", "(d)"],
figtitle="{} - {}".format(index,
ETCCDI_LABELS[index]))
#*************************
# DWD indices
if True:
for index in DWD_INDICES:
print(index)
if not os.path.exists(
DATALOC +
"First_Guess_Daily_{}_{}.nc".format(settings.YEAR, index)):
print("File {} missing".format(
"First_Guess_Daily_{}_{}.nc".format(settings.YEAR, index)))
continue
cube_list = iris.load(
DATALOC +
"First_Guess_Daily_{}_{}.nc".format(settings.YEAR, index))
if len(cube_list) == 1:
cube = cube_list[0]
else:
# these have two fields
for cube in cube_list:
if cube.units == "mm per 5 day" and index == "RX5":
break
elif cube.var_name == "consecutive_wet_days_index_per_time_period" and index == "CWD":
break
elif cube.var_name == "consecutive_dry_days_index_per_time_period" and index == "CDD":
break
else:
print(
"Check cube for {} for extra fields".format(index))
cube = cube[0] # take only single slice
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
if index in ["CDD"]:
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
cmap = settings.COLOURMAP_DICT["precip_sequential_r"]
elif index in ["CWD"]:
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["DD"]:
bounds = [0, 40, 80, 120, 160, 200, 240, 280, 320, 360]
cmap = settings.COLOURMAP_DICT["precip_sequential_r"]
elif index in ["PD"]:
bounds = [0, 40, 80, 120, 160, 200, 240, 280, 320, 360]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["RX1", "PD10"]:
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["RX5"]:
bounds = [0, 50, 100, 150, 200, 250, 300, 350, 400, 450]
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R10", "R20", "R95P", "SDII", "PD20"]:
bounds = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_dwd".format(index, settings.YEAR),
cube,
cmap,
bounds,
"{} ({})".format(index, DWD_UNITS[index]),
title="{} - {}".format(index, DWD_LABELS[index]))
#*************************
# DWD differences indices
if True:
for index in DWD_INDICES:
print(index)
if not os.path.exists(
DATALOC +
"Diff_{}-Mean_{}.nc".format(index, settings.YEAR)):
print("File {} missing".format("Diff_{}-Mean_{}.nc".format(
index, settings.YEAR)))
continue
cube_list = iris.load(
DATALOC + "Diff_{}-Mean_{}.nc".format(index, settings.YEAR))
if len(cube_list) == 1:
cube = cube_list[0]
else:
# these have two fields
for cube in cube_list:
if cube.units == "mm per 5 day" and index == "RX5":
break
cube = cube[0] # take only single slice
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
if index in ["RX1"]:
bounds = [-100, -50, -25, -10, -5, 0, 5, 10, 25, 50, 100]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["RX5"]:
bounds = [-50, -30, -20, -10, -5, 0, 5, 10, 20, 30, 50]
bounds = [-100, -50, -25, -10, -5, 0, 5, 10, 25, 50, 100]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["PD10", "PD20", "R95P"]:
bounds = [-50, -30, -20, -10, -5, 0, 5, 10, 20, 30, 50]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_diff_dwd".format(index, settings.YEAR),
cube,
cmap,
bounds,
"Anomalies from 1982-2016 {} ({})".format(
index, DWD_UNITS[index]),
title="{} - {}".format(index, DWD_LABELS[index]))
if index == "PD10":
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_PEX_{}_{}_diff_dwd".format("R10mm", settings.YEAR),
cube,
cmap,
bounds,
"Anomalies from 1982-2016 {} ({})".format(
"R10mm", ETCCDI_UNITS["R10mm"]),
figtext="(l) {} anomalies".format("R10mm"))
else:
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_PEX_{}_{}_diff_dwd".format(index, settings.YEAR),
cube,
cmap,
bounds,
"Anomalies from 1982-2016 {} ({})".format(
index, DWD_UNITS[index]),
figtext="(l) {} anomalies".format(index))
#*************************
# MERRA map
if True:
index = "R10mm"
cube = iris.load(DATALOC + "MERRA2_ann_2019_r10mm_gl_anom.nc")[0]
bounds = [-50, -30, -20, -10, -5, 0, 5, 10, 20, 30, 50]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PEX_R10mm_{}_merra2".format(settings.YEAR),
cube[0],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="MERRA-2 {} - {}".format(index, ETCCDI_LABELS[index]))
#*************************
# ERA5 map
if False:
index = "Rx1day"
cube = read_era5("prmax_{}.txt".format(settings.YEAR))
bounds = [0, 2, 5, 10, 20, 40, 80, 160, 300, 450]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PEX_Rx1day_{}_era5".format(settings.YEAR),
cube,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="ERA5 {} - {}".format(index, ETCCDI_LABELS[index]))
#*************************
# ERA5 map
if True:
index = "Rx1day"
cube = read_era5("prmax1d_anomaly_for_{}_wrt_1981-2010.txt".format(
settings.YEAR))
bounds = [-400, -100, -75, -50, -25, 0, 25, 50, 75, 100, 400]
bounds = [-400, -100, -50, -20, -10, 0, 10, 20, 50, 100, 400]
bounds = [-100, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 100]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_Rx1day_{}_anoms_era5".format(settings.YEAR),
cube,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="ERA5 {} - {}".format(index, ETCCDI_LABELS[index]))
# utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_PEX_Rx1day_{}_anoms_era5".format(settings.YEAR), cube, cmap, bounds, "{} ({})".format(index, ETCCDI_UNITS[index]), figtext="(i) Rx1day anomalies")
# cube = read_era5("prmax_for_{}_as_a_percentage_of_1981-2010_mean.txt".format(settings.YEAR))
# bounds = [0, 10, 25, 50, 75, 100, 150, 200, 250, 300, 1000]
# cmap = settings.COLOURMAP_DICT["hydrological"]
# utils.plot_smooth_map_iris(settings.IMAGELOC + "PEX_Rx1day_{}_anoms_percent_era5".format(settings.YEAR), cube, cmap, bounds, "Anomalies from 1981-2010 (%)", title="ERA5 - Rx1day %")
# utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_PEX_Rx1day_{}_anoms_percent_era5".format(settings.YEAR), cube, cmap, bounds, "Anomalies from 1981-2010 (%)", figtext="(m) Rx1day anomalies")
#*************************
# DWD percentile
if True:
# dwd_cube = read_dwd_percentile(DATALOC + "GPCC_perzentile_{}.xyzras".format(settings.YEAR))
cube_list = iris.load(DATALOC + "Quantile_12month_{}01-{}12.nc".format(
settings.YEAR, settings.YEAR))
dwd_cube = cube_list[0][0]
bounds = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PEX_{}_{}_dwd".format("R90", settings.YEAR),
dwd_cube,
cmap,
bounds,
"{} ({})".format("percentile", "%"),
title="Percentile of the annual precipitation total")
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_PEX_{}_{}_dwd".format("R90", settings.YEAR),
dwd_cube,
cmap,
bounds,
"{} ({})".format("percentile", "%"),
figtext="(l) Percentile of the Annual Precipitation Total")
#*************************
# DWD drought
if True:
# dwd_cube = read_dwd_percentile(DATALOC + "GPCC_perzentile_{}.xyzras".format(settings.YEAR))
cube_list = iris.load(DATALOC + "GPCC_DI_201912_12.nc")
dwd_cube = cube_list[0][0]
bounds = [-10, -5, -3, -2, -1, 0, 1, 2, 3, 5, 10]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(settings.IMAGELOC +
"PEX_{}_{}_dwd".format("DI", settings.YEAR),
dwd_cube,
cmap,
bounds,
"{} ({})".format("DI", "-"),
title="12 month Drought Index")
#*************************
# GHCND totals and ratios
if False:
import cartopy.feature as cfeature
NAMES = {"japan": "Japan", "neaus": "Australia", "hawaii": "Hawaii"}
EXTENTS = {
"japan": (132, 33, [126, 139, 30, 36]),
"neaus": (145, -17, [139, 151, -26, -8]),
"hawaii": (-158, 20, [-161, -154, 18.5, 22.5])
}
land_10m = cfeature.NaturalEarthFeature(
'physical',
'land',
'10m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
land_50m = cfeature.NaturalEarthFeature(
'physical',
'land',
'50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
for index in ["Rx5day", "Rx1day"]:
for state in ["03-neaus", "07-japan", "08-hawaii"]:
print("{} - {}".format(state, index))
lats, lons, value_mm, prev_value_mm = read_ghcnd(
"{}/{}-{}-non0-sorted-{}.txt".format(
DATALOC, index, settings.YEAR, state))
ratio = value_mm / prev_value_mm
# set up figure
if "neaus" in state:
fig = plt.figure(figsize=(8, 5))
else:
fig = plt.figure(figsize=(8, 3.5))
if index == "Rx5day":
bounds = [0, 100, 200, 300, 400, 500, 600, 700, 800, 900]
elif index == "Rx1day":
bounds = [0, 50, 100, 150, 200, 250, 300, 350, 400, 450]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
# ratio_cmap = plt.cm.YlOrRd
# ratio_bounds = [1, 1.05, 1.1, 1.15, 1.2, 1.3, 1.5, 1.75, 2.0 ]
# ratio_cmap = settings.COLOURMAP_DICT["hydrological"]
# ratio_bounds = [0.01, 0.5, 0.75, 0.9, 0.95, 1.0, 1.05, 1.1, 1.25, 2.0, 10.0]
ratio_cmap = plt.cm.Blues
ratio_bounds = [
0.01, 0.5, 0.7, 0.9, 1.0, 1.1, 1.3, 1.5, 2.0, 10.0
]
ratio_norm = mpl.cm.colors.BoundaryNorm(
ratio_bounds, ratio_cmap.N)
plt.clf()
# set up axes
ax0 = plt.axes([0.01, 0.12, 0.45, 0.85],
projection=cartopy.crs.Stereographic(
central_longitude=EXTENTS[state[3:]][0],
central_latitude=EXTENTS[state[3:]][1]))
ax1 = plt.axes([0.51, 0.12, 0.45, 0.85],
projection=cartopy.crs.Stereographic(
central_longitude=EXTENTS[state[3:]][0],
central_latitude=EXTENTS[state[3:]][1]))
for ax in [ax0, ax1]:
ax.set_extent(EXTENTS[state[3:]][2],
cartopy.crs.PlateCarree())
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
ax.add_feature(states_provinces, edgecolor='gray')
ax.coastlines(resolution="10m", linewidth=0.5)
ax.add_feature(land_50m,
zorder=0,
facecolor="0.9",
edgecolor="k")
# add other features
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.BORDERS,
zorder=0,
facecolor="0.9",
edgecolor="k")
# plot
for ax, data, cm, bnds, nrm, label in zip(
[ax0, ax1], [value_mm, ratio], [cmap, ratio_cmap],
[bounds, ratio_bounds], [norm, ratio_norm],
["{} (mm)".format(index), "Ratio to previous record"]):
scatter = ax.scatter(lons, lats, c=data, cmap=cm, norm=nrm, s=50, \
transform=cartopy.crs.Geodetic(), edgecolor='0.5', linewidth=0.5)
# thicken border of colorbar and the dividers
# http://stackoverflow.com/questions/14477696/customizing-colorbar-border-color-on-matplotlib
if "Ratio" in label:
cb = fig.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, fraction=0.05, \
aspect=30, ticks=bnds[1:-1], label=label, drawedges=True)
cb.set_ticklabels(
["{:g}".format(b) for b in bnds[1:-1]])
else:
cb = fig.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, fraction=0.05, \
aspect=30, ticks=bnds[1:-1], label=label, drawedges=True)
cb.set_ticklabels(["{:g}".format(b) for b in bnds])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE * 0.6,
direction='in')
if index == "Rx5day" and state[3:] == "hawaii":
ax0.text(0.05,
1.05,
"(a)",
transform=ax0.transAxes,
fontsize=settings.FONTSIZE * 0.8)
ax1.text(0.05,
1.05,
"(b)",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE * 0.8)
elif index == "Rx1day" and state[3:] == "hawaii":
ax0.text(0.05,
1.05,
"(c)",
transform=ax0.transAxes,
fontsize=settings.FONTSIZE * 0.8)
ax1.text(0.05,
1.05,
"(d)",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE * 0.8)
plt.savefig(
settings.IMAGELOC +
"PEX_{}_{}-{}".format(index, NAMES[state[3:]], state[:2]) +
settings.OUTFMT)
plt.close()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Cloudiness timeseries
if True:
plt.clf()
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
infilename = os.path.join(
DATALOC,
"{}_global_cloudiness_timeseries_v2.txt".format(settings.YEAR))
# anomalies
patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \
read_ts(infilename, anomaly=True)
utils.plot_ts_panel(ax1, [
patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2,
patmosdx, cci
],
"-",
"hydrological",
loc="")
ax1.text(0.02,
0.9,
"(a) Satellite - Anomalies",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE)
# actuals
patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \
read_ts(infilename)
utils.plot_ts_panel(ax2, [
patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2,
patmosdx, cci
],
"-",
"hydrological",
loc=LEGEND_LOC,
ncol=3)
ax2.text(0.02,
0.9,
"(b) Satellite - Actual",
transform=ax2.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# prettify
ax1.set_ylabel("Anomaly (%)", fontsize=settings.FONTSIZE)
ax2.set_ylabel("(%)", fontsize=settings.FONTSIZE)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
minorLocator = MultipleLocator(1)
for ax in [ax1, ax2]:
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
fig.subplots_adjust(right=0.96,
top=0.99,
bottom=0.04,
left=0.09,
hspace=0.001)
plt.xlim([hirs.times[0] - 1, hirs.times[-1] + 1])
ax1.set_ylim([-4.4, 6.9])
ax2.set_ylim([0, 95])
plt.savefig(settings.IMAGELOC + "CLD_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# make a version using the full base period of the data and only the pre2000 datasets
if False:
plt.clf()
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
# anomalies
patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \
read_ts(DATALOC + "{}_global_cloudiness_timeseries.txt".format(settings.YEAR), \
anomaly=True, fullbase=True)
utils.plot_ts_panel(ax1, [patmosx, hirs, satcorps, clara_a2, cci],
"-",
"hydrological",
loc="")
ax1.text(0.02,
0.9,
"(a) Satellite - Anomalies",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE)
# actuals
patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \
read_ts(DATALOC + "{}_global_cloudiness_timeseries.txt".format(settings.YEAR))
utils.plot_ts_panel(ax2, [patmosx, hirs, satcorps, clara_a2, cci], "-", "hydrological", \
loc=LEGEND_LOC, ncol=3)
ax2.text(0.02,
0.9,
"(b) Satellite - Actual",
transform=ax2.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# prettify
ax1.set_ylabel("Anomaly (%)", fontsize=settings.FONTSIZE)
ax2.set_ylabel("(%)", fontsize=settings.FONTSIZE)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
minorLocator = MultipleLocator(1)
for ax in [ax1, ax2]:
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
plt.xlim([hirs.times[0] - 1, hirs.times[-1] + 1])
ax1.set_ylim([-4.9, 4.4])
ax2.set_ylim([0, 95])
plt.savefig(settings.IMAGELOC +
"CLD_ts_fullbaseperiod{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Cloudiness map
if True:
mapfile_dict = scipy.io.readsav(
DATALOC +
"patmosx_global_monthly_cloudiness_anomaly_map_{}.sav".format(
settings.YEAR))
annual_anoms = mapfile_dict["annual_anom"] * 100.
djf_anoms = mapfile_dict["djf_anom"] * 100.
jja_anoms = mapfile_dict["jja_anom"] * 100.
mam_anoms = mapfile_dict["mam_anom"] * 100.
son_anoms = mapfile_dict["son_anom"] * 100.
lats = mapfile_dict["lat"]
lons = mapfile_dict["lon"]
cube = utils.make_iris_cube_2d(annual_anoms, lats[:, 0], lons[0],
"CLD_anom", "%")
bounds = [-100, -15, -10, -5, -2.5, 0, 2.5, 5, 10, 15, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "CLD_{}_anoms".format(settings.YEAR), cube, \
settings.COLOURMAP_DICT["hydrological"], bounds, \
"Anomalies from 1981-2010 (%)")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_CLD_{}_anoms".format(settings.YEAR), \
cube, settings.COLOURMAP_DICT["hydrological"], bounds, \
"Anomalies from 1981-2010 (%)", figtext="(n) Cloudiness")
#************************************************************************
# Cloudiness Seasonal Map
if True:
cubelist = []
for season in [djf_anoms, mam_anoms, jja_anoms, son_anoms]:
cube = utils.make_iris_cube_2d(season, lats[:, 0], lons[0],
"CLD_anom", "%")
cubelist += [cube]
utils.plot_smooth_map_iris_multipanel(settings.IMAGELOC + "CLD_{}_anoms_seasons".format(settings.YEAR), \
cubelist, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomaly (%)", shape=(2, 2), \
title=["DJF", "MAM", "JJA", "SON"], \
figtext=["(a)", "(b)", "(c)", "(d)"])
#************************************************************************
# Cloudiness Hovmoller
if True:
data_dict = scipy.io.readsav(
DATALOC +
"patmosx_global_monthly_cloudiness_hovmuller_{}.sav".format(
settings.YEAR))
lats = data_dict["latitude"]
times = data_dict["hov_time"]
anoms = data_dict["hov_anom"] * 100.
utils.plot_hovmuller(settings.IMAGELOC + "CLD_hovmuller", times, lats,
anoms, settings.COLOURMAP_DICT["hydrological"],
bounds, "Anomaly (%)")
return # run_all_plots
def run_all_plots():
if True:
for index in INDICES:
# dummy so far
# NYEARS = 66
# rank_bounds = [-1,1,2,3,NYEARS-2,NYEARS-1,NYEARS,100]
rank_bounds = [-4.5, -3.5, -2.5, -1.5, 1.5, 2.5, 3.5, 4.5]
# sort the bounds and colourbars
if index in ["TX90p", "TN90p"]:
bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100]
cmap = settings.COLOURMAP_DICT["temperature"]
elif index in ["TX10p", "TN10p"]:
bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100]
cmap = settings.COLOURMAP_DICT["temperature_r"]
elif index in ["TXx", "TNx", "TXn", "TNn"]:
bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100]
cmap = settings.COLOURMAP_DICT["temperature"]
cube_list = iris.load(
DATALOC +
"GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc".
format(index,
int(settings.YEAR) + 1))
names = np.array([cube.name() for cube in cube_list])
#*************
# plot annual map
selected_cube, = np.where(names == "Ann")[0]
cube = cube_list[selected_cube]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# change from % to days
cube.data = cube.data * 3.65
cube = ApplyClimatology(cube)
# select the year to plot
years = GetYears(cube)
loc, = np.where(years == SELECTED_YEAR)
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"TEX_{}_{}_anoms_ghcndex".format(index, settings.YEAR),
cube[loc[0]],
cmap,
bounds,
"Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]),
title="{} - {}".format(index, INDEX_LABELS[index]),
figtext=FIGURE_LABELS[index])
if index == "TX90p":
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_TEX_{}_{}_anoms_ghcndex".format(
index, settings.YEAR),
cube[loc[0]],
cmap,
bounds,
"Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]),
title="",
figtext="(c) Warm Days",
save_netcdf_filename="{}{}_for_NOAA_{}.nc".format(
DATALOC, index,
dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y")))
if index == "TN10p":
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_TEX_{}_{}_anoms_ghcndex".format(
index, settings.YEAR),
cube[loc[0]],
cmap,
bounds,
"Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]),
title="",
figtext="(d) Cool Nights",
save_netcdf_filename="{}{}_for_NOAA_{}.nc".format(
DATALOC, index,
dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y")))
rank_cube = get_ranks(cube)
plot_rank_map(
settings.IMAGELOC +
"TEX_{}_{}_rank_ghcndex".format(index, settings.YEAR),
rank_cube[loc[0]],
cmap,
rank_bounds,
"Rank",
title="{} - {}".format(index, INDEX_LABELS[index]))
#*************
# plot season maps (2x2)
season_list = []
for season in SEASONS:
# extract each month
month_data = []
months = SEASON_DICT[season]
for month in months:
selected_cube, = np.where(names == month)[0]
cube = cube_list[selected_cube]
if month == "Dec":
# need to extract from previous year - cheat by rolling data around
cube.data = np.roll(cube.data, 1, axis=0)
cube.data.mask[
0, :, :] = True # and mask out the previous years'
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# change from % to days
cube.data = cube.data * (
3.65 / 4.) # assume a season is 1/4 of a year
month_data += [cube.data]
# finished getting all months, make a dummy cube to populate
month_data = np.ma.array(month_data)
season_cube = copy.deepcopy(cube)
# take appropriate seasonal value
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
season_cube.data = np.ma.mean(month_data, axis=0)
elif index in ["TXx", "TNx"]:
season_cube.data = np.ma.max(month_data, axis=0)
elif index in ["TXn", "TNn"]:
season_cube.data = np.ma.min(month_data, axis=0)
# mask if fewer that 2 months present
nmonths_locs = np.ma.count(month_data, axis=0)
season_cube.data = np.ma.masked_where(nmonths_locs < 2,
season_cube.data)
# make anomalies
season_cube = ApplyClimatology(season_cube)
# fix for plotting
season_cube.coord('latitude').guess_bounds()
season_cube.coord('longitude').guess_bounds()
# select the year to plot
years = GetYears(cube)
loc, = np.where(years == SELECTED_YEAR)
# add to list
season_list += [season_cube[loc[0]]]
# sort the bounds and colourbars
if index in ["TX90p", "TN90p"]:
bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100]
elif index in ["TX10p", "TN10p"]:
bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100]
elif index in ["TXx", "TNx", "TXn", "TNn"]:
bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100]
# pass to plotting routine
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"TEX_{}_{}_seasons_ghcndex".format(index, settings.YEAR),
season_list,
cmap,
bounds,
"Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]),
shape=(2, 2),
title=SEASONS,
figtext=SEASON_LABELS[index],
figtitle="{} - {}".format(index, INDEX_LABELS[index]))
#*************
# timeseries obs
if True:
for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]:
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
ax3 = ax1.twinx()
ax4 = ax2.twinx()
axes = (ax1, ax2, ax3, ax4)
for ix, index in enumerate(index_pair):
index_ts, cover_ts = obtain_timeseries(
DATALOC +
"GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc".
format(index,
int(settings.YEAR) + 1), "Ann", "GHCNDEX", index)
utils.plot_ts_panel(axes[ix], [index_ts],
"-",
"temperature",
loc="",
bbox=BBOX) # no legend as single line
axes[ix].text(0.02,
0.9,
"({}) {}".format(string.ascii_lowercase[ix],
index),
transform=axes[ix].transAxes,
fontsize=settings.FONTSIZE)
# red tickmarks
axes[ix].tick_params(axis='y', colors='red', direction="in")
# and smoothed
index_ts.data.fill_value = -99.9
smoothed = binomialfilter(index_ts.data.filled(),
-99.9,
5,
pad=False)
smoothed = np.ma.masked_where(smoothed == -99.9, smoothed)
axes[ix].plot(index_ts.times, smoothed, "r--", lw=LW)
# print the index, the current anomaly and the rank information
print(index,
index_ts.data.compressed()[-1] / 36.5,
np.argsort(np.argsort(index_ts.data.compressed())) + 1)
axes[ix + 2].plot(cover_ts.times, cover_ts.data, "k:", lw=2)
axes[ix + 2].yaxis.set_label_position("right")
axes[ix + 2].yaxis.set_ticks_position('right')
# prettify
plt.xlim([1950, int(settings.YEAR) + 1])
axes[0].set_ylim([15, None])
if "X" in index:
axes[1].set_ylim([19, 59])
elif "N" in index:
axes[1].set_ylim([11, 59])
ax3.set_ylim([0, 98])
ax4.set_ylim([0, 98])
fig.text(0.03,
0.5,
"Number of Days",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE,
color="r")
fig.text(0.97,
0.5,
"% land covered",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
for ax in axes:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
tick.label2.set_fontsize(settings.FONTSIZE)
for tick in axes[1].xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
fig.subplots_adjust(left=0.1,
right=0.9,
top=0.98,
bottom=0.05,
hspace=0.001)
plt.savefig(settings.IMAGELOC + "TEX_{}+{}_ts_ghcndex{}".format(
index_pair[0], index_pair[1], settings.OUTFMT))
plt.close()
# #*************
# # timeseries ERA-Int
# for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]:
# fig, axes = plt.subplots(2, figsize=(8, 6.5), sharex=True)
# for ix, index in enumerate(index_pair):
# index_ts, cover_ts = obtain_timeseries(DATALOC + "ERA-Int_1979-{}_{}_LSmask.nc".format(settings.YEAR, index), "Annual", "ERA-Interim", index)
# utils.plot_ts_panel(axes[ix], [index_ts], "-", "temperature", loc = LEGEND_LOC, bbox = BBOX)
# axes[ix].text(0.02, 0.9, "({}) {}".format(string.ascii_lowercase[ix], index), transform = axes[ix].transAxes, fontsize = settings.FONTSIZE)
# # and smoothed
# index_ts.data.fill_value = -99.9
# smoothed = binomialfilter(index_ts.data.filled(), -99.9, 5, pad = False)
# smoothed = np.ma.masked_where(smoothed == -99.9, smoothed)
# axes[ix].plot(index_ts.times, smoothed, "r--", lw = LW)
# # prettify
# plt.xlim([1950,2019])
# axes[0].set_ylim([15,None])
# axes[1].set_ylim([16,59])
# fig.text(0.03, 0.5, "Number of Days", va='center', rotation='vertical', fontsize = settings.FONTSIZE)
# for ax in axes:
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for tick in axes[1].xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# fig.subplots_adjust(right = 0.95, top = 0.95, bottom = 0.05, hspace = 0.001)
# plt.savefig(settings.IMAGELOC+"TEX_{}+{}_ts_erai{}".format(index_pair[0], index_pair[1], settings.OUTFMT))
# plt.close()
# #*************
# # ERA-Int Maps (annual only)
# for index in INDICES:
# # sort the bounds and colourbars
# if index in ["TX90p", "TN90p"]:
# bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100]
# cmap=settings.COLOURMAP_DICT["temperature"]
# elif index in ["TX10p", "TN10p"]:
# bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100]
# cmap=settings.COLOURMAP_DICT["temperature_r"]
# elif index in ["TXx", "TNx", "TXn", "TNn"]:
# bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100]
# cmap=settings.COLOURMAP_DICT["temperature"]
# cube_list = iris.load(DATALOC + "ERA-Int_1979-{}_{}_LSmask.nc".format(settings.YEAR, index))
# names = np.array([cube.name() for cube in cube_list])
# #*************
# # plot annual map
# selected_cube, = np.where(names == "Annual")[0]
# cube = cube_list[selected_cube]
# cube.coord('latitude').guess_bounds()
# cube.coord('longitude').guess_bounds()
# if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# # change from % to days
# cube.data = cube.data * 3.65
# cube = ApplyClimatology(cube)
# # select the year to plot
# years = GetYears(cube)
# loc, = np.where(years == SELECTED_YEAR)
# utils.plot_smooth_map_iris(settings.IMAGELOC + "TEX_{}_{}_anoms_erai".format(index, settings.YEAR), cube[loc[0]], cmap, bounds, "Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]), title = "ERA-Interim {} - {}".format(index, INDEX_LABELS[index]), figtext = FIGURE_LABELS[index])
# #*************
# # plot season maps (2x2)
# season_list = []
# for season in SEASONS:
# # extract each month
# month_data = []
# months = SEASON_DICT_ERA[season]
# for month in months:
# selected_cube, = np.where(names == month)[0]
# cube = cube_list[selected_cube]
# if month == "December":
# # need to extract from previous year - cheat by rolling data around
# cube.data = np.roll(cube.data, 1, axis = 0)
# cube.data.mask[0,:,:] = True # and mask out the previous years'
# if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# # change from % to days
# cube.data = cube.data * (3.65/4.) # assume a season is 1/4 of a year
# month_data += [cube.data]
# # finished getting all months, make a dummy cube to populate
# month_data = np.ma.array(month_data)
# season_cube = copy.deepcopy(cube)
# # take appropriate seasonal value
# if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# season_cube.data = np.ma.mean(month_data, axis = 0)
# elif index in ["TXx", "TNx"]:
# season_cube.data = np.ma.max(month_data, axis = 0)
# elif index in ["TXn", "TNn"]:
# season_cube.data = np.ma.min(month_data, axis = 0)
# # mask if fewer that 2 months present
# nmonths_locs = np.ma.count(month_data, axis = 0)
# season_cube.data = np.ma.masked_where(nmonths_locs < 2, season_cube.data)
# # make anomalies
# season_cube = ApplyClimatology(season_cube)
# # fix for plotting
# season_cube.coord('latitude').guess_bounds()
# season_cube.coord('longitude').guess_bounds()
# # select the year to plot
# years = GetYears(cube)
# loc, = np.where(years == SELECTED_YEAR)
# # add to list
# season_list += [season_cube[loc[0]]]
# # sort the bounds and colourbars
# if index in ["TX90p", "TN90p"]:
# bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100]
# elif index in ["TX10p", "TN10p"]:
# bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100]
# elif index in ["TXx", "TNx", "TXn", "TNn"]:
# bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100]
# # pass to plotting routine
# utils.plot_smooth_map_iris_multipanel(settings.IMAGELOC + "TEX_{}_{}_seasons_erai".format(index, settings.YEAR), season_list, cmap, bounds, "Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]), shape = (2,2), title = SEASONS, figtext = SEASON_LABELS[index], figtitle = "{} - {}".format(index, INDEX_LABELS[index]))
#*************
# timeseries ERA5
ERA5LOCTEMP = "/data/users/rdunn/reanalyses/data/era5/v20200409/indices/"
# ERA5LOCTEMP = "/scratch/rdunn/reanalyses/era5/final/"
if True:
for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]:
fig, axes = plt.subplots(2, figsize=(8, 6.5), sharex=True)
for ix, index in enumerate(index_pair):
index_ts, cover_ts = obtain_timeseries(
ERA5LOCTEMP +
"ERA5_{}_1979-{}.nc".format(index, settings.YEAR),
"Ann",
"ERA5",
index,
is_era5=True)
utils.plot_ts_panel(axes[ix], [index_ts],
"-",
"temperature",
loc=LEGEND_LOC,
bbox=BBOX)
axes[ix].text(0.02,
0.9,
"({}) {}".format(string.ascii_lowercase[ix],
index),
transform=axes[ix].transAxes,
fontsize=settings.FONTSIZE)
# and smoothed
index_ts.data.fill_value = -99.9
smoothed = binomialfilter(index_ts.data.filled(),
-99.9,
5,
pad=False)
smoothed = np.ma.masked_where(smoothed == -99.9, smoothed)
axes[ix].plot(index_ts.times, smoothed, "r--", lw=LW)
# prettify
plt.xlim([1950, int(settings.YEAR) + 1])
axes[0].set_ylim([15, None])
axes[1].set_ylim([16, 59])
fig.text(0.03,
0.5,
"Number of Days",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
for ax in axes:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in axes[1].xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
fig.subplots_adjust(right=0.95,
top=0.95,
bottom=0.05,
hspace=0.001)
plt.savefig(settings.IMAGELOC + "TEX_{}+{}_ts_era5{}".format(
index_pair[0], index_pair[1], settings.OUTFMT))
plt.close()
#*************
# ERA Maps (annual only)
if True:
for index in INDICES:
# sort the bounds and colourbars
if index in ["TX90p", "TN90p"]:
# bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100]
bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100]
cmap = settings.COLOURMAP_DICT["temperature"]
elif index in ["TX10p", "TN10p"]:
# bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100]
bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100]
cmap = settings.COLOURMAP_DICT["temperature_r"]
elif index in ["TXx", "TNx", "TXn", "TNn"]:
bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100]
cmap = settings.COLOURMAP_DICT["temperature"]
cube_list = iris.load(
ERA5LOCTEMP +
"ERA5_{}_1979-{}.nc".format(index, settings.YEAR))
names = np.array([cube.var_name for cube in cube_list])
#*************
# plot annual map
selected_cube, = np.where(names == "Ann")[0]
cube = cube_list[selected_cube]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# change from % to days
cube.data = cube.data * 3.65
cube = ApplyClimatology(cube, is_era5=True)
# select the year to plot
years = GetYears(cube, is_era5=True)
loc, = np.where(years == SELECTED_YEAR)
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"TEX_{}_{}_anoms_era5".format(index, settings.YEAR),
cube[loc[0]],
cmap,
bounds,
"Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]),
title="ERA5 {} - {}".format(index, INDEX_LABELS[index]),
figtext=FIGURE_LABELS[index])
#*************
# plot season maps (2x2)
season_list = []
for season in SEASONS:
# extract each month
month_data = []
months = SEASON_DICT[season]
for month in months:
selected_cube, = np.where(names == month)[0]
cube = cube_list[selected_cube]
if month == "December":
# need to extract from previous year - cheat by rolling data around
cube.data = np.roll(cube.data, 1, axis=0)
cube.data.mask[
0, :, :] = True # and mask out the previous years'
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# change from % to days
cube.data = cube.data * (
3.65 / 4.) # assume a season is 1/4 of a year
month_data += [cube.data]
# finished getting all months, make a dummy cube to populate
month_data = np.ma.array(month_data)
season_cube = copy.deepcopy(cube)
# take appropriate seasonal value
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
season_cube.data = np.ma.mean(month_data, axis=0)
elif index in ["TXx", "TNx"]:
season_cube.data = np.ma.max(month_data, axis=0)
elif index in ["TXn", "TNn"]:
season_cube.data = np.ma.min(month_data, axis=0)
# mask if fewer that 2 months present
nmonths_locs = np.ma.count(month_data, axis=0)
season_cube.data = np.ma.masked_where(nmonths_locs < 2,
season_cube.data)
# make anomalies
season_cube = ApplyClimatology(season_cube, is_era5=True)
# fix for plotting
season_cube.coord('latitude').guess_bounds()
season_cube.coord('longitude').guess_bounds()
# select the year to plot
years = GetYears(cube, is_era5=True)
loc, = np.where(years == SELECTED_YEAR)
# add to list
season_list += [season_cube[loc[0]]]
# sort the bounds and colourbars
if index in ["TX90p", "TN90p"]:
bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100]
elif index in ["TX10p", "TN10p"]:
bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100]
elif index in ["TXx", "TNx", "TXn", "TNn"]:
bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100]
# pass to plotting routine
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"TEX_{}_{}_seasons_era5".format(index, settings.YEAR),
season_list,
cmap,
bounds,
"Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]),
shape=(2, 2),
title=SEASONS,
figtext=SEASON_LABELS[index],
figtitle="ERA5 {} - {}".format(index, INDEX_LABELS[index]))
#*************
# timeseries obs with uncertainties
if True:
for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]:
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
ax3 = ax1.twinx()
ax4 = ax2.twinx()
axes = (ax1, ax2, ax3, ax4)
for ix, index in enumerate(index_pair):
index_ts, cover_ts = obtain_timeseries(
DATALOC +
"GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc".
format(index,
int(settings.YEAR) + 1), "Ann", "GHCNDEX", index)
utils.plot_ts_panel(axes[ix], [index_ts],
"-",
"temperature",
loc="",
bbox=BBOX) # no legend as single line
axes[ix].text(0.02,
0.9,
"({}) {}".format(string.ascii_lowercase[ix],
index),
transform=axes[ix].transAxes,
fontsize=settings.FONTSIZE)
# obs cube
cube_list = iris.load(
DATALOC +
"GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc".
format(index,
int(settings.YEAR) + 1))
names = np.array([cube.var_name for cube in cube_list])
selected_cube, = np.where(names == "Ann")[0]
ghcndex_cube = cube_list[selected_cube]
ghcndex_cube.coord('latitude').guess_bounds()
ghcndex_cube.coord('longitude').guess_bounds()
ghcndex_cube = fix_time_coord(ghcndex_cube)
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# change from % to days
ghcndex_cube.data = ghcndex_cube.data * 3.65
# era5 cube
cube_list = iris.load(
ERA5LOCTEMP +
"ERA5_{}_1979-{}.nc".format(index, settings.YEAR))
names = np.array([cube.var_name for cube in cube_list])
selected_cube, = np.where(names == "Ann")[0]
era5_cube = cube_list[selected_cube]
era5_cube.coord('latitude').guess_bounds()
era5_cube.coord('longitude').guess_bounds()
if index in ["TX90p", "TN90p", "TX10p", "TN10p"]:
# change from % to days
era5_cube.data = era5_cube.data * 3.65
# need to regrid
era5_cube = era5_cube.regrid(
ghcndex_cube,
iris.analysis.Linear(extrapolation_mode="mask"))
coverage_offset, coverage_stdev = compute_coverage_error(
ghcndex_cube, era5_cube)
coverage_stdev *= 2. # 90%, 2s.d.
axes[ix].fill_between(index_ts.times,
index_ts.data - coverage_stdev,
index_ts.data + coverage_stdev,
color='mistyrose',
label="ERA5 coverage uncertainty")
# red tickmarks
axes[ix].tick_params(axis='y', colors='red', direction="in")
# and smoothed
index_ts.data.fill_value = -99.9
smoothed = binomialfilter(index_ts.data.filled(),
-99.9,
5,
pad=False)
smoothed = np.ma.masked_where(smoothed == -99.9, smoothed)
axes[ix].plot(index_ts.times, smoothed, "r--", lw=LW)
# print the index, the current anomaly and the rank information
print(index,
index_ts.data.compressed()[-1] / 36.5,
np.argsort(np.argsort(index_ts.data.compressed())) + 1)
axes[ix + 2].plot(cover_ts.times, cover_ts.data, "k:", lw=2)
axes[ix + 2].yaxis.set_label_position("right")
axes[ix + 2].yaxis.set_ticks_position('right')
# prettify
plt.xlim([1950, int(settings.YEAR) + 1])
axes[0].set_ylim([15, None])
if "X" in index:
axes[1].set_ylim([19, 59])
elif "N" in index:
axes[1].set_ylim([11, 59])
ax3.set_ylim([0, 98])
ax4.set_ylim([0, 98])
fig.text(0.03,
0.5,
"Number of Days",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE,
color="r")
fig.text(0.97,
0.5,
"% land covered",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
for ax in axes:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
tick.label2.set_fontsize(settings.FONTSIZE)
for tick in axes[1].xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
fig.subplots_adjust(left=0.1,
right=0.9,
top=0.98,
bottom=0.05,
hspace=0.001)
plt.savefig(settings.IMAGELOC +
"TEX_{}+{}_ts_ghcndex_uncertainties{}".format(
index_pair[0], index_pair[1], settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries - 2016 version
if False:
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
gfed = read_csv(DATALOC + "timeseries_NAme", "GFED3.1")
gfas3 = read_csv(DATALOC + "timeseries_NAme", "GFAS1p3")
gfas0 = read_csv(DATALOC + "timeseries_NAme", "GFAS1p0")
utils.plot_ts_panel(ax1, [gfed, gfas3, gfas0],
"-",
"land_surface",
loc=LEGEND_LOC)
ax1.set_ylabel("Tg(C) per month", fontsize=settings.FONTSIZE)
ax1.set_ylim([0, 100])
gfed = read_csv(DATALOC + "timeseries_TAsi", "GFED3.1")
gfas3 = read_csv(DATALOC + "timeseries_TAsi", "GFAS1p3")
gfas0 = read_csv(DATALOC + "timeseries_TAsi", "GFAS1p0")
utils.plot_ts_panel(ax2, [gfed, gfas3, gfas0],
"-",
"land_surface",
loc=LEGEND_LOC)
ax2.set_ylabel("Tg(C) per month", fontsize=settings.FONTSIZE)
ax2.set_ylim([0, 450])
# sort formatting
plt.xlim([1997, 2017])
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax2]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# sort labelling
ax1.text(0.02,
0.9,
"(a) North America",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Tropical Asia",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
plt.savefig(settings.IMAGELOC + "BOB_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Timeseries - 2017, 2018 version
if True:
fig = plt.figure(figsize=(8, 5))
plt.clf()
ax = plt.axes([0.11, 0.09, 0.88, 0.89])
gfed = read_gfed_csv(DATALOC + "data4Johannes.txt", "global")
gfas = read_csv(DATALOC + "timeseries_glob", "GFAS1p4")
utils.plot_ts_panel(ax, [gfed, gfas],
"-",
"land_surface",
loc=LEGEND_LOC)
ax.set_ylabel("Tg(C) month" + r'$^{-1}$', fontsize=settings.FONTSIZE)
ax.set_ylim([0, 840])
# sort formatting
plt.xlim([1997, int(settings.YEAR) + 2])
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# sort labelling
ax.text(0.02,
0.9,
"Global",
transform=ax.transAxes,
fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC + "BOB_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Timeseries - 2019 version
if True:
majorLocator = MultipleLocator(5)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,
figsize=(8, 10),
sharex=True)
data = read_csv(DATALOC + "timeseries_TAsi", "GFAS1p4")
utils.plot_ts_panel(ax1, [data], "-", "land_surface", loc="")
ax1.text(0.02,
0.9,
"(a) Tropical Asia",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax1.set_ylim([0, 160])
data = read_csv(DATALOC + "timeseries_Arct", "GFAS1p4")
utils.plot_ts_panel(ax2, [data], "-", "land_surface", loc="")
ax2.text(0.02,
0.9,
"(b) Arctic",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.set_ylim([0, 12])
data = read_csv(DATALOC + "timeseries_SEAu", "GFAS1p4")
utils.plot_ts_panel(ax3, [data], "-", "land_surface", loc="")
ax3.text(0.02,
0.9,
"(c) NSW & Victoria",
transform=ax3.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.set_ylim([0, 16])
data = read_csv(DATALOC + "timeseries_SAme", "GFAS1p4")
utils.plot_ts_panel(ax4, [data], "-", "land_surface", loc="")
ax4.text(0.02,
0.9,
"(d) Southern America",
transform=ax4.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax4.set_ylim([0, 160])
# sort formatting
plt.xlim([2003, int(settings.YEAR) + 2])
fig.text(0.03,
0.5,
"Tg(C) per month",
fontsize=settings.FONTSIZE,
rotation="vertical")
ax4.xaxis.set_major_locator(majorLocator)
for tick in ax4.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax2, ax3, ax4]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# sort labelling
fig.subplots_adjust(right=0.96, top=0.98, bottom=0.05, hspace=0.001)
plt.savefig(settings.IMAGELOC +
"BOB_regional_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Global Anomalies
if True:
# cube = read_data(DATALOC + "anomaly{}from2003-2015_smooth.txt".format(settings.YEAR), 2.1021584473146504e-05)
# cube_list = iris.load(DATALOC + "anomaly{}from2003-{}_coarse20.grb".format(settings.YEAR, int(settings.YEAR)-1))
cube_list = iris.load(
DATALOC +
"anomaly{}from2003-{}_coarse20.grb".format(settings.YEAR, 2010))
# 2016 - these grib files have 6 identical fields (email from J Kaiser 10/3/2017)
cube = cube_list[0]
# convert from kg(C) m-2 s-1 to g(C) m-2 a-1
print("remove factor 1.14 after 2018 run")
cube.data = cube.data * 1000 * 60 * 60 * 24 * 365. * GFAS_FACTOR
bounds = [-1000, -160, -80, -40, -10, -5, 5, 10, 40, 80, 160, 1000]
bounds = [-1000, -100, -40, -10, -5, -1, 1, 5, 10, 40, 100, 1000]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "BOB_anomalies{}".format(settings.YEAR), cube,
settings.COLOURMAP_DICT["land_surface"], bounds,
"Anomalies from 2003-{} (g C m".format(2010) + r'$^{-2}$' + " yr" +
r'$^{-1}$' + ")")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_BOB_anomalies{}".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["land_surface"],
bounds,
"Anomalies from 2003-{} (g C m".format(2010) + r'$^{-2}$' + " yr" +
r'$^{-1}$' + ")",
figtext="(af) Carbon Emissions from Biomass Burning")
#************************************************************************
# Global actuals
if True:
# cube = read_data(DATALOC + "year2016_smooth.txt", 0)
cube_list = iris.load(DATALOC +
"year{}_coarse20.grb".format(settings.YEAR))
cube = cube_list[0]
# convert from kg(C) m-2 s-1 to g(C) m-2 a-1
print("remove factor 1.14 after 2018 run")
cube.data = cube.data * 1000 * 60 * 60 * 24 * 365. * GFAS_FACTOR
bounds = [0, 5, 10, 40, 80, 120, 160, 200, 240, 500]
bounds = [0, 1, 5, 10, 40, 80, 120, 160, 200, 500]
# adjust to mask out <1 gC/m2/yr (Feb 2018)
cmap = plt.cm.YlOrBr
cmaplist = [cmap(i) for i in range(cmap.N)]
for i in range(30):
cmaplist[i] = (0.75, 0.75, 0.75, 1.0)
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)
cube.data = np.ma.masked_where(cube.data < 1, cube.data)
print("using hard coded sequential colourmap")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "BOB_actuals{}".format(settings.YEAR), cube,
cmap, bounds, "g C m" + r'$^{-2}$' + " yr" + r'$^{-1}$' + "")
#************************************************************************
# Global climatology
if False:
cube_list = iris.load(DATALOC + "clim2003-2015_smooth.grb")
cube = cube_list[0]
# convert from kg(C) m-2 s-1 to g(C) m-2 a-1
cube.data = cube.data * 1000 * 60 * 60 * 24 * 365.
bounds = [0, 5, 10, 40, 80, 120, 160, 200, 240, 500]
print("using hard coded sequential colourmap")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "BOB_climatology{}".format(settings.YEAR),
cube, plt.cm.YlOrBr, bounds,
"g C m" + r'$^{-2}$' + " yr" + r'$^{-1}$' + "")
return # run_all_plots
#************************************************************************
# Discharge Map
cube_list = iris.load(data_loc + "discharge.{}.nc".format(settings.YEAR))
cube = fix_coords(cube_list[0])
#cube.data = np.ma.masked_where(np.logical_and(cube.data < 20, cube.data > -20), cube.data)
mask = cube.data.mask
bounds = [-10000, -1000, -500, -100, -50, -25, 25, 50, 100, 500, 1000, 10000]
utils.plot_smooth_map_iris(
image_loc + "RDC_discharge_{}_jra55".format(settings.YEAR), cube[0],
settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 1958-{} (m".format(int(settings.YEAR) - 1) + r'$^{3}$' +
" s" + r'$^{-1}$' + ")")
utils.plot_smooth_map_iris(
image_loc + "p2.1_RDC_discharge_{}_jra55".format(settings.YEAR),
cube[0],
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Anomalies from 1958-{} (m".format(int(settings.YEAR) - 1) + r'$^{3}$' +
" s" + r'$^{-1}$' + ")",
figtext="(o) River Discharge")
#************************************************************************
# Runoff Map
cube_list = iris.load(data_loc + "runoff.{}.nc".format(settings.YEAR))
def run_all_plots():
#************************************************************************
# GRACE timeseries
if True:
# model = read_ts(DATALOC + "glb_avg_tws_2003-2018_model.txt", "Model")
# model.ls = "--"
grace = read_ts(DATALOC + "avg_JPLM06v2_land.txt", "GRACE")
grace_fo = utils.Timeseries("GRACE FO", grace.times[:], grace.data[:])
# post 2018
locs, = np.where(grace_fo.times > 2018)
grace_fo.times = grace_fo.times[locs]
grace_fo.data = grace_fo.data[locs]
# pre 2018
locs, = np.where(grace.times < 2018)
grace.times = grace.times[locs]
grace.data = grace.data[locs]
fig = plt.figure(figsize=(8, 5))
ax1 = plt.axes([0.1, 0.1, 0.88, 0.88])
utils.plot_ts_panel(ax1, [grace, grace_fo],
"-",
"hydrological",
loc=LEGEND_LOC)
#*******************
# prettify
ax1.set_ylim([-5.2, 2.3])
ax1.set_xlim([2003, int(settings.YEAR) + 1.3])
ax1.set_ylabel("Anomaly (cm)", fontsize=settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "TWS_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Hovmuller - Model
if False:
times, latitudes, data = read_hovmuller(DATALOC + "zonal_mean_tws.txt")
bounds = np.array([-200, -12, -9, -6, -3, 0, 3, 6, 9, 12, 200])
utils.plot_hovmuller(settings.IMAGELOC + "TWS_hovmuller_grace", times,
latitudes, data,
settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomaly (cm)")
#************************************************************************
# GRACE Hovmuller
if True:
times, latitudes, data = read_hovmuller_2017(DATALOC)
bounds = np.array([-200, -12, -9, -6, -3, 0, 3, 6, 9, 12, 200])
utils.plot_hovmuller(settings.IMAGELOC + "TWS_hovmuller_grace", times,
latitudes, data.T,
settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomaly (cm)")
#************************************************************************
# Difference Map
if True:
cube = read_map_data(
DATALOC + "tws_changes_{}-{}_2.txt".format(settings.YEAR,
int(settings.YEAR) - 1))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_TWS_{}_diffs".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Difference between {} and {} Equivalent Depth of Water (cm)".
format(settings.YEAR,
int(settings.YEAR) - 1),
figtext="(q) Terrestrial Water Storage")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "TWS_{}_diffs".format(settings.YEAR), cube,
settings.COLOURMAP_DICT["hydrological"], bounds,
"Difference between {} and {} Equivalent Depth of Water (cm)".
format(settings.YEAR,
int(settings.YEAR) - 1))
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries
if True:
IRdata = read_binary_ts(
DATALOC +
"TimeseriesBHRNIR_C7_poids_{}.bin".format(int(settings.YEAR) + 1))
Vdata = read_binary_ts(
DATALOC +
"TimeseriesBHRV_C7_poids_{}.bin".format(int(settings.YEAR) + 1))
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
COLOURS = settings.COLOURS["land_surface"]
for dataset in Vdata:
print(dataset.name)
locs, = np.where(dataset.data != 0)
ls = "--"
lw = 1
if dataset.name.split(" ")[-1] == "Smoothed":
ls = "-"
lw = 2
ax1.plot(dataset.times[locs],
dataset.data[locs],
c=COLOURS[dataset.name],
ls=ls,
label=dataset.name,
lw=lw)
ax1.axhline(0, c='0.5', ls='--')
utils.thicken_panel_border(ax1)
for dataset in IRdata:
print(dataset.name)
locs, = np.where(
dataset.data != 0) # remove zero bits (mainly for smoothed)
ls = "--"
lw = 1
if dataset.name.split(" ")[-1] == "Smoothed":
ls = "-"
lw = 2
ax2.plot(dataset.times[locs],
dataset.data[locs],
c=COLOURS[dataset.name],
ls=ls,
label=dataset.name,
lw=lw)
ax2.legend(loc=LEGEND_LOC,
ncol=2,
frameon=False,
prop={'size': settings.LEGEND_FONTSIZE * 0.8},
labelspacing=0.1,
columnspacing=0.5)
ax2.axhline(0, c='0.5', ls='--')
utils.thicken_panel_border(ax2)
#*******************
# prettify
fig.text(0.01,
0.5,
"Normalized Anomalies (%)",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
plt.xlim([2002.5, int(settings.YEAR) + 1.5])
ax2.set_ylim([-5.8, None])
for ax in [ax1, ax2]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_minor_locator(minorLocator)
ax.set_yticks(ax.get_yticks()[1:-1])
ax.yaxis.set_ticks_position('left')
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax1.text(0.02,
0.9,
"(a) Visible",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Near Infrared",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
fig.subplots_adjust(right=0.95, top=0.95, bottom=0.05, hspace=0.001)
plt.savefig(settings.IMAGELOC + "ABD_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Hovmullers
if True:
IRdata = read_binary(
DATALOC +
"HovMullerBHRNIR_C7_{}.bin".format(int(settings.YEAR) + 1))
Vdata = read_binary(
DATALOC + "HovMullerBHRV_C7_{}.bin".format(int(settings.YEAR) + 1))
# reshape - from Readme
IRdata = IRdata.reshape(360, DURATION)
Vdata = Vdata.reshape(360, DURATION)
IRdata = np.ma.masked_where(IRdata <= -100.,
IRdata) # * 100 # from readme
Vdata = np.ma.masked_where(Vdata <= -100., Vdata) # * 100
lats = np.arange(-90, 90, 0.5)
# curtail times
locs, = np.where(times > int(settings.YEAR) + 1)
IRdata.mask[:, locs] = True
Vdata.mask[:, locs] = True
times.mask[locs] = True
# cant use "cmap.set_bad" for contour as ignored when contouring
bounds = [-100, -20, -15, -10, -5, -1, 1, 5, 10, 15, 20, 100]
utils.plot_hovmuller(settings.IMAGELOC + "ABD_NIR_hovmuller",
times,
lats,
IRdata,
settings.COLOURMAP_DICT["land_surface_r"],
bounds,
"Normalized Anomalies (%)",
figtext="(b)",
background="0.7")
utils.plot_hovmuller(settings.IMAGELOC + "ABD_V_hovmuller",
times,
lats,
Vdata,
settings.COLOURMAP_DICT["land_surface_r"],
bounds,
"Normalized Anomalies (%)",
figtext="(a)",
background="0.7")
#************************************************************************
# Anomalies
if True:
IRdata = read_binary(
DATALOC + "Annual_BHRNIR_C7_{}.bin".format(int(settings.YEAR) + 1))
Vdata = read_binary(
DATALOC + "Annual_BHRV_C7_{}.bin".format(int(settings.YEAR) + 1))
# reshape - from Readme
IRdata = IRdata.reshape(360, 720)
Vdata = Vdata.reshape(360, 720)
IRdata = np.ma.masked_where(IRdata <= -9999., IRdata)
Vdata = np.ma.masked_where(Vdata <= -9999., Vdata)
IRdata = np.ma.masked_where(IRdata == 0., IRdata)
Vdata = np.ma.masked_where(Vdata == 0., Vdata)
delta = 0.5
lons = np.arange(-180 + (delta / 2.), 180, delta)
lats = np.arange(-90 + (delta / 2.), 90, delta)
ir_cube = utils.make_iris_cube_2d(IRdata, lats, lons, "IR Albedo", "%")
v_cube = utils.make_iris_cube_2d(Vdata, lats, lons, "V Albedo", "%")
bounds = [-100, -20, -15, -10, -5, 0, 5, 10, 15, 20, 100]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_ABD_V_{}".format(settings.YEAR),
v_cube,
settings.COLOURMAP_DICT["land_surface_r"],
bounds,
"Anomalies from 2003-{} (%)".format(2010),
figtext="(ac) Land Surface Albedo in the Visible")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ABD_V_{}".format(settings.YEAR), v_cube,
settings.COLOURMAP_DICT["land_surface_r"], bounds,
"Anomalies from 2003-{} (%)".format(2010))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_ABD_NIR_{}".format(settings.YEAR),
ir_cube,
settings.COLOURMAP_DICT["land_surface_r"],
bounds,
"Anomalies from 2003-{} (%)".format(2010),
figtext="(ad) Land Surface Albedo in the Near Infrared")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ABD_NIR_{}".format(settings.YEAR), ir_cube,
settings.COLOURMAP_DICT["land_surface_r"], bounds,
"Anomalies from 2003-{} (%)".format(2010))
return # run_all_plots
#***************
# Anomaly Scatter map
bounds = [-8, -2, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2, 8]
lons = np.arange(-90, 120, 30)
lats = np.arange(-180, 210, 30)
dummy = np.ma.zeros((len(lats), len(lons)))
dummy.mask = np.ones(dummy.shape)
cube = utils.make_iris_cube_2d(dummy, lats, lons, "blank", "m")
utils.plot_smooth_map_iris(image_loc + "LKT_anomaly",
cube,
settings.COLOURMAP_DICT["temperature"],
bounds,
"Anomaly (" + r"$^{\circ}$" + "C)",
scatter=[anomalies[1], anomalies[0], anomalies[2]],
figtext="",
title="")
utils.plot_smooth_map_iris(image_loc + "p2.1_LKT_anomaly",
cube,
settings.COLOURMAP_DICT["temperature"],
bounds,
"Anomaly (" + r"$^{\circ}$" + "C)",
scatter=[anomalies[1], anomalies[0], anomalies[2]],
figtext="(b) Lake Temperatures",
title="")
#************************************************************************
# END
#************************************************************************
def run_all_plots(download=False):
if download:
# download if required
# AAO
http("https://www.esrl.noaa.gov/", "psd/data/correlation", "aao.data", DATALOC, diagnostics=True)
http("https://www.cpc.ncep.noaa.gov", "products/precip/CWlink/daily_ao_index/aao", "monthly.aao.index.b79.current.ascii", DATALOC, diagnostics=True)
# AO
http("https://www.cpc.ncep.noaa.gov", "products/precip/CWlink/daily_ao_index", "monthly.ao.index.b50.current.ascii", DATALOC, diagnostics=True)
# NAO
http("https://climatedataguide.ucar.edu", "sites/default/files", "nao_station_seasonal.txt", DATALOC, diagnostics=True)
print("HadSLP2r & SOI likely to fail")
# HadSLP2r
http("http://www.metoffice.gov.uk", "hadobs/hadslp2/data", "hadslp2r.asc.gz", DATALOC, diagnostics=True)
# SOI
doftp("ftp://ftp.bom.gov.au", "/anon/home/ncc/www/sco/soi/", "soiplaintext.html", DATALOC, diagnostics=True)
print("Now check downloads")
sys.exit()
#************************************************************************
# Timeseries figures - winter NAO
# initial run usually only Dec + Jan for recent year
if True:
# tried minor locator
YEARS = ["2017", "2018", "2019"]
plot_data, smoothed_data = read_winter_nao(DATALOC, YEARS)
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 6.5))
TEXTS = ["(d)", "(e)", "(f)"]
for a, ax in enumerate([ax1, ax2, ax3]):
year = int(YEARS[a])
plt_bars(ax, plot_data[a], TEXTS[a], w=1, invert=False)
utils.thicken_panel_border(ax)
ax.xaxis.set_ticks([dt.datetime(year, 12, 1), dt.datetime(year+1, 1, 1), dt.datetime(year+1, 2, 1), dt.datetime(year+1, 3, 1)])
ax.plot(plot_data[a].times+dt.timedelta(hours=12), smoothed_data[a], "k-", lw=LW)
ax.set_xlim([dt.datetime(year, 11, 29), dt.datetime(year+1, 2, 1) + dt.timedelta(days=30)])
ax3.xaxis.set_ticklabels(["Dec", "Jan", "Feb", "Mar"], fontsize=settings.FONTSIZE)
ax2.set_ylabel("Winter NAO Index (hPa)", fontsize=settings.FONTSIZE)
fig.subplots_adjust(right=0.98, top=0.98, bottom=0.05, hspace=0.001)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
for ax in [ax1, ax2, ax3]:
ax.set_ylim([-69, 99])
ax.yaxis.set_ticks_position('left')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC+"SLP_ts_winter_nao{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Timeseries figures - different indices
if True:
SOI = read_soi(DATALOC + "soiplaintext.html")
AO = read_a_ao(DATALOC + "monthly.ao.index.b50.current.ascii", "AO", 3)
AAO = read_a_ao(DATALOC + "monthly.aao.index.b79.current.ascii", "AAO", 5)
# SNAO = read_snao(DATALOC + "JA STANDARDISED SNAO.txt") # from Chris Folland - unavailable in 2019
NAO = read_nao(DATALOC + "nao_station_seasonal.txt")
fig = plt.figure(figsize=(8, 6))
# manually set up the 10 axes
w = 0.42
h = 0.23
c = 0.51
ax1 = plt.axes([c-w, 0.99-h, w, h])
ax2 = plt.axes([c, 0.99-h, w, h])
ax3 = plt.axes([c-w, 0.99-(2*h), w, h], sharex=ax1)
ax4 = plt.axes([c, 0.99-(2*h), w, h], sharex=ax2)
ax5 = plt.axes([c-w, 0.99-(3*h), w, h], sharex=ax1)
ax6 = plt.axes([c, 0.99-(3*h), w, h], sharex=ax2)
ax7 = plt.axes([c-w, 0.99-(4*h), w, h], sharex=ax1)
ax8 = plt.axes([c, 0.99-(4*h), w, h], sharex=ax2)
# ax9 = plt.axes([c-w, 0.99-(5*h), w, h], sharex=ax1)
# ax10= plt.axes([c, 0.99-(5*h), w, h], sharex=ax2)
plt_bars(ax1, SOI, "(a)", w=1./12, invert=True)
plt_bars(ax3, AO, "(c)", w=1./12)
plt_bars(ax5, AAO, "(e)", w=1./12)
plt_bars(ax7, NAO, "(g)", w=1.)
# plt_bars(ax9, SNAO, "(i)", w=1.)
ax1.set_xlim([1860, int(settings.YEAR)+1])
plt_bars(ax2, SOI, "(b)", w=1./12, invert=True)
plt_bars(ax4, AO, "(d)", w=1./12)
plt_bars(ax6, AAO, "(f)", w=1./12)
plt_bars(ax8, NAO, "(h)", w=0.9)
# plt_bars(ax10, SNAO, "(j)", w=0.9)
ax2.set_xlim([2006, int(settings.YEAR)+2])
for ax in [ax2, ax4, ax6, ax8]:#, ax10]:
ax.yaxis.tick_right()
# prettify
for ax in [ax1, ax3, ax5, ax7]:#, ax9]:
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
utils.thicken_panel_border(ax)
ax.yaxis.set_ticks_position('left')
for ax in [ax2, ax4, ax6, ax8]:#, ax10]:
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.yaxis.tick_right()
for tick in ax.yaxis.get_major_ticks():
tick.label2.set_fontsize(settings.FONTSIZE)
utils.thicken_panel_border(ax)
plt.setp([a.get_xticklabels() for a in fig.axes[:-2]], visible=False)
ax1.xaxis.set_ticks([1880, 1920, 1960, 2000])
ax2.xaxis.set_ticks([2007, 2010, 2013, 2016, 2019])
ax1.yaxis.set_ticks([-40, 0, 40])
ax2.yaxis.set_ticks([-40, 0, 40])
ax3.yaxis.set_ticks([-4, 0, 4])
ax4.yaxis.set_ticks([-4, 0, 4])
ax5.yaxis.set_ticks([-2, 0, 2])
ax6.yaxis.set_ticks([-2, 0, 2])
ax7.yaxis.set_ticks([-4, 0, 4])
ax8.yaxis.set_ticks([-4, 0, 4])
# ax9.yaxis.set_ticks([-2, 0, 2])
# ax10.yaxis.set_ticks([-2, 0, 2])
minorLocator = MultipleLocator(1)
ax2.xaxis.set_minor_locator(minorLocator)
ax1.set_ylim([-40, 45])
ax2.set_ylim([-40, 45])
ax3.set_ylim([-4.5, 4.9])
ax4.set_ylim([-4.5, 4.9])
ax5.set_ylabel("Standard Units", fontsize=settings.FONTSIZE)
plt.savefig(settings.IMAGELOC+"SLP_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Global map
if True:
cube = read_hadslp(DATALOC + "hadslp2r.asc")
# restrict to 1900 to last full year
date_constraint = utils.periodConstraint(cube, dt.datetime(1900, 1, 1), dt.datetime(int(settings.YEAR)+1, 1, 1))
cube = cube.extract(date_constraint)
# convert to 1981-2010 climatology.
clim_constraint = utils.periodConstraint(cube, dt.datetime(1981, 1, 1), dt.datetime(2011, 1, 1))
clim_cube = cube.extract(clim_constraint)
clim_data = clim_cube.data.reshape(-1, 12, clim_cube.data.shape[-2], clim_cube.data.shape[-1])
# more than 15 years present
climatology = np.ma.mean(clim_data, axis=0)
nyears = np.ma.count(clim_data, axis=0)
climatology = np.ma.masked_where(nyears <= 15, climatology) # Kate keeps GT 15.
# extract final year
final_year_constraint = utils.periodConstraint(cube, dt.datetime(int(settings.YEAR), 1, 1), dt.datetime(int(settings.YEAR)+1, 1, 1))
final_year_cube = cube.extract(final_year_constraint)
final_year_cube.data = final_year_cube.data - climatology
# more than 6 months present
annual_cube = final_year_cube.collapsed(['time'], iris.analysis.MEAN)
nmonths = np.ma.count(final_year_cube.data, axis=0)
annual_cube.data = np.ma.masked_where(nmonths <= 6, annual_cube.data)
bounds = [-100, -8, -4, -2, -1, 0, 1, 2, 4, 8, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SLP_{}_anoms_hadslp".format(settings.YEAR), annual_cube, settings.COLOURMAP_DICT["circulation"], bounds, "Anomalies from 1981-2010 (hPa)")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_SLP_{}_anoms_hadslp".format(settings.YEAR), annual_cube, settings.COLOURMAP_DICT["circulation"], bounds, "Anomalies from 1981-2010 (hPa)", figtext="(u) Sea Level Pressure")
plt.close()
del annual_cube
del final_year_cube
gc.collect()
#************************************************************************
# Polar Figures (1x3)
if True:
# apply climatology - incomplete end year, so repeat climatology and then trunkate
climatology = np.tile(climatology, ((cube.data.shape[0]//12)+1, 1, 1))
anoms = iris.cube.Cube.copy(cube)
anoms.data = anoms.data - climatology[0:anoms.data.shape[0], :, :]
bounds = [-100, -8, -4, -2, -1, -0.5, 0.5, 1, 2, 4, 8, 100]
# set up a 1 x 3 set of axes
fig = plt.figure(figsize=(4, 9.5))
plt.clf()
# set up plot settings
cmap = settings.COLOURMAP_DICT["circulation"]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
PLOTYEARS = [2017, 2018, 2019]
PLOTLABELS = ["(a) 2017/18", "(b) 2018/19", "(c) 2019/20"]
# boundary circle
theta = np.linspace(0, 2*np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# spin through axes
for a in range(3):
ax = plt.subplot(3, 1, a+1, projection=cartopy.crs.NorthPolarStereo())
plot_cube = iris.cube.Cube.copy(anoms)
# extract 3 winter months
date_constraint = utils.periodConstraint(anoms, dt.datetime(PLOTYEARS[a], 12, 1), dt.datetime(PLOTYEARS[a]+1, 3, 1))
plot_cube = plot_cube.extract(date_constraint)
# plot down to (almost) equator
lat_constraint = utils.latConstraint([3, 80])
plot_cube = plot_cube.extract(lat_constraint)
# take the mean
try:
plot_cube = plot_cube.collapsed(['time'], iris.analysis.MEAN)
except iris.exceptions.CoordinateCollapseError:
pass
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
ax.set_extent([-180, 180, 0, 90], cartopy.crs.PlateCarree())
ext = ax.get_extent() # save the original extent
mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax)
ax.set_extent(ext, ax.projection) # fix the extent change from colormesh
ax.text(0.0, 1.02, PLOTLABELS[a], fontsize=settings.FONTSIZE, transform=ax.transAxes)
# add a colourbar for the figure
cbar_ax = fig.add_axes([0.77, 0.07, 0.04, 0.9])
cb = plt.colorbar(mesh, cax=cbar_ax, orientation='vertical', ticks=bounds[1:-1], drawedges=True)
cb.ax.tick_params(axis='y', labelsize=settings.FONTSIZE, direction='in', size=0)
cb.set_label(label="Anomaly (hPa)", fontsize=settings.FONTSIZE)
# prettify
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
fig.subplots_adjust(bottom=0.01, top=0.97, left=0.01, right=0.8, wspace=0.02)
plt.title("")
fig.text(0.03, 0.95, "", fontsize=settings.FONTSIZE * 0.8)
plt.savefig(settings.IMAGELOC + "SLP_polar{}".format(settings.OUTFMT))
plt.close()
del plot_cube
del climatology
gc.collect()
#************************************************************************
# SNAO figures
if False:
date_constraint = utils.periodConstraint(anoms, dt.datetime(int(settings.YEAR), 7, 1), dt.datetime(int(settings.YEAR), 9, 1))
ja_cube = anoms.extract(date_constraint)
ja_cube = ja_cube.collapsed(['time'], iris.analysis.MEAN)
bounds = [-100, -4, -3, -2, -1, 0, 1, 2, 3, 4, 100]
cmap = settings.COLOURMAP_DICT["circulation"]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
PLOTLABEL = "(a) July - August"
fig = plt.figure(figsize=(8, 10))
plt.clf()
# make axes by hand
axes = ([0.05, 0.45, 0.9, 0.55], [0.1, 0.05, 0.88, 0.35])
ax = plt.axes(axes[0], projection=cartopy.crs.Robinson())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k")
ax.coastlines()
mesh = iris.plot.pcolormesh(ja_cube, cmap=cmap, norm=norm, axes=ax)
ax.text(-0.05, 1.05, PLOTLABEL, fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes)
# colorbar
cb = plt.colorbar(mesh, orientation='horizontal', ticks=bounds[1:-1], label="Anomaly (hPa)", drawedges=True, pad=0.05)
# prettify
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
# and the timeseries
ax = plt.axes(axes[1])
snao = np.genfromtxt(DATALOC+"{} DAILY SNAO.txt".format(settings.YEAR), dtype=(float))
data = snao[:, 1]
times = np.array([dt.datetime(int(settings.YEAR), 7, 1) + dt.timedelta(days=i) for i in range(len(data))])
SNAO = utils.Timeseries("Summer NAO Index", times, data)
plt_bars(ax, SNAO, "", w=0.7, label=False)
ax.text(-0.1, 1.05, "(b) Summer NAO Index", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes)
ax.yaxis.set_label("Summer NAO Index")
# label every 14 days
ticks = [dt.datetime(int(settings.YEAR), 7, 1) + dt.timedelta(days=i) for i in range(0, len(data), 14)]
ax.xaxis.set_ticks(ticks)
ax.xaxis.set_ticklabels([dt.datetime.strftime(t, "%d %b %Y") for t in ticks])
minorLocator = MultipleLocator(1)
ax.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax3)
plt.savefig(settings.IMAGELOC + "SLP_SNAO{}".format(settings.OUTFMT))
plt.close()
del ja_cube
gc.collect()
#************************************************************************
# EU figure
if False:
contour = True
date_constraint = utils.periodConstraint(anoms, dt.datetime(int(settings.YEAR), 7, 1), dt.datetime(int(settings.YEAR), 8, 1))
jul_cube = anoms.extract(date_constraint)
date_constraint = utils.periodConstraint(anoms, dt.datetime(int(settings.YEAR), 8, 1), dt.datetime(int(settings.YEAR), 9, 1))
aug_cube = anoms.extract(date_constraint)
bounds = [-100, -4, -3, -2, -1, 0, 1, 2, 3, 4, 100]
cmap = settings.COLOURMAP_DICT["circulation"]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
PLOTLABELS = ["(a) July", "(b) August"]
fig = plt.figure(figsize=(8, 14))
plt.clf()
# make axes by hand
axes = ([0.1, 0.65, 0.8, 0.3], [0.1, 0.35, 0.8, 0.3], [0.1, 0.05, 0.8, 0.25])
cube_list = [jul_cube, aug_cube]
for a in range(2):
ax = plt.axes(axes[a], projection=cartopy.crs.PlateCarree(central_longitude=-10))
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k")
ax.coastlines()
ax.set_extent([-70, 40, 30, 80], cartopy.crs.PlateCarree())
if contour:
from scipy.ndimage.filters import gaussian_filter
sigma = 0.5
cube = cube_list[a]
cube.data = gaussian_filter(cube.data, sigma)
mesh = iris.plot.contourf(cube, bounds, cmap=cmap, norm=norm, axes=ax)
else:
mesh = iris.plot.pcolormesh(cube_list[a], cmap=cmap, norm=norm, axes=ax)
ax.text(-0.05, 1.05, PLOTLABELS[a], fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes)
# colorbar
cb = plt.colorbar(mesh, orientation='horizontal', ticks=bounds[1:-1], label="Anomaly (hPa)", drawedges=True)
# prettify
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
# and the timeseries
ax3 = plt.axes(axes[2])
snao = np.genfromtxt(DATALOC+"{} DAILY SNAO.txt".format(settings.YEAR), dtype=(float))
data = snao[:, 1]
times = np.array([dt.datetime(int(settings.YEAR), 7, 1) + dt.timedelta(days=i) for i in range(len(data))])
SNAO = utils.Timeseries("Summer NAO Index", times, data)
plt_bars(ax3, SNAO, "", w=0.7, label=False)
ax3.text(-0.05, 1.05, "(c) Summer NAO Index", fontsize=settings.FONTSIZE * 0.8, transform=ax3.transAxes)
ax3.yaxis.set_label("Summer NAO Index")
# label every 14 days
ticks = [dt.datetime(int(settings.YEAR), 7, 1) + dt.timedelta(days=i) for i in range(0, len(data), 14)]
ax3.xaxis.set_ticks(ticks)
ax3.xaxis.set_ticklabels([dt.datetime.strftime(t, "%d %b %Y") for t in ticks])
minorLocator = MultipleLocator(1)
ax3.xaxis.set_minor_locator(minorLocator)
ax3.yaxis.set_ticks_position('left')
utils.thicken_panel_border(ax3)
plt.savefig(settings.IMAGELOC + "SLP_NAtlantic{}".format(settings.OUTFMT))
plt.close()
del cube
del anoms
gc.collect()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries
if True:
cube_list = np.array(
iris.load(DATALOC +
"ESA_CCI_SM_COMBINED_monthAnomaliesPerHemisphere.nc"))
names = np.array([c.var_name for c in cube_list])
north_obs = cube_list[names == "nObs_north"][0]
south_obs = cube_list[names == "nObs_south"][0]
glob_obs = cube_list[names == "nObs_global"][0]
north = cube_list[names == "Anomalies_north"][0]
south = cube_list[names == "Anomalies_south"][0]
glob = cube_list[names == "Anomalies_global"][0]
# as date in days-from - let Iris do the heavy lifting and do this one manually
fig = plt.figure(figsize=(8, 6))
ax1 = plt.axes([0.14, 0.2, 0.84, 0.79])
iris.plot.plot(south, 'b', label="S. Hemisphere", lw=LW)
iris.plot.plot(north, "0.5", label="N. Hemisphere", lw=LW)
iris.plot.plot(glob, "k", label="Global", lw=LW)
ax1.text(0.02,
0.9,
"ESA CCI SM",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE)
# number of observations
ax2 = plt.axes([0.14, 0.07, 0.84, 0.13], sharex=ax1)
south_obs.data = 100 * south_obs.data / 244243.
north_obs.data = 100 * north_obs.data / 244243.
glob_obs.data = 100 * glob_obs.data / 244243.
iris.plot.plot(south_obs, 'b', lw=LW)
iris.plot.plot(north_obs, "0.5", lw=LW)
iris.plot.plot(glob_obs, "k", lw=LW)
#*******************
# prettify
ax1.set_ylim([-0.021, 0.021])
ax1.set_ylabel("Anomaly (m" + r'$^{3}$' + "m" + r'$^{-3}$' + ")",
fontsize=settings.FONTSIZE)
ax1.axhline(0, c='0.5', ls='--')
ax1.set_yticks([-0.02, -0.01, 0, 0.01, 0.02])
ax2.set_ylim([0, 100])
ax2.set_ylabel("% \n obs.", fontsize=settings.FONTSIZE)
ax2.set_yticks([0, 25, 50, 75])
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax2]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.yaxis.set_ticks_position('left')
ax1.legend(loc=LEGEND_LOC,
ncol=1,
frameon=False,
prop={'size': settings.LEGEND_FONTSIZE},
labelspacing=0.1,
columnspacing=0.5)
utils.thicken_panel_border(ax1)
utils.thicken_panel_border(ax2)
minorLocator = MultipleLocator(365.242199) # in days since
ax1.xaxis.set_minor_locator(minorLocator)
lims = ax1.get_xlim()
ax1.set_xlim([lims[0] - 100, lims[1] + 100])
# ax2.set_xticklabels("")
plt.savefig(settings.IMAGELOC +
"SMS_ts_esa_cci{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Annual Map
if True:
cube_list = iris.load(DATALOC +
"ESA_CCI_SM_COMBINED_anomalyMaps_yearly.nc")
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [
-100, -0.04, -0.03, -0.02, -0.01, 0, 0.01, 0.02, 0.03, 0.04, 100
]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "SMS_{}_esa_cci".format(settings.YEAR),
cube[-1], settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 1991-2010 (m" + r'$^{3}$' + "m" + r'$^{-3}$' + ")")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_SMS_{}_esa_cci".format(settings.YEAR),
cube[-1],
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Anomalies from 1991-2010 (m" + r'$^{3}$' + "m" + r'$^{-3}$' + ")",
figtext="(r) Soil Moisture")
#************************************************************************
# Seasonal Map
if True:
cube_list = iris.load(DATALOC +
"ESA_CCI_SM_COMBINED_anomalyMaps_monthly.nc")
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
month_list = [cube[i] for i in range(-12, 0, 1)]
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC + "SMS_{}_anoms_seasons".format(settings.YEAR),
month_list,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Anomaly (m" + r'$^{3}$' + "m" + r'$^{-3}$' + ")",
shape=(6, 2),
title=MONTHS,
figtext=[
"(a)", "(b)", "(c)", "(d)", "(e)", "(f)", "(g)", "(h)", "(i)",
"(j)", "(k)", "(l)"
])
#************************************************************************
# Hovmuller
if True:
cube_list = iris.load(
DATALOC + "ESA_CCI_SM_COMBINED_anomaly_hovmoeller_diagram.nc")
for cube in cube_list:
if cube.name() == "hovmoeller": break
print("issues with masking in 2018 - remove and reset")
data = cube.data[:]
data.mask = np.zeros(data.shape)
data = np.ma.masked_where(data == data.fill_value, data)
cube.data = data
# to here
latitudes = cube.coord("latitude").points
anoms = cube.data * 0.01 # added for offset in 2017 report
bounds = [
-100, -0.04, -0.03, -0.02, -0.01, 0, 0.01, 0.02, 0.03, 0.04, 100
]
# extract the time data
timeUnits = cube.coord("time").units
dt_time = timeUnits.num2date(cube.coord("time").points)
times = np.array([(date.year + (date.month - 1) / 12.)
for date in dt_time])
utils.plot_hovmuller(settings.IMAGELOC + "SMS_hovmuller", times,
latitudes, anoms,
settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomaly (m" + r'$^{3}$' + "m" + r'$^{-3}$' + ")")
return # run_all_plots
def run_all_plots():
#************************************************************************
# Upper Tropospheric Humidity timeseries figure
if True:
HIRSSTART = 1979
MWSTART = 1999
ERASTART = 1979
# smooth by 3 months
hirs = read_ts(DATALOC + "hirs_data.aa", HIRSSTART, "HIRS", smooth=3)
mw = read_ts(DATALOC + "mw_data.aa", MWSTART, "Microwave", smooth=3)
era5 = read_ts(DATALOC + "era5_data.aa", ERASTART, "ERA5", smooth=3)
fig = plt.figure(figsize=(8, 5))
ax = plt.axes([0.11, 0.08, 0.86, 0.90])
utils.plot_ts_panel(ax, [hirs, mw, era5],
"-",
"hydrological",
loc=LEGEND_LOC,
ncol=3)
#*******************
# prettify
fig.text(0.01,
0.5,
"Anomalies (% rh)",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
plt.ylim([-2.0, 2.0])
plt.xlim([1979, int(settings.YEAR) + 1.5])
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "UTH_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# HIRS map
if True:
cube = read_map(DATALOC, "hirs")
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
# utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_UTH_{}_anoms_hirs".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 2001-2010 (% rh)", figtext = "(n) Upper Tropospheric Humidity")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "UTH_{}_anoms_hirs".format(settings.YEAR),
cube, settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 2001-2010 (% rh)")
#************************************************************************
# MW map
if True:
cube = read_map(DATALOC, "mw")
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "UTH_{}_anoms_mw".format(settings.YEAR), cube,
settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 2001-2010 (% rh)")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_UTH_{}_anoms_mw".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Anomalies from 2001-2010 (% rh)",
figtext="(j) Upper Tropospheric Humidity")
return # run_all_plots
seasonal_list = []
cube_list = iris.load(data_loc +
"TCCO_ANO_YEAR_JAS_mean_{}.nc".format(settings.YEAR))
names = np.array([cube.var_name for cube in cube_list])
selected_cube, = np.where(names == "tcco_ano")
cube = cube_list[selected_cube[0]]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -20, -15, -10, -5, 0, 5, 10, 15, 20, 100]
utils.plot_smooth_map_iris(
image_loc + "CMO_{}_anoms".format(settings.YEAR), cube,
settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 2003-{} (%)".format(settings.YEAR[2:]))
utils.plot_smooth_map_iris(
image_loc + "p2.1_CMO_{}_anoms".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-{} (%)".format(settings.YEAR[2:]),
figtext="(ab) Carbon Monoxide")
# # Global Map - Jan-Jun
# selected_cube, = np.where(names == "tcco_ano_jan_jun")
# cube = cube_list[selected_cube[0]]
# cube.coord('latitude').guess_bounds()
def run_all_plots():
#************************************************************************
# Global Anomaly map
cube = read_map(
DATALOC + "tco_omimls_anomaly_{}.txt".format(settings.YEAR),
"TCO_anom", "DU")
bounds = np.array([-100, -4, -3, -2, -1, 0, 1, 2, 3, 4, 100])
bounds = np.array(
[-100, -1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1.0, 100])
utils.plot_smooth_map_iris(settings.IMAGELOC +
"TCO_anomaly_{}".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2004-08 (DU)",
contour=True)
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_TCO_anomaly_{}".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2004-08 (DU)",
figtext="(aa) OMI/MLS Tropospheric Column Ozone",
contour=True)
#************************************************************************
# Global Trend map
cube = read_map(DATALOC + "tco_omimls_trends_{}.txt".format(settings.YEAR),
"TCO_trend", "DU")
bounds = np.array([-100, -3, -2, -1, -0.5, 0, 0.5, 1, 2, 3, 100])
# utils.plot_smooth_map_iris(settings.IMAGELOC + "TCO_trend_{}".format(settings.YEAR), cube, settings.COLOURMAP_DICT["composition"], bounds, "(DU per decade)")
sig_lats, sig_lons, sig_data = read_significance(
DATALOC + "tco_omimls_trends_{}.txt".format(settings.YEAR), DATALOC +
"tco_omimls_trends_95pct_signficance_{}.txt".format(settings.YEAR))
# use local adapted routine.
plot_smooth_map_iris(settings.IMAGELOC +
"TCO_trend_significance_{}".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["composition"],
bounds,
"(DU per decade)",
scatter=(sig_lons, sig_lats, sig_data))
#************************************************************************
# Timeseries
# monthly_global, annual_global, monthly_NH, annual_NH, monthly_SH, annual_SH = read_data(DATALOC + "OMI_MLS_trop_ozone_burden_2004_2015.txt")
monthly_global = read_data(
DATALOC + "BAMS_SOTC_TROPOSPHERIC_OZONE_TG_60Sto60N_{}.txt".format(
settings.YEAR), "mg")
monthly_SH = read_data(
DATALOC +
"BAMS_SOTC_TROPOSPHERIC_OZONE_TG_0to60S_{}.txt".format(settings.YEAR),
"msh")
monthly_NH = read_data(
DATALOC +
"BAMS_SOTC_TROPOSPHERIC_OZONE_TG_0to60N_{}.txt".format(settings.YEAR),
"mnh")
annual_global = read_data(
DATALOC +
"BAMS_SOTC_TROPOSPHERIC_OZONE_TG_RUNNING_MEAN_60Sto60N_{}.txt".format(
settings.YEAR), "ag")
annual_SH = read_data(
DATALOC +
"BAMS_SOTC_TROPOSPHERIC_OZONE_TG_RUNNING_MEAN_0to60S_{}.txt".format(
settings.YEAR), "ash")
annual_NH = read_data(
DATALOC +
"BAMS_SOTC_TROPOSPHERIC_OZONE_TG_RUNNING_MEAN_0to60N_{}.txt".format(
settings.YEAR), "anh")
minor_tick_interval = 1
minorLocator = MultipleLocator(minor_tick_interval)
COLOURS = settings.COLOURS["composition"]
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.14, 0.07, 0.84, 0.90])
plt.plot(monthly_global.times,
monthly_global.data,
'k',
ls='-',
label=r"1.79$\pm$0.47 Tg yr$^{-1}$",
lw=LW)
plt.plot(annual_global.times, annual_global.data, 'k', ls='--', lw=LW)
plt.text(2004,
250,
"60" + r'$^{\circ}$' + "S - 60" + r'$^{\circ}$' + "N",
va='center',
color='k',
fontsize=settings.FONTSIZE)
plt.plot(monthly_NH.times,
monthly_NH.data,
'r',
ls='-',
label=r"0.89$\pm$0.39 Tg yr$^{-1}$",
lw=LW)
plt.plot(annual_NH.times, annual_NH.data, 'r', ls='--', lw=LW)
plt.text(2004,
180,
"0" + r'$^{\circ}$' + " - 60" + r'$^{\circ}$' + "N",
va='center',
color='r',
fontsize=settings.FONTSIZE)
plt.plot(monthly_SH.times,
monthly_SH.data,
'c',
ls='-',
label=r"0.90$\pm$0.46 Tg yr$^{-1}$",
lw=LW)
plt.plot(annual_SH.times, annual_SH.data, 'c', ls='--', lw=LW)
plt.text(2004,
110,
"60" + r'$^{\circ}$' + "S - 0" + r'$^{\circ}$' + "",
va='center',
color='c',
fontsize=settings.FONTSIZE)
ax.legend(loc=LEGEND_LOC,
ncol=1,
frameon=False,
prop={'size': settings.LEGEND_FONTSIZE},
labelspacing=0.1,
columnspacing=0.5)
# prettify
ax.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax)
fig.text(0.04,
0.5,
"Tropospheric Ozone\n(Tg)",
va='center',
rotation='vertical',
ha="center",
fontsize=settings.FONTSIZE)
plt.xlim([2003.5, int(settings.YEAR) + 1.5])
plt.ylim([90, 340])
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "TCO_ts{}".format(settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries plot
if True:
monthly = read_data(DATALOC + "monthlist", "AOD monthly")
annual = read_data(DATALOC + "yearlist", "AOD annual")
minor_tick_interval = 1
minorLocator = MultipleLocator(minor_tick_interval)
COLOURS = settings.COLOURS["composition"]
fig = plt.figure(figsize=(8, 5))
ax = plt.axes([0.13, 0.07, 0.85, 0.86])
plt.plot(monthly.times,
monthly.data,
COLOURS[monthly.name],
ls='-',
label=monthly.name,
lw=LW)
plt.plot(annual.times,
annual.data,
COLOURS[annual.name],
ls='-',
label=annual.name,
lw=LW)
ax.legend(loc=LEGEND_LOC,
ncol=1,
frameon=False,
prop={'size': settings.LEGEND_FONTSIZE},
labelspacing=0.1,
columnspacing=0.5)
# prettify
ax.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax)
fig.text(0.03,
0.5,
"AOD",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
plt.xlim([2002.5, int(settings.YEAR) + 1.5])
plt.ylim([0.09, 0.21])
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "ASL_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Global maps
# total
if True:
total = iris.load(DATALOC + "diffAOD.nc") #.format(settings.YEAR))
bounds = np.array([
-10, -0.15, -0.10, -0.05, -0.025, -0.01, 0.01, 0.025, 0.05, 0.10,
0.15, 10
])
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_total_anomalies_{}".format(settings.YEAR),
total[0][0], settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 2003-{} (AOD)".format(settings.YEAR[2:]))
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_total_anomalies_{}".format(settings.YEAR),
total[0][0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1),
figtext="(x) Total Aerosol")
# extreme AOD
if True:
extreme = iris.load(DATALOC + "NB999.nc") #.format(settings.YEAR))
bounds = np.array([0, 2.5, 5, 10, 20, 30, 40, 90])
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_extreme_days_{}".format(settings.YEAR),
extreme[0][0], plt.cm.YlOrBr, bounds,
"Number of days with AOD above the 99.9th percentile (days)")
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_extreme_days_{}".format(settings.YEAR),
extreme[0][0],
plt.cm.YlOrBr,
bounds,
"Number of days with AOD above the 99.9th percentile (days)",
figtext="(y) Extreme Aerosol Days")
# Biomass Burning
if False:
BB = iris.load(DATALOC + "diffBB.nc") #.format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_BB_anomalies_{}".format(settings.YEAR),
BB[0], settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1))
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_BB_anomalies_{}".format(settings.YEAR),
BB[0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1),
figtext="(y) Black Carbon & Organic Matter Aerosol")
# Dust
if False:
dust = iris.load(DATALOC + "diffDU.nc") #.format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_dust_anomalies_{}".format(settings.YEAR),
dust[0], settings.COLOURMAP_DICT["composition"], bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1))
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_ASL_dust_anomalies_{}".format(settings.YEAR),
dust[0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-20{} (AOD)".format(
int(settings.YEAR[2:]) - 1),
figtext="(z) Dust Aerosol")
#************************************************************************
# Total, Trend and Event map
if False:
# as one colorbar per map, don't use the utils Iris routine
fig = plt.figure(figsize=(8, 15))
#total = iris.load(DATALOC + "Aerosol_Fig2a_Total_Averages.nc")
#trend = iris.load(DATALOC + "Aerosol_Fig2b_Total_Trends.nc")
#event = iris.load(DATALOC + "Aerosol_Fig2c_ExtremeDayCounts_{}.nc".format(settings.YEAR))
total = iris.load(DATALOC + "meanAOD.nc")
trend = iris.load(DATALOC + "trend.nc")
number = iris.load(DATALOC + "numbermonth.nc")
bounds_a = np.array(
[0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 1.0])
bounds_b = np.array([
-10, -0.010, -0.005, -0.003, -0.002, -0.001, 0.001, 0.002, 0.003,
0.005, 0.010, 10
])
bounds_c = np.array([0, 1, 2, 3, 4, 5, 6, 7, 10])
all_cubes = [total, trend, number]
all_bounds = [bounds_a, bounds_b, bounds_c]
# do colourmaps by hand
cmap = [
plt.cm.YlOrBr, settings.COLOURMAP_DICT["composition"],
plt.cm.YlOrBr
]
PLOTLABELS = ["(a)", "(b)", "(c)"]
cb_label = ["AOD", "AOD yr" + r'$^{-1}$', "Months"]
# spin through axes
for a in range(3):
norm = mpl.cm.colors.BoundaryNorm(all_bounds[a], cmap[a].N)
ax = plt.subplot(3, 1, a + 1, projection=cartopy.crs.Robinson())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ext = ax.get_extent() # save the original extent
mesh = iris.plot.pcolormesh(all_cubes[a][0],
cmap=cmap[a],
norm=norm,
axes=ax)
ax.set_extent(
ext, ax.projection) # fix the extent change from colormesh
ax.text(0.0,
1.0,
PLOTLABELS[a],
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
# sort the colourbar
cb = fig.colorbar(mesh, ax=ax, orientation='horizontal', \
ticks=all_bounds[a][1:-1], label=cb_label[a], drawedges=True, pad=0.05, fraction=0.07, aspect=30)
if a == 1:
cb.set_ticklabels(
["{:6.3f}".format(b) for b in all_bounds[a][1:-1]])
else:
cb.set_ticklabels(
["{:g}".format(b) for b in all_bounds[a][1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
plt.savefig(settings.IMAGELOC + "ASL_Trends{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Total, and two trends maps
if True:
# as one colorbar per map, don't use the utils Iris routine
fig = plt.figure(figsize=(8, 15))
total = iris.load(DATALOC + "AOD2003-2009.nc")[0]
trend1 = iris.load(DATALOC + "trend2003-2019.nc")[0]
trend1.data = np.ma.masked_where(trend1.data == -99, trend1.data)
trend2 = iris.load(DATALOC + "medianaod12-19sig.nc")[0]
trend2.data = np.ma.masked_where(trend2.data == -99, trend2.data)
bounds_a = np.array(
[0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 1.0])
bounds_b = np.array([
-10, -0.020, -0.010, -0.006, -0.004, -0.002, 0.002, 0.004, 0.006,
0.010, 0.020, 10
])
bounds_c = np.array([
-10, -0.020, -0.010, -0.006, -0.004, -0.002, 0.002, 0.004, 0.006,
0.010, 0.020, 10
])
all_cubes = [total, trend1, trend2]
all_bounds = [bounds_a, bounds_b, bounds_c]
# do colourmaps by hand
cmap = [
plt.cm.YlOrBr, settings.COLOURMAP_DICT["composition"],
settings.COLOURMAP_DICT["composition"]
]
PLOTLABELS = [
"(a) Mean 2003-19", "(b) Trend 2003-19", "(c) Trend 2012-19"
]
cb_label = ["AOD", "AOD yr" + r'$^{-1}$', "AOD yr" + r'$^{-1}$']
# spin through axes
for a in range(3):
norm = mpl.cm.colors.BoundaryNorm(all_bounds[a], cmap[a].N)
ax = plt.subplot(3, 1, a + 1, projection=cartopy.crs.Robinson())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ext = ax.get_extent() # save the original extent
cube = all_cubes[a][0]
if settings.OUTFMT in [".eps", ".pdf"]:
if cube.coord("latitude").points.shape[0] > 180 or cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print(
"Regridding cube for {} output to {} degree resolution"
.format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(cube.data.shape))
plot_cube = utils.regrid_cube(cube, regrid_size,
regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
else:
plot_cube = copy.deepcopy(cube)
else:
plot_cube = copy.deepcopy(cube)
mesh = iris.plot.pcolormesh(plot_cube,
cmap=cmap[a],
norm=norm,
axes=ax)
ax.set_extent(
ext, ax.projection) # fix the extent change from colormesh
ax.text(0.0,
1.0,
PLOTLABELS[a],
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
# sort the colourbar
cb = fig.colorbar(mesh, ax=ax, orientation='horizontal', \
ticks=all_bounds[a][1:-1], label=cb_label[a], drawedges=True, pad=0.05, fraction=0.07, aspect=30)
if a == 1:
cb.set_ticklabels(
["{:6.3f}".format(b) for b in all_bounds[a][1:-1]])
else:
cb.set_ticklabels(
["{:g}".format(b) for b in all_bounds[a][1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
plt.savefig(settings.IMAGELOC + "ASL_Trends{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Forcing map
if True:
bounds = np.array([
-10, -1.4, -1.0, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 1.0, 1.4, 10
])
RFari = iris.load(
DATALOC + "aerorf_camsra_rfari_2003-{}.nc".format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_RFari_anomalies_{}".format(settings.YEAR),
RFari[0][0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-{}".format(settings.YEAR),
figtext="(a) CAMSRA: RFari (SW). Mean -0.56" + r'$\pm$' +
"0.16 Wm" + r'$^{-1}$')
RFaci = iris.load(
DATALOC + "aerorf_camsra_rfaci_2003-{}.nc".format(settings.YEAR))
utils.plot_smooth_map_iris(
settings.IMAGELOC + "ASL_RFaci_anomalies_{}".format(settings.YEAR),
RFaci[0][0],
settings.COLOURMAP_DICT["composition"],
bounds,
"Anomalies from 2003-{}".format(settings.YEAR),
figtext="(c) CAMSRA: RFaci (SW). Mean -0.80" + r'$\pm$' +
"0.53 Wm" + r'$^{-1}$')
# and timeseries
cubelist = iris.load(
DATALOC +
"aerorf_camsra_timeseries_2003-{}.nc".format(settings.YEAR))
LABELS = {"RFari": "(b)", "RFaci": "(d)"}
ERRORS = {"RFari": 0.286, "RFaci": 0.667}
for cube in cubelist:
name = str(cube.var_name)
name = name[:2].upper() + name[2:]
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.13, 0.10, 0.8, 0.86])
iris.plot.plot(cube, 'k', label=cube.var_name, lw=LW)
timeUnits = cube.coord("time").units
dt_time = timeUnits.num2date(cube.coord("time").points)
upper = cube.data * (1 + ERRORS[name])
lower = cube.data * (1 - ERRORS[name])
ax.fill_between(dt_time, upper, lower, color="0.7", label=None)
# prettify
ax.set_ylabel("{} (W m".format(name) + r'$^{-2}$' + ")",
fontsize=settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.yaxis.set_ticks_position('left')
utils.thicken_panel_border(ax)
minorLocator = MultipleLocator(365.242199) # in days since
ax.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax)
# set labels
if cube.var_name == "rfari":
ax.text(0.02, 0.9, "(b)")
ax.set_ylim([-0.8, -0.3])
else:
ax.text(0.02, 0.9, "(d")
ax.set_ylim([-1.6, -0.2])
ax.text(0.02,
0.9,
LABELS[name],
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
plt.savefig(settings.IMAGELOC +
"ASL_ts_{}{}".format(cube.var_name, settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries figure (2 panels)
if True:
for region in ["global"]:
if region == "global":
raobcore, rich, ratpac, UAH, rss, era5, merra, jra = \
read_csv(DATALOC + "SotC_AnnTemps_2020_0520_LTTGL.csv")
plt.clf()
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True)
# sondes
utils.plot_ts_panel(ax1, [raobcore, rich, ratpac], "-", "temperature", loc=LEGEND_LOC)
# satellites
utils.plot_ts_panel(ax2, [UAH, rss], "-", "temperature", loc=LEGEND_LOC)
ax2.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE)
# reanalyses
if region == "global":
# jra_actuals, jra_anoms = utils.read_jra55(settings.REANALYSISLOC + "JRA-55_MSUch2LT_global_ts.txt", "temperature")
# merra_actuals, merra_anoms = utils.read_merra_LT_LS(settings.REANALYSISLOC + "MERRA2_MSU_Tanom_ann_{}.dat".format(settings.YEAR), LT=True)
# utils.plot_ts_panel(ax3, [erai, era5, jra_anoms, merra_anoms], "-", "temperature", loc=LEGEND_LOC)
twenty_cr_actuals = utils.read_20cr(settings.REANALYSISLOC + "tlt.global.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(twenty_cr_actuals, 1981, 2010)
utils.plot_ts_panel(ax3, [era5, jra, merra], "-", "temperature", loc=LEGEND_LOC)
else:
utils.plot_ts_panel(ax3, [era5], "-", "temperature", loc=LEGEND_LOC)
# sort formatting
plt.xlim([raobcore.times[0]-1, raobcore.times[-1]+1])
ax1.set_ylim([-0.89, 0.89])
ax2.set_ylim([-0.89, 0.89])
ax3.set_ylim([-0.89, 0.89])
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax3.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# sort labelling
ax1.text(0.02, 0.9, "(a) Radiosondes", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02, 0.9, "(b) Satellites", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02, 0.9, "(c) Reanalyses", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE)
fig.subplots_adjust(right=0.98, top=0.98, bottom=0.05, hspace=0.001)
plt.savefig(settings.IMAGELOC+"LTT_ts_{}{}".format(region, settings.OUTFMT))
plt.close()
# #************************************************************************
# # Land Fraction - 2018
# high, low = read_hilo(DATALOC + "high_low.dat")
# fig, ax1 = plt.subplots(1, figsize=(8, 5))
# ax1.plot(high.times, high.data, c="r", ls="-", lw=2, label=high.name)
# ax1.plot(low.times, low.data, c="b", ls="-", lw=2, label=low.name)
# ax1.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.FONTSIZE})
# ax1.set_xlim([1978, int(settings.YEAR)+2])
# ax1.set_ylabel("Percentage of Global Area (%)", fontsize=settings.FONTSIZE)
# for tick in ax1.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for tick in ax1.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# utils.thicken_panel_border(ax1)
# plt.savefig(settings.IMAGELOC+"LTT_land_area_ts{}".format(settings.OUTFMT))
# plt.close()
#************************************************************************
# Read in ERA5 anomalies
if False:
cube_list = iris.load(DATALOC + "2019TLTAnom.nc")
names = np.array([c.var_name for c in cube_list])
loc, = np.where(names == "tltAnnualAnom")[0]
cube = cube_list[loc]
bounds = np.array([-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100])
bounds = np.array([-100, -1.6, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.6, 100])
bounds = np.array([-100, -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0, 100])
cmap = settings.COLOURMAP_DICT["temperature"]
utils.plot_smooth_map_iris(settings.IMAGELOC + "LTT_{}_anoms_era5".format(settings.YEAR), cube, cmap, \
bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LTT_{}_anoms_era5".format(settings.YEAR), cube, \
cmap, bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \
figtext="(e) Lower Tropospheric Temperature")
#************************************************************************
# Read in ERA5 anomalies with record pixels
if True:
cube_list = iris.load(DATALOC + "2019TLTAnom_warmest.nc")
names = np.array([c.var_name for c in cube_list])
loc, = np.where(names == "tltAnnualAnom")[0]
cube = cube_list[loc]
loc, = np.where(names == "recordMask")[0]
record = cube_list[loc]
record.coord("latitude").guess_bounds()
record.coord("longitude").guess_bounds()
lats, lons, data = [], [], []
for t, lat in enumerate(record.coord("latitude").points):
for n, lon in enumerate(record.coord("longitude").points):
if record.data[t, n] == 1:
data += [cube.data[t, n]]
lats += [np.mean(record.coord("latitude").bounds[t])]
lons += [np.mean(record.coord("longitude").bounds[n])]
bounds = np.array([-100, -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0, 100])
cmap = settings.COLOURMAP_DICT["temperature"]
utils.plot_smooth_map_iris(settings.IMAGELOC + "LTT_{}_anoms_era5".format(settings.YEAR), cube, cmap, \
bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5", \
scatter=(lons, lats, data), smarker="dots")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LTT_{}_anoms_era5".format(settings.YEAR), cube, \
cmap, bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \
figtext="(e) Lower Tropospheric Temperature", \
scatter=(lons, lats, data), smarker="dots")
#************************************************************************
# ERA-I Hovmuller
# times, latitudes, data = utils.erai_2dts_read(settings.REANALYSISLOC, "ltt")
# bounds = np.array([-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100])
# bounds = np.array([-100, -1.6, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.6, 100])
# # sort the time axis
# start = (times[0] - 101)/10000.
# end = (times[-1] - 1201)/10000.
# year = start
# new_times = []
# months = (np.arange(12))/12.
# while year <= end:
# new_times += [np.array([year for i in range(12)]) + months]
# year += 1
# new_times = np.array(new_times).reshape(-1)
# # sort the climatology to 1981-2010 (monthly!)
# # reshape to deal with monthly
# data = data.reshape(-1, 12, data.shape[-1])
# new_times = new_times.reshape(-1, 12)
# # extract climatology period
# start_loc, = np.where(new_times[:, 0] == CLIMSTART)
# end_loc, = np.where(new_times[:, 0] == CLIMEND)
# clim_data = data[start_loc[0]:end_loc[0] + 1, :, :]
# climatology = np.mean(clim_data, axis=0)
# # make anomalies
# data = np.array([data[i, :, :] - climatology for i in range(data.shape[0])])
# # return to original shapes
# data = data.reshape(-1, data.shape[-1])
# new_times = new_times.reshape(-1)
# utils.plot_hovmuller(settings.IMAGELOC + "LTT_hovmuller_erai", new_times, latitudes, data.T, \
# settings.COLOURMAP_DICT["temperature"], bounds, \
# "Anomaly ("+r'$^{\circ}$'+"C)", cosine=True)
# version with MEI on top
# mei = read_mei(os.path.join(DATALOC, "MEI.dat"))
# utils.plot_hovmuller(settings.IMAGELOC + "LTT_hovmuller_era_MEI", new_times, latitudes, data.T, \
# settings.COLOURMAP_DICT["temperature"], bounds, \
# "Anomaly ("+r'$^{\circ}$'+"C)", cosine=True, extra_ts=mei)
return # run_all_plots
def run_all_plots():
if False:
#************************************************************************
# Filled Timeseries
#************************************************************************
moderate, severe, extreme = read_data(DATALOC +
"dry_areas_bams_2018_series.csv")
plt.clf()
# main panel
ax1 = plt.axes([0.1, 0.1, 0.85, 0.8])
ax1.fill_between(moderate.times,
0,
moderate.data,
color="yellow",
label="Moderate (<-2)")
ax1.fill_between(severe.times,
0,
severe.data,
color="orange",
label="Severe (<-3)")
ax1.fill_between(extreme.times,
0,
extreme.data,
color="red",
label="Extreme (<-4)")
ax1.set_ylabel("% Area", fontsize=settings.FONTSIZE)
ax1.legend(loc="upper left",
ncol=1,
frameon=False,
prop={'size': settings.LEGEND_FONTSIZE},
labelspacing=0.1,
columnspacing=0.5)
utils.thicken_panel_border(ax1)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
minor_tick_interval = 1
minorLocator = MultipleLocator(minor_tick_interval)
ax1.xaxis.set_minor_locator(minorLocator)
ax1.set_xlim([1950, int(settings.YEAR) + 2])
ax1.set_ylim([None, 44])
# inset panel
ax2 = plt.axes([0.7, 0.7, 0.29, 0.29])
ax2.fill_between(moderate.times[-12:],
0,
moderate.data[-12:],
color="yellow",
label="Moderate (<-2)")
ax2.fill_between(severe.times[-12:],
0,
severe.data[-12:],
color="orange",
label="Severe (<-3)")
ax2.fill_between(extreme.times[-12:],
0,
extreme.data[-12:],
color="red",
label="Extreme (<-4)")
utils.thicken_panel_border(ax2)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE * 0.7)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE * 0.7)
ax2.set_xlim([int(settings.YEAR), int(settings.YEAR) + 1])
ax2.set_xticks(np.arange(2017, 2018, 1. / 12.))
ax2.set_xticklabels(
["J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"])
ax2.set_ylim([None, 34])
ax2.set_xlim(
[float(settings.YEAR) - 0.09,
float(settings.YEAR) + 0.99])
ax2.text(int(settings.YEAR) + 0.1, 27, settings.YEAR)
plt.savefig(settings.IMAGELOC + "DGT_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Anomaly Map
#************************************************************************
# Maps for selected years
if True:
cube_list = iris.load(
DATALOC +
"BAMS.scPDSI.cru.4.04.2020.early.GLOBAL.annual.mean.2018.2019.nc")
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -4, -3, -2, -1, 0, 1, 2, 3, 4, 100]
# select the year - plot 2019 (cube[1])
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"DGT_{}_rel_1901-{}".format(settings.YEAR, settings.YEAR),
cube[1],
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Categories relative to 1901-{} (self-calibrating PDSI)".format(
settings.YEAR),
cb_extra=["Dry", "Wet"],
contour=True)
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_DGT_{}_rel_1901-{}".format(settings.YEAR, settings.YEAR),
cube[1],
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Categories relative to 1901-{} (self-calibrating PDSI)".format(
settings.YEAR),
figtext="(t) Drought (self-calibrating PDSI)",
cb_extra=["Dry", "Wet"],
contour=True,
save_netcdf_filename="{}DGT_for_NOAA_{}.nc".format(
DATALOC, dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y")))
# plot 2019-2018
newcube = cube[1] - cube[0]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "DGT_{}-{}_rel_1901-{}".format(
settings.YEAR,
int(settings.YEAR) - 1, settings.YEAR),
newcube,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Change in Categories from {} to {} (self-calibrating PDSI)".
format(int(settings.YEAR) - 1, settings.YEAR),
cb_extra=["Dry", "Wet"])
if False:
# from 2015 report
# select the last year
cube_list = []
YEARS = [1982, 1997]
for year in YEARS:
loc, = np.where(cube.coord("time").points == year)
cube_list += [cube[loc][0]]
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"DGR_{}_{}_{}".format(settings.YEAR, YEARS[0], YEARS[1]),
cube_list,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Categories relative to 1901-2015 (self-calibrating PSDI)",
shape=(2, 1),
title=[str(y) for y in YEARS],
figtext=["(a)", "(b)"])
# select the last year
cube_list = []
YEARS = [1985, 1987]
for year in YEARS:
loc, = np.where(cube.coord("time").points == year)
cube_list += [cube[loc][0]]
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"DGR_{}_{}_{}".format(settings.YEAR, YEARS[0], YEARS[1]),
cube_list,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Categories relative to 1901-2015 (self-calibrating PSDI)",
shape=(2, 1),
title=[str(y) for y in YEARS],
figtext=["(a)", "(b)"])
return # run_all_plots
def run_all_plots():
#************************************************************************
# Read in Australian # Data
# aus_lat, aus_lon, aus_anom_8110, aus_trend_79_pres, aus_years, aus_anomalies, aus_clim_8110 = \
# read_australia("{}/u_Extracted_ANN_{}_v2.csv".format(DATALOC, settings.YEAR), start_year = 1979)
# # read_australia("{}/u_Extracted_ANN_1974_{}_RD.csv".format(DATALOC, settings.YEAR))
# Timeseries figure
if True:
# ERA data
era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read(
settings.REANALYSISLOC, "wnd", annual=True)
land_era5_clim, land_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_land, CLIMSTART, 2010)
land_merra_anoms = utils.read_merra(settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), \
"wind", "L", anomalies=True)
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_ws10m_globalland_ts.txt",
"windspeed")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "wspd10m.land.txt", "wind speed")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, CLIMSTART, 2010)
# Plot timeseries figure
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,
figsize=(8, 13),
sharex=True)
print("{} {} {} {} {}".format("name", "mean", "anomaly", "trend/dec",
"N station"))
for region in all_regions:
if region.name == "Australia":
# use the Australian data
years = aus_years
anomalies = aus_anomalies
print(anomalies)
region.nstat = len(aus_lat)
region.slope = np.mean(
aus_trend_79_pres
) # just do the mean of all station slopes
region.mean = np.mean(aus_clim_8110)
else:
# Read in the HadISD annual anomalies
years, anomalies, m3, m10 = read_hadisd_annual_anomalies(
region)
# Print data for table
print("{} {} {} {} {}".format(region.name, region.mean,
anomalies[-1], region.slope * 10.,
region.nstat))
order = anomalies.argsort()
ranks = order.argsort() + 1
print("{} highest {} lowest".format(
len(ranks) - ranks[-1], ranks[-1]))
# plot data
if region.fname == "GlobalNoOz":
ax1.plot(years,
anomalies,
c=region.color,
label=region.name,
lw=3,
zorder=10)
else:
ax1.plot(years,
anomalies,
c=region.color,
label=region.name,
lw=2)
if region.fname == "GlobalNoOz":
ax3.plot(years, m3, c=region.color, lw=3, zorder=10)
ax4.plot(years, m10, c=region.color, lw=3)
elif region.name != "Australia":
ax3.plot(years, m3, c=region.color, lw=2)
ax4.plot(years, m10, c=region.color, lw=2)
# plot reanalyses separately
ax2.plot(land_era5_anoms.times, land_era5_anoms.data, c=settings.COLOURS["circulation"]["ERA5"], \
label="ERA5 (land only)", lw=2)
ax2.plot(land_merra_anoms.times, land_merra_anoms.data, c=settings.COLOURS["circulation"]["MERRA-2"], \
label="MERRA-2 (land only)", lw=2)
# ax2.plot(jra_anoms.times, jra_anoms.data, c=settings.COLOURS["circulation"]["JRA-55"], \
# label="JRA-55 (land only)", lw=2)
ax2.plot(twenty_cr_anoms.times, twenty_cr_anoms.data, c=settings.COLOURS["circulation"]["20CRv3"], \
label="20CRv3 (land only)", lw=2)
# finish off plot
ax1.axhline(0, c='0.5', ls='--')
ax2.axhline(0, c='0.5', ls='--')
ax1.text(0.02,
0.9,
"(a) In Situ - all Speeds",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Reanalyses - all Speeds",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02, 0.9, "(c) In Situ >3 m s"+r'$^{-1}$'+" Winds", transform=ax3.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax4.text(0.02, 0.9, "(d) In Situ >10 m s"+r'$^{-1}$'+" Winds", transform=ax4.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
fig.text(0.02, 0.72, "Wind Anomaly (m s"+r'$^{-1}$'+")", va='center', rotation='vertical', \
fontsize=settings.LABEL_FONTSIZE)
fig.text(0.02, 0.3, "Wind Frequency (% yr"+r'$^{-1}$'+")", va='center', rotation='vertical', \
fontsize=settings.LABEL_FONTSIZE)
ax1.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.9))
ax2.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.9))
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
fig.subplots_adjust(right=0.96, top=0.99, bottom=0.03, hspace=0.001)
for tick in ax4.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.xlim([1970, int(settings.YEAR) + 1])
ax1.set_ylim([-0.39, 1.0])
ax2.set_ylim([-0.39, 1.0])
ax3.set_ylim([23, 68])
ax4.set_ylim([0, 6.5])
minorLocator = MultipleLocator(1)
for ax in [ax1, ax2, ax3, ax4]:
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.yaxis.set_ticks_position('left')
plt.savefig(settings.IMAGELOC +
"WND_land_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# HadISD Anomaly figure
if True:
# Read in HadISD station anomalies
stn_id, hadisd_lons, hadisd_lats, mean8110, hadisd_anomaly_8110, hadisd_trend_79_pres = read_hadisd_global_summary(
)
# combine together
lats = hadisd_lats
lons = hadisd_lons
anom = hadisd_anomaly_8110
trend = hadisd_trend_79_pres * 10
# lats = np.append(hadisd_lats, aus_lat)
# lons = np.append(hadisd_lons, aus_lon)
# anom = combine_arrays((hadisd_anomaly_8110, aus_anom_8110))
# trend = combine_arrays((hadisd_trend_79_pres, aus_trend_79_pres)) * 10.
# bounds = [-100, -0.8, -0.4, -0.2, -0.1, 0, 0.1, 0.2, 0.4, 0.8, 100]
bounds = [-100, -0.4, -0.2, -0.1, -0.05, 0, 0.05, 0.1, 0.2, 0.4, 100]
utils.scatter_plot_map(settings.IMAGELOC + "WND_{}_obs_trend".format(settings.YEAR), trend, \
lons, lats, settings.COLOURMAP_DICT["circulation_r"], bounds, "Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)')
bounds = [-100, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 100]
utils.scatter_plot_map(settings.IMAGELOC + "WND_{}_obs_anomaly".format(settings.YEAR), anom, \
lons, lats, settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)')
total = float(len(anom.compressed()))
pos, = np.ma.where(anom > 0)
neg, = np.ma.where(anom < 0)
print("Anomalies: positive {:5.3f} negative {:5.3f}".format(
len(pos) / total,
len(neg) / total))
pos, = np.ma.where(anom > 0.5)
neg, = np.ma.where(anom < -0.5)
print("Anomalies: positive {:5.3f} negative {:5.3f} (than 0.5)".format(
len(pos) / total,
len(neg) / total))
pos, = np.ma.where(anom > 1.0)
neg, = np.ma.where(anom < -1.0)
print("Anomalies: positive {:5.3f} negative {:5.3f} (than 1.0)".format(
len(pos) / total,
len(neg) / total))
total = float(len(trend.compressed()))
pos, = np.ma.where(trend > 0)
neg, = np.ma.where(trend < 0)
print("Trends: positive {:5.3f} negative {:5.3f}".format(
len(pos) / total,
len(neg) / total))
#************************************************************************
# ERA5 + HadISD Anomaly figure
if True:
# Read in ERA anomalies
cube_list = iris.load(settings.REANALYSISLOC +
"era5_ws10_{}01-{}12_ann_ano.nc".format(
settings.YEAR, settings.YEAR))
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_era5_obs_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', scatter = (lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_era5_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)')
#************************************************************************
# MERRA Anomaly figure
if True:
anoms = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"MERRA2_wind_anomaly_map_{}".format(settings.YEAR))
bounds = [-40, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 40]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_merra2_anomaly", anoms, settings.COLOURMAP_DICT["circulation_r"], bounds,\
"Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")")
#************************************************************************
# MERRA + HadISD Anomaly figure
if True:
# Read in MERRA anomalies
cube_list = iris.load(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom_{}.nc".format(settings.YEAR),
"10m Wind Speed Anomaly (1981-2010)")
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra_obs_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', scatter=(lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_WND_{}_merra_obs_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', figtext="(v) Surface Winds", scatter=(lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)')
#************************************************************************
# MERRA/RSS ocean + HadISD Anomaly figure
if True:
# Read in MERRA/RSS trends
anomalies = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"Merged_wind_anomaly_map_{}".format(settings.YEAR))
bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_anomaly".format(settings.YEAR), \
anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$)')
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_obs_anomaly".format(settings.YEAR), \
anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$)',
scatter=(lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_WND_{}_merra-rss_obs_anomaly".format(settings.YEAR), \
anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$)', figtext="(v) Surface Winds", scatter=(lons, lats, anom))
#************************************************************************
# Ocean timeseries
if True:
satellite = read_ts_cube(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}.nc".format(settings.YEAR),
"RSS_wind_global_annual_anom_ts", "Satellite MW Radiometers")
satellite_clim, satellite_anom = utils.calculate_climatology_and_anomalies_1d(
satellite, 1988, 2010)
# print("NO IN SITU OCEAN DATA FOR 2016, using 2015 data")
# nocs = read_ts_cube(DATALOC + "NOCSv2.0_oceanW_5by5_8110anoms_areaTS_FEB2016.nc", "Globally Average 70S-70N", "NOCSv2.0")
# WASwind = read_ts_cube(DATALOC + "waswind_v1_0_1.monthly_areaTS_19502011.nc","Globally Averaged Anomalies 70S-70N", "WASwind")
# print("FIXING WASWIND TIMES - DATAFILE HAS WRONG DESCRIPTOR")
# WASwind.times = WASwind.times - (1973-1950)
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_ws10m_globalocean_ts.txt", "wind")
era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read(
settings.REANALYSISLOC, "wnd", annual=True)
ocean_era5_clim, ocean_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_ocean, 1988, 2010)
merra_anoms = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR),
"wind",
"O",
anomalies=True)
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "wspd10m.ocean.txt", "wind speed")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1988, 2010)
fig, (ax1) = plt.subplots(1, figsize=(8, 5), sharex=True)
# Satellite
# utils.plot_ts_panel(ax1, [satellite_anom], "-", "circulation", loc=LEGEND_LOC, bbox=BBOX)
# In Situ
# utils.plot_ts_panel(ax2, [nocs, WASwind], "-", "circulation", loc=LEGEND_LOC, bbox=BBOX)
# ax2.set_ylabel("Anomaly (m s"+r'$^{-1}$'+")", fontsize = settings.FONTSIZE)
# Reanalyses & Satellite single panel
satellite_anom.lw = 4
satellite_anom.zorder = 10
utils.plot_ts_panel(
ax1,
[satellite_anom, ocean_era5_anoms, merra_anoms, twenty_cr_anoms],
"-",
"circulation",
loc=LEGEND_LOC,
bbox=BBOX)
#*******************
# prettify
ax1.axhline(0, c='0.5', ls='--')
plt.ylabel("Wind Anomaly (m s" + r'$^{-1}$' + ")",
fontsize=settings.LABEL_FONTSIZE)
ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.99))
# sort formatting
plt.xlim([1970, int(settings.YEAR) + 1])
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# for ax in [ax1, ax2, ax3]:
for ax in [ax1]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.set_ylim([-0.28, 0.45])
ax.yaxis.set_ticks([-0.2, 0.0, 0.2, 0.4])
ax.yaxis.set_ticks_position('left')
# sort labelling
ax1.text(0.03,
0.9,
"Satellites & Reanalyses",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
# ax2.text(0.03, 0.9, "(b) In Situ", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE)
# ax3.text(0.03, 0.9, "(b) Reanalyses", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE
fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
plt.savefig(settings.IMAGELOC +
"WND_ocean_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Ocean maps
if True:
anoms = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"RSS_wind_anomaly_map_{}".format(settings.YEAR))
bounds = [-40, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 40]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_rss_anomaly".format(settings.YEAR), anoms, settings.COLOURMAP_DICT["circulation_r"], bounds,\
"Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")")
#************************************************************************
# MERRA/RSS ocean + HadISD Trend figure
if True:
# Read in MERRA/RSS trends
trends = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"Wind_trend_map")
bounds = [-4, -0.8, -0.4, -0.2, -0.1, 0, 0.1, 0.2, 0.4, 0.8, 4]
bounds = [-100, -0.4, -0.2, -0.1, -0.05, 0, 0.05, 0.1, 0.2, 0.4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_trend".format(settings.YEAR), \
trends, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)')
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_obs_trend".format(settings.YEAR), \
trends, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)',
scatter=(lons, lats, trend))
return # run_all_plots
def run_all_plots():
#***************
# Anomaly Scatter map
anomalies = read_lakes(DATALOC +
"DATA_StateOfTheClimate_WaterLevelsAnom_v2.csv")
bounds = [-2, -1, -0.75, -0.50, -0.25, 0, 0.25, 0.50, 0.75, 1, 2]
lons = np.arange(-90, 120, 30)
lats = np.arange(-180, 210, 30)
dummy = np.ma.zeros((len(lats), len(lons)))
dummy.mask = np.ones(dummy.shape)
cube = utils.make_iris_cube_2d(dummy, lats, lons, "blank", "m")
utils.plot_smooth_map_iris(settings.IMAGELOC + "LWL_anomaly", cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1992-2002 (m)", \
scatter=[anomalies[1], anomalies[0], anomalies[2]], figtext="", title="")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LWL_{}_anomaly".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], \
bounds, "Anomalies from 1992-2002 (m)", \
scatter=[anomalies[1], anomalies[0], anomalies[2]], figtext="(m) Lake Water Level", title="")
#***************
# multi-panel time series
indata = read_continuous_csv(
os.path.join(
DATALOC,
"DATA_StateOfTheLakes_WaterLevelAnomTimeSeries_sub_v1.csv"))
names = np.array([d.name for d in indata])
lake_order = [
"Huron", "Superior", "Balkhash", "Tanganyika", "Caspian Sea",
"Large Aral Sea", "Urmia", "Rukwa"
]
# match HUM plot size
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(16, 8), sharex=True)
counter = 0
for row in axes:
for ax in row:
lake, = np.where(names == lake_order[counter])[0]
lake = indata[lake]
utils.thicken_panel_border(ax)
ax.plot(lake.times, lake.data, "k-")
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_major_locator(mdates.YearLocator(10))
ax.xaxis.set_minor_locator(mdates.YearLocator(1))
ax.text(0.1,
0.9,
lake.name,
fontsize=settings.FONTSIZE,
transform=ax.transAxes)
ylim = ax.get_ylim()
ax.set_ylim([ylim[0], ylim[1] * 1.2])
counter += 1
fig.text(0.01,
0.5,
"Lake Level (m)",
rotation="vertical",
va="center",
fontsize=settings.FONTSIZE)
fig.subplots_adjust(left=0.07,
right=0.99,
bottom=0.05,
top=0.99,
hspace=0.001)
plt.savefig(settings.IMAGELOC +
"LWL_ts_{}{}".format(settings.YEAR, settings.OUTFMT))
#***************
# Lake Cutouts
EXTENTS = {
"GL": (86, 45, [-93, -78, 41, 49]),
"RU": (62, 42, [43, 81, 35, 48]),
"AF": (31, -6, [28.5, 33, -10, -3])
}
for region in ["GL", "RU", "AF"]:
if region == "AF":
fig = plt.figure(figsize=(6, 8))
elif region == "RU":
fig = plt.figure(figsize=(18, 8))
elif region == "GL":
fig = plt.figure(figsize=(12, 8))
plt.clf()
ax = plt.axes([0.05, 0.05, 0.9, 0.9],
projection=cartopy.crs.PlateCarree(
central_longitude=EXTENTS[region][0]))
ax.set_extent(EXTENTS[region][2], cartopy.crs.PlateCarree())
ax.add_feature(cartopy.feature.LAND.with_scale('10m'),
zorder=0,
facecolor="0.75",
edgecolor="k")
ax.add_feature(cartopy.feature.OCEAN.with_scale('10m'),
zorder=0,
facecolor="#e0ffff",
edgecolor="k")
ax.add_feature(cartopy.feature.LAKES.with_scale('10m'),
zorder=0,
facecolor="#e0ffff",
edgecolor="k")
ax.add_feature(cartopy.feature.RIVERS.with_scale('10m'),
zorder=0,
edgecolor="#e0ffff")
ax.coastlines(resolution="10m", linewidth=0.5)
# add other features
gl = ax.gridlines(draw_labels=True)
gl.xlabel_style = {'size': settings.FONTSIZE}
gl.ylabel_style = {'size': settings.FONTSIZE}
# add labels
if region == "AF":
ax.text(30,
-5,
"Tanganyika",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
ax.text(32,
-7.5,
"Rukwa",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
elif region == "RU":
ax.text(46,
38,
"Urmia",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
ax.text(52,
44,
"Caspian Sea",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
ax.text(60.5,
45,
"Large Aral Sea",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
ax.text(75,
45.5,
"Balkhash",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
elif region == "GL":
ax.text(-91,
46.2,
"Superior",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
ax.text(-89,
42,
"Michigan",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
ax.text(-84.5,
44.5,
"Huron",
fontsize=settings.FONTSIZE,
transform=cartopy.crs.Geodetic())
plt.savefig(settings.IMAGELOC + "LWL_map_{}".format(region) +
settings.OUTFMT)
plt.close()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries figures
if True:
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True)
UAH, rss, ratpac, raobcore, rich, noaa, jra, merra = read_csv(
DATALOC + "SotC_AnnTemps_2020_0220_LSTGL.csv")
# ssu2, ncar = read_ssu_csv(DATALOC + "2018_LTT_LST_SSU_date0401_SSU.csv")
eral, erao, eralo = read_era5(DATALOC + "ERA5_TLS_GLOBAL")
eralo_ann = utils.Timeseries("ERA5",
np.reshape(eralo.times, [-1, 12])[:, 0],
utils.annual_average(eralo.data))
# Sondes [no RATPAC for 2019]
utils.plot_ts_panel(ax1, [raobcore, rich],
"-",
"temperature",
loc=LEGEND_LOC)
# satellites
utils.plot_ts_panel(ax2, [UAH, noaa, rss],
"-",
"temperature",
loc=LEGEND_LOC)
# reanalyses
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_MSUch4_global_ts.txt",
"temperature")
merra_actuals, merra_anoms = utils.read_merra_LT_LS(
settings.REANALYSISLOC +
"MERRA2_MSU_Tanom_ann_{}.dat".format(settings.YEAR),
LS=True)
utils.plot_ts_panel(ax3, [eralo_ann, jra_anoms, merra_anoms],
"-",
"temperature",
loc=LEGEND_LOC)
# ax3.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE)
# Upper Stratosphere
# utils.plot_ts_panel(ax4, [ssu2, ncar], "-", "temperature", loc=LEGEND_LOC)
# sort formatting
plt.xlim([1957, raobcore.times[-1] + 1])
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax2, ax3]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.set_ylim([-1.2, 2.2])
# sort labelling
ax1.text(0.02, 0.88, "(a) Radiosondes", transform=ax1.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02, 0.88, "(b) Satellites", transform=ax2.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02, 0.88, "(c) Reanalyses", transform=ax3.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
fig.text(0.01,
0.45,
"Anomaly (" + r'$^\circ$' + "C)",
fontsize=settings.FONTSIZE,
rotation="vertical")
fig.subplots_adjust(right=0.98, top=0.98, bottom=0.04, hspace=0.001)
plt.savefig(settings.IMAGELOC + "LST_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Timeseries figures
if True:
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True)
ssu1, ssu1_2, ssu2, ssu2_2, ssu3, ssu3_2 = read_ssu(DATALOC +
"SSU.dat")
# SSU3
utils.plot_ts_panel(ax1, [ssu3, ssu3_2], "-", "temperature", loc="")
# SSU2
utils.plot_ts_panel(ax2, [ssu2, ssu2_2], "-", "temperature", loc="")
# SSU1
utils.plot_ts_panel(ax3, [ssu1, ssu1_2],
"-",
"temperature",
loc=LEGEND_LOC)
# sort formatting
plt.xlim([1957, ssu1.times[-1] + 2])
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax2, ax3]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.set_ylim([-1.3, 2.2])
# sort labelling
ax1.text(0.02, 0.88, "(a) SSU3", transform=ax1.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02, 0.88, "(b) SSU2", transform=ax2.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02, 0.88, "(c) SSU1", transform=ax3.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
fig.text(0.01,
0.45,
"Anomaly (" + r'$^\circ$' + "C)",
fontsize=settings.FONTSIZE,
rotation="vertical")
fig.subplots_adjust(right=0.98, top=0.98, bottom=0.04, hspace=0.001)
plt.savefig(settings.IMAGELOC + "LST_SSU_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Combined Timeseries figures
if True:
fig = plt.figure(figsize=(12, 8))
# manually set up the 10 axes
w = 0.42
h = 0.31
c = 0.51
ax1 = plt.axes([c - w, 0.99 - h, w, h])
ax2 = plt.axes([c, 0.99 - h, w, h])
ax3 = plt.axes([c - w, 0.99 - (2 * h), w, h], sharex=ax1)
ax4 = plt.axes([c, 0.99 - (2 * h), w, h], sharex=ax2)
ax5 = plt.axes([c - w, 0.99 - (3 * h), w, h], sharex=ax1)
ax6 = plt.axes([c, 0.99 - (3 * h), w, h], sharex=ax2)
UAH, rss, ratpac, raobcore, rich, noaa, jra, merra = read_csv(
DATALOC + "SotC_AnnTemps_2020_0220_LSTGL.csv")
# ssu2, ncar = read_ssu_csv(DATALOC + "2018_LTT_LST_SSU_date0401_SSU.csv")
eral, erao, eralo = read_era5(DATALOC + "ERA5_TLS_GLOBAL")
eralo_ann = utils.Timeseries("ERA5",
np.reshape(eralo.times, [-1, 12])[:, 0],
utils.annual_average(eralo.data))
# update to ERA5.1 during revisions for SotC2019
era51 = np.genfromtxt(DATALOC + "ERA5_1_update.dat")
eralo_ann = utils.Timeseries("ERA5", era51[:, 0], era51[:, 1])
# Sondes [no RATPAC for 2019]
utils.plot_ts_panel(ax2, [raobcore, rich, ratpac],
"-",
"temperature",
loc=LEGEND_LOC)
# satellites
utils.plot_ts_panel(ax4, [UAH, noaa, rss],
"-",
"temperature",
loc=LEGEND_LOC)
# reanalyses
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_MSUch4_global_ts.txt",
"temperature")
merra_actuals, merra_anoms = utils.read_merra_LT_LS(
settings.REANALYSISLOC +
"MERRA2_MSU_Tanom_ann_{}.dat".format(settings.YEAR),
LS=True)
utils.plot_ts_panel(ax6, [eralo_ann, jra_anoms, merra_anoms],
"-",
"temperature",
loc=LEGEND_LOC)
ssu1, ssu1_2, ssu2, ssu2_2, ssu3, ssu3_2 = read_ssu(DATALOC +
"SSU.dat")
# SSU3
utils.plot_ts_panel(ax1, [ssu3, ssu3_2], "-", "temperature", loc="")
# SSU2
utils.plot_ts_panel(ax3, [ssu2, ssu2_2], "-", "temperature", loc="")
# SSU1
utils.plot_ts_panel(ax5, [ssu1, ssu1_2],
"-",
"temperature",
loc=LEGEND_LOC)
# sort labelling
for ax in [ax2, ax4, ax6]:
ax.yaxis.tick_right()
for tick in ax.yaxis.get_major_ticks():
tick.label2.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax3, ax5]:
ax.yaxis.tick_right()
ax.yaxis.set_ticks_position('left')
ax2.text(0.02, 0.88, "(d) Radiosondes", transform=ax2.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax4.text(0.02, 0.88, "(e) Satellites", transform=ax4.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax6.text(0.02, 0.88, "(f) Reanalyses", transform=ax6.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax1.text(0.02, 0.88, "(a) SSU3", transform=ax1.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02, 0.88, "(b) SSU2", transform=ax3.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax5.text(0.02, 0.88, "(c) SSU1", transform=ax5.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
plt.setp([a.get_xticklabels() for a in fig.axes[:-2]], visible=False)
# sort formatting
ax1.set_xlim([1957, raobcore.times[-1] + 3])
ax2.set_xlim([1957, raobcore.times[-1] + 3])
for ax in [ax5, ax6]:
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.set_ylim([-1.2, 2.2])
fig.text(0.01,
0.55,
"Anomaly (" + r'$^\circ$' + "C)",
fontsize=settings.FONTSIZE,
rotation="vertical")
fig.subplots_adjust(right=0.98, top=0.98, bottom=0.04, hspace=0.001)
plt.savefig(settings.IMAGELOC +
"LST_combined_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Polar Figure
if False:
fig, ax1 = plt.subplots(figsize=(8, 5))
north, south = read_polar(DATALOC +
"polar_data_{}.dat".format(settings.YEAR))
ax1.plot(north.times, north.data, c="b", ls="-", label=north.name)
ax1.plot(south.times, south.data, c="r", ls="-", label=south.name)
ax1.legend(loc=LEGEND_LOC,
frameon=False,
prop={'size': settings.FONTSIZE})
ax1.set_xlim([1978, 2018 + 2])
ax1.set_ylabel("Anomaly (" + r'$^\circ$' + "C)",
fontsize=settings.FONTSIZE)
utils.thicken_panel_border(ax1)
plt.savefig(settings.IMAGELOC +
"LST_polar_ts{}".format(settings.OUTFMT))
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.close()
#************************************************************************
# Polar and QBO Timeseries figures
# fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True)
# north, south, qbo = read_qbo_csv(DATALOC + "SOC_Strat_Data_QBO.csv")
# noaa_qbo = read_qbo_ncdf(DATALOC + "qbo_noaa.nc", "NOAA v4.0")
# uah_qbo = read_qbo_ncdf(DATALOC + "qbo_uah.nc", "UAH v6.0")
# rss_qbo = read_qbo_ncdf(DATALOC + "qbo_rss.nc", "RSS v3.3")
# utils.plot_ts_panel(ax1, [north], "-", "temperature", loc="")
# utils.plot_ts_panel(ax2, [south], "-", "temperature", loc="")
# utils.plot_ts_panel(ax3, [noaa_qbo, uah_qbo, rss_qbo], "-", "temperature", loc="", lw=1)
# lines3, labels3 = ax3.get_legend_handles_labels()
# plt.figlegend(lines3, labels3, "lower center", frameon=False, ncol=3, fontsize=settings.FONTSIZE)
# ax1.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE)
# ax2.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE)
# ax3.set_ylabel("QBO Index", fontsize=settings.FONTSIZE)
# ax1.text(0.02, 0.88, "(a) North Polar Pentad Anomalies", transform=ax1.transAxes, \
# fontsize=settings.LABEL_FONTSIZE)
# ax2.text(0.02, 0.88, "(b) South Polar Pentad Anomalies", transform=ax2.transAxes, \
# fontsize=settings.LABEL_FONTSIZE)
# ax3.text(0.02, 0.88, "(c) QBO from Lower Stratospheric Temperature", transform=ax3.transAxes, \
# fontsize=settings.LABEL_FONTSIZE)
# ax3.text(-0.15, 0.88, "West", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE)
# ax3.text(-0.15, 0.01, "East", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE)
# # sort formatting
# plt.xlim([1978, int(settings.YEAR)+1])
# for tick in ax3.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for ax in [ax1, ax2, ax3]:
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# ax1.set_ylim([-10, 19])
# ax2.set_ylim([-10, 23])
# ax3.set_ylim([-1.1, 1.4])
# fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
# plt.savefig(settings.IMAGELOC+"LST_qbo_ts{}".format(settings.OUTFMT))
# plt.close()
#************************************************************************
# MERRA Trop and Strat Timeseries figures
# fourh, threeh, twofiveh, twoh, onefiveh, fourtwoav, oneh, seventy, fifty, thirty, twenty, ten, seventytwentyav = read_merra_monthly(DATALOC + "MERRA2_400_20_temp_anom_1980-{}.txt".format(settings.YEAR))
# fourtwoav.data = np.ma.masked_where(fourtwoav.times < 1994, fourtwoav.data)
# seventytwentyav.data = np.ma.masked_where(seventytwentyav.times < 1994, seventytwentyav.data)
# fourtwoav.times = np.ma.masked_where(fourtwoav.times < 1994, fourtwoav.times)
# seventytwentyav.times = np.ma.masked_where(seventytwentyav.times < 1994, seventytwentyav.times)
# fig, (ax1, ax2) = plt.subplots(2, figsize = (8, 8), sharex=True)
# # trop
# fit = utils.fit_plot_points(0.025, -50.01, fourtwoav.times)
# ax1.plot(fourh.times, fit, c="0.5", lw = 2, ls = "-", label = "fit", zorder = 10)
# utils.plot_ts_panel(ax1, [fourh, threeh, twofiveh, twoh, fourtwoav], "-", "lst", loc = LEGEND_LOC)
# # strat
# fit = utils.fit_plot_points(-0.0014, 2.4527, seventytwentyav.times)
# ax2.plot(fourh.times, fit, c="0.5", lw = 2, ls = "-", label = "fit", zorder = 10)
# utils.plot_ts_panel(ax2, [seventy, fifty, thirty, twenty, seventytwentyav], "-", "lst", loc = LEGEND_LOC)
# # sort formatting
# plt.xlim([1978, fourh.times[-1]+1])
# ax1.set_ylim([-1.7,1.5])
# ax2.set_ylim([-2.0,2.7])
# for tick in ax2.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for ax in [ax1, ax2]:
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# # sort labelling
# ax1.text(0.02, 0.9, "(a) MERRA-2 Troposphere", transform = ax1.transAxes, fontsize = settings.LABEL_FONTSIZE)
# ax2.text(0.02, 0.9, "(b) MERRA-2 Stratosphere", transform = ax2.transAxes, fontsize = settings.LABEL_FONTSIZE)
# ax1.text(0.7, 0.9, "y=0.025x - 50.01", transform = ax1.transAxes, fontsize = settings.LABEL_FONTSIZE*0.8)
# ax2.text(0.7, 0.9, "y=-0.0014x + 2.4527", transform = ax2.transAxes, fontsize = settings.LABEL_FONTSIZE*0.8)
# ax1.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize = settings.FONTSIZE)
# ax2.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize = settings.FONTSIZE)
# fig.subplots_adjust(right = 0.95, top = 0.95, hspace = 0.001)
# plt.savefig(settings.IMAGELOC+"LST_merra_ts{}".format(settings.OUTFMT))
# plt.close()
#************************************************************************
# Zonal figures
# fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (8, 6.5), sharey=True)
# cfsr, merra, era, jra = read_zonal(DATALOC + "TLS_Reanal_zonal_trends_1994-{}.txt".format(settings.YEAR), "R")
# star, uah, rss = read_zonal(DATALOC + "TLS_Satellite_zonal_trends_1994-{}.txt".format(settings.YEAR), "S")
# # Reanalyses
# utils.plot_ts_panel(ax1, [cfsr, merra, era, jra], "--", "temperature", loc = "lower left", ncol = 1)
# # Satellite
# utils.plot_ts_panel(ax2, [star, uah, rss], "--", "temperature", loc = "center right", ncol = 1)
# ax1.axvline(0,color = "0.5", ls = "--")
# ax2.axvline(0,color = "0.5", ls = "--")
# # sort formatting
# plt.ylim([-90,90])
# ax1.set_ylabel("Latitude", fontsize = settings.LABEL_FONTSIZE)
# ax1.set_xlabel("Trend ("+r'$^\circ$'+"C decade"+r'$^{-1}$'+")", fontsize = settings.FONTSIZE)
# ax2.set_xlabel("Trend ("+r'$^\circ$'+"C decade"+r'$^{-1}$'+")", fontsize = settings.FONTSIZE)
# for tick in ax1.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for ax in [ax1, ax2]:
# ax.set_xlim([-0.3,0.6])
# ax.set_xticks(np.arange(-0.2,0.8,0.2))
# ax.set_yticks(np.arange(-90,120,30))
# for tick in ax.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# # sort labelling
# ax1.text(0.02, 0.9, "(a) Radiosondes", transform = ax1.transAxes, fontsize = settings.LABEL_FONTSIZE)
# ax2.text(0.02, 0.9, "(b) Satellites", transform = ax2.transAxes, fontsize = settings.LABEL_FONTSIZE)
# fig.subplots_adjust(right = 0.95, top = 0.95, hspace = 0.001)
# plt.savefig(settings.IMAGELOC+"LST_profiles{}".format(settings.OUTFMT))
# plt.close()
#************************************************************************
# ERA5 Anomaly figure
if True:
# Read in ERA anomalies
cube_list = iris.load(settings.REANALYSISLOC +
"era5_tls_{}01-{}12_ann_ano.nc".format(
settings.YEAR, settings.YEAR))
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4]
utils.plot_smooth_map_iris(settings.IMAGELOC + "LST_{}_anoms_era5".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["temperature"], bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LST_{}_anoms_era5".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["temperature"], bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \
figtext="(f) Lower Stratosphere Temperature")
#************************************************************************
# merra Anomaly figure
if False:
import netCDF4 as ncdf
ncfile = ncdf.Dataset(settings.REANALYSISLOC +
"merra2_tls_ANNUAL_anom.nc")
var = ncfile.variables["TLS ANOM"][:] # this is a masked array
nlons = ncfile.variables["LONGITUDES"][:]
nlats = ncfile.variables["LATITUDES"][:]
cube = utils.make_iris_cube_2d(var, nlats, nlons, "LST", "C")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "LST_{}_anoms_merra".format(settings.YEAR),
cube, settings.COLOURMAP_DICT["temperature"], bounds,
"Anomalies from 1981-2010 (" + r'$^{\circ}$' + "C)")
#************************************************************************
# 2015 MERRA seasonal figure
# import netCDF4 as ncdf
# month_list = []
# for month in MONTHS:
# print month
# # IRIS doesn't like the whitespace in the "TLS ANOM"
# ncfile=ncdf.Dataset(settings.REANALYSISLOC + "merra2_tls_{}_anom.nc".format(month.upper()),'r')
# var=ncfile.variables["TLS ANOM"][:] # this is a masked array
# lons = ncfile.variables["LONGITUDES"][:]
# lats = ncfile.variables["LATITUDES"][:]
# ncfile.close()
# cube = utils.make_iris_cube_2d(var, lats, lons, "TLS_ANOM", "C")
# month_list += [cube]
# # pass to plotting routine
# utils.plot_smooth_map_iris_multipanel(settings.IMAGELOC + "LST_{}_monthly_merra".format(settings.YEAR), month_list, \
# settings.COLOURMAP_DICT["temperature"], bounds, \
# "Anomaly ("+r'$^{\circ}$'+"C)", shape = (6,2), \
# title = MONTHS, \
# figtext = ["(a)","(b)","(c)","(d)", "(e)","(f)","(g)","(h)","(i)","(j)","(k)","(l)"])
return # run_all_plots
