def read_map(filename):
resolution = 2.5
lats = np.arange(88.75, -88.75 - resolution, -resolution)
lons = np.arange(-178.75, 178.75 + resolution, resolution)
data = np.zeros((len(lats), len(lons)))
lat_ctr = 0
this_lat = []
with open(filename, 'r') as infile:
for ll, line in enumerate(infile):
for ls in line.split():
this_lat.append(ls)
if len(this_lat) == len(lons):
# read in all for one longitude
data[lat_ctr, :] = this_lat
lat_ctr += 1
this_lat = []
data = np.ma.masked_where(data <= -99999.99, data)
cube = utils.make_iris_cube_2d(data, lats, lons, "PCP_anom", "mm")
return cube
def read_dwd_percentile(filename):
"""
Read data from .txt file into Iris cube
:param str filename: file to process
:returns: cube
"""
# use header to hard code the final array shapes
# read in the dat
indata = np.genfromtxt(filename, dtype=(float))
longitudes = np.unique(indata[:, 0])
latitudes = np.unique(indata[:, 1])
data = np.ma.zeros((latitudes.shape[0], longitudes.shape[0]))
data.mask = np.ones(data.shape)
for val in indata:
yloc, = np.where(longitudes == val[0])
xloc, = np.where(latitudes == val[1])
data[xloc[0], yloc[0]] = val[2]
# mask the missing values
data = np.ma.masked_where(data <= -99999.99, data)
cube = utils.make_iris_cube_2d(data, latitudes, longitudes, "R90p", "%")
return cube # read_dwd_percentile
def read_maps(filename, name, units, footer=False):
if footer:
indata = np.genfromtxt(filename, dtype=(float), skip_footer=2)
else:
indata = np.genfromtxt(filename, dtype=(float))
indata = np.ma.masked_where(indata <= -99.999, indata)
indata = indata[::-1, :]
nlat, nlon = indata.shape
delta_lat = 180./nlat
delta_lon = 360./nlon
# presume -90 --> 90, -180 --> 180
LATS = np.arange(-90. + (delta_lat/2.), 90. + (delta_lat/2.), delta_lat)
LONS = np.arange(-180. + (delta_lon/2.), 180. + (delta_lon/2.), delta_lon)
cube = utils.make_iris_cube_2d(indata, LATS, LONS, name, units)
return cube # read_maps
def read_map_data(filename):
'''
Read data for maps and convert to cube. Given as single list files
:param str filename: file to read
:returns: cube
'''
grace = np.genfromtxt(filename, dtype=(float))
lats = grace[:, 0]
lons = grace[:, 1]
anoms = grace[:, 2]
longitudes = np.unique(lons)
latitudes = np.unique(lats)
data = np.ma.zeros((len(latitudes), len(longitudes)))
data.mask = np.ones(data.shape)
for v, val in enumerate(anoms):
xloc, = np.where(longitudes == lons[v])
yloc, = np.where(latitudes == lats[v])
data[yloc[0], xloc[0]] = val
data = np.ma.masked_where(data == -9999.0, data)
cube = utils.make_iris_cube_2d(data, latitudes, longitudes, "TWS_anom",
"mm")
return cube
def read_noaa_mlost(filename, year):
"""
Read the NOAA MLOST data and returns a cube of the year.
:param str filename: filename to read
:param int year: year to extract
:returns: cube of 1 year of temperature anomalies
"""
all_mlost = np.genfromtxt(filename, dtype=(float))
DELTA = 5
# read from i, j columns to get the size of the array
longitudes = np.arange(-180 + (DELTA / 2.), 180 + (DELTA / 2.), DELTA)
latitudes = np.arange(-90 + (DELTA / 2.), 90 + (DELTA / 2.), DELTA)
# set up a masked data array
data = np.ma.zeros((len(latitudes), len(longitudes)))
data.mask = np.ones(data.shape)
# spin through each line
for line in all_mlost:
if line[0] == year:
lat_loc, = np.where(latitudes == line[1])
lon_loc, = np.where(longitudes == line[2])
data[lat_loc, lon_loc] = line[3]
data.mask[lat_loc, lon_loc] = False
cube = utils.make_iris_cube_2d(data, latitudes, longitudes, "temperature",
"C")
return cube # read_noaa_mlost
def read_map(filename, name, units):
'''
Read data for maps and convert to cube. Given as single list files
:param str filename: file to read
:returns: cube
'''
indata = np.genfromtxt(filename, dtype=(float), skip_header=1)
lons = np.arange(-177.5, 177.5 + 5, 5) # hard coded from header
lats = np.arange(-57.5, 57.5 + 5, 5) # hard coded from header
anoms = indata[:, 1:]
cube = utils.make_iris_cube_2d(anoms.T, lats, lons, name, units)
return cube
def read_nasa_giss(filename):
"""
Read the NASA GISS data and returns a cube of the year.
:param str filename: filename to read
:returns: cube of 1 year of temperature anomalies
"""
all_giss = np.genfromtxt(filename, dtype=(float), skip_header=2)
# 2 degree, but just in case
# read from i, j columns to get the size of the array
latitudes = np.zeros(np.max(all_giss[:, 1]).astype(int))
longitudes = np.zeros(np.max(all_giss[:, 0]).astype(int))
# set up a masked data array
data = np.ma.zeros(
(np.max(all_giss[:, 1]).astype(int), np.max(all_giss[:,
0]).astype(int)))
data.mask = np.ones(data.shape)
# spin through each line
for line in all_giss:
# use the indexing provided
i = line[1]
j = line[0]
data[i - 1, j - 1] = line[4]
# and read in the coordinates too
if j == 1:
longitudes[i - 1] = line[2]
if i == 1:
latitudes[j - 1] = line[3]
# mask the missing data
data = np.ma.masked_where(data > 1000, data)
cube = utils.make_iris_cube_2d(data, latitudes, longitudes, "temperature",
"C")
return cube # read_nasa_giss
def read_map(data_loc, name):
'''
Read the map data
:param str data_loc: location where to find files
:param str name: name of measuring platform
:returns: cube
'''
lons = np.genfromtxt(data_loc + "{}_lon_map.aa".format(name),
dtype=(float),
skip_header=5)
lats = np.genfromtxt(data_loc + "{}_lat_map.aa".format(name),
dtype=(float),
skip_header=5)
data = np.zeros((len(lats), len(lons)))
this_lat = []
lon_ctr = 0
lat_ctr = 0
with open(data_loc + "{}_{}_anom_map.aa".format(name, settings.YEAR),
'r') as infile:
for ll, line in enumerate(infile):
if ll > 4: # skip the first 5 lines
for ls in line.split():
this_lat.append(ls)
if len(this_lat) == len(lons):
# read in all for one longitude
data[lat_ctr, :] = this_lat
lat_ctr += 1
this_lat = []
cube = utils.make_iris_cube_2d(data, lats, lons, "UTH_anom", "%")
return cube # read_map
def read_map(filename):
"""
Read user supplied CSV for GLE for anomaly map
"""
indata = np.genfromtxt(filename, delimiter=",", dtype=(float), \
missing_values="NaN", filling_values=-99.9)
# 21 Feb 2018 - the missing_value/filling_value doesn't seem to work for some reason
indata = np.ma.masked_where(indata == -99.9, indata)
indata = np.ma.masked_where(indata == 0, indata)
indata = np.ma.masked_where(indata != indata,
indata) # catch any final NaNs
# from email - 83.5N to 56 S at 0.25 degree resoln
delta = 0.25
lats = np.arange(90 - (delta / 2.), -90, -delta)
lons = np.arange(-180 + (delta / 2.), 180, delta)
cube = utils.make_iris_cube_2d(indata, lats, lons, "GLE", "mm/yr")
return cube # read_map
def read_era5(filename):
fieldwidths = (7, 7, 7, 7, 7, 7, 7, 7, 7, 7)
try:
with open(DATALOC + filename, "r", encoding="latin-1") as infile:
latitudes = []
data = []
for lc, line in enumerate(infile):
# skip header
if lc < 2:
continue
sp_line = line.split()
if sp_line[0] == "latitude":
# read in last data
if lc > 3:
data += [values]
latitudes += [float(sp_line[1])]
values = []
else:
values += [float(t) for t in parse(line, fieldwidths)]
# and final one
data += [values]
data = np.array(data)
latitudes = np.array(latitudes)
except IOError:
print("{} doesn't exist".format(DATALOC + filename))
longitudes = np.arange(0, 360, 0.28125)
cube = utils.make_iris_cube_2d(data, latitudes, longitudes,
"max Precipitation", "mm")
return cube
def read_data(filename):
"""
Read data from .txt file into Iris cube
:param str filename: file to process
:returns: cube
"""
# use header to hard code the final array shapes
longitudes = np.arange(0.625, 360.625, 1.25)
latitudes = np.arange(-89.5, 90.5, 1.)
data = np.ma.zeros((latitudes.shape[0], longitudes.shape[0]))
# read in the dat
indata = np.genfromtxt(filename, dtype=(float), skip_header=4)
this_lat = []
tl = 0
# process each row, append until have complete latitude band
for row in indata:
this_lat += [r for r in row]
if len(this_lat) == longitudes.shape[0]:
# copy into final array and reset
data[tl, :] = this_lat
tl += 1
this_lat = []
# mask the missing values
data = np.ma.masked_where(data <= -999.000, data)
cube = utils.make_iris_cube_2d(data, latitudes, longitudes, "SOZ_anom",
"DU")
return cube # read_data
def read_data(filename, mdi):
'''
Read text format data into data array and return a cube
Not used in SotC2016
:param str filename: file to read
:returns: cube
'''
data = np.genfromtxt(filename, dtype=(float))
nlats = data.shape[0]
nlons = data.shape[1]
latitudes = np.arange(90, -90, -180. / nlats)
longitudes = np.arange(0, 360, 360. / nlons)
data = np.ma.masked_where(data == mdi, data)
cube = utils.make_iris_cube_2d(data, latitudes, longitudes, "BOB",
"g C/m2/yr")
return cube # read_data
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():
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 - 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
#plot_lakes(ax, anomalies[1], anomalies[0], anomalies[2], cmap, norm, "Lake Temperature Anomaly\n("+r"$^{\circ}$"+"C)", bounds, "(c)")
plt.savefig(image_loc + "LKT_ts_maps{}".format(settings.OUTFMT))
plt.close()
#***************
# 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]],
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
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():
#***************
# 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