Code to analyse the effect of the Madden-Julian Oscillation on the global electric circuit
https://eee.ipfran.ru/en/
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285 KiB
285 KiB
EOF analysis of contributions to the IP¶
Here we represent an analysis of contributions to the ionospheric potential (IP), using the concept of empirical orthogonal functions (EOFs).
Imports¶
In [5]:
# data processing
import numpy as np
# plotting the data
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, HPacker
# Fourier analysis
from scipy.fft import rfft, rfftfreq, irfft
Loading the data¶
In [6]:
map_contrib = np.load('./data/DAILY-IP-MAP-V4.3.npy')
# original data with the shape (number of days, number of latitudes, number of longitudes)
# contains IP values (not normalised) depending on (d, lat, lon)
# d (axis 0) is the number of a day starting with 0 and ending with 4991
# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020
# lat (axis 1) describes the latitude (an integer in [0, 180])
# lon (axis 2) describes the longitude (an integer in [0, 360])
map_contrib /= np.mean(np.sum(map_contrib, axis=(1, 2)))
map_contrib *= 240e3
# normalisation of contributions to the IP to the global mean of 240 kV
ip = np.sum(map_contrib, axis=(1, 2)) # total IP values for different days
rmm = np.genfromtxt('./data/rmm.txt')
rmm = rmm[::3, [3, 4]] # RMM1 and RMM2
# the array should look like the IP data (with every third day taken)
angle_rmm = np.arctan2(rmm[:, 1], rmm[:, 0]) # phase angles of RMM values
phase_rmm = np.floor((angle_rmm / np.pi + 1) * 4).astype(int) # phase numbers
# phase separation
phase_avg_ip = np.zeros((8), dtype=float)
counter = np.zeros((8), dtype=int)
for i in range(len(phase_rmm)):
counter[phase_rmm[i]] += 1
phase_avg_ip[phase_rmm[i]] += ip[i] # summing over each phase
phase_avg_ip /= counter # averaging over each phase
Preprocessing of the data for the EOF analysis¶
In [7]:
def remove_enso(a):
"""
Remove the patterns related to ENSO variability.
:param a: an array with the shape (number of days, number of longitudes)
:return: the same array with ENSO variability removed
"""
enso34 = np.load('./data/DAILY-ENSO34.npy') # mean SST values in the Niño 3.4 region
assert len(enso34) == a.shape[0]
a_mean_before = np.mean(a, axis=0) # mean values over days before the procedure
p = np.polyfit(enso34, a, 1)
a -= np.matmul(enso34[:, np.newaxis], p[0, np.newaxis, :]) + p[1, np.newaxis, :]
# pointwise subtraction of a linear regression between enso34[:] and a[:, ...]
a_mean_after = np.mean(a, axis=0) # mean values over days after the procedure
a += a_mean_before[np.newaxis, :] - a_mean_after[np.newaxis, :]
# restoring the original mean values
return a
def remove_seasonal(a):
"""
Remove the seasonal variability.
:param a: an array with the shape (number of days, number of longitudes)
:return: the same array with first four harmonics of seasonal variability removed
"""
frequencies = rfftfreq(a.shape[0], 3) # every third day is taken in the data
fourier_transform = rfft(a, axis=0)
for m in range(1, 5): # the removal of the four leading seasonal harmonics
condition = (np.fabs(frequencies - m/365.25) <= frequencies[1]/2)
# in fact frequencies[1] == 1 / (a.shape[0]*3)
fourier_transform = np.where(
condition[:, np.newaxis], 0, fourier_transform
)
a = irfft(fourier_transform, a.shape[0], axis=0)
# the mean value changes negligibly since we remove seasonal harmonics with zero mean values
return a
latitude = 15 # the boundary latitude for tropics
assert map_contrib.shape[1] == 180 # the next formula assumes this
assert latitude < 90 # the next formula will not work correctly for 90
map_contrib = np.sum(map_contrib[:, 90-latitude:-90+latitude, :], axis=1)
# summing over latitudes in the equatorial region
map_contrib = remove_enso(map_contrib)
map_contrib = remove_seasonal(map_contrib)
# this removes the variability on the ENSO timescale and seasonal variability
# it is necessary to do this in the presented order as the removal of patterns
# linearly related to the ENSO34 brings variability with a period of about one year
print(f'Processed contribution of the 15° S–15° N region: {np.sum(np.mean(map_contrib, axis=0))}')
map_contrib -= np.mean(map_contrib, axis=0) # subtraction of the mean distribution
EOF analysis¶
In [8]:
def eof_analysis(a):
"""
Calculate empirical orthogonal functions (EOFs) and related quantities.
:param a: an array with the shape (number of days, number of longitudes)
:return: EOFs, explained variances, principal components (PCs)
"""
cov = np.cov(a, rowvar=False) # covariance matrix
lambdas, eofs = np.linalg.eig(cov) # diagonalisation of the covariance matrix
# EOFs are eigenvectors with the Euclidean norm equal to 1
order = lambdas.argsort()[::-1] # sorting by decreasing eigenvalues
lambdas = lambdas[order]
eofs = eofs[:, order]
expvars = lambdas / np.sum(lambdas) # the fraction of variance explained by each EOF
pcs = np.matmul(a, eofs) # PCs for the EOFs
# a[day, longitude] × eofs[longitude, number] == pcs[day, number]
return eofs, expvars, pcs
eofs, expvars, pcs = eof_analysis(map_contrib)
In [9]:
reofs = np.zeros((eofs.shape[0], 3), dtype=float)
reofs[:, 0] = eofs[:, 0]
reofs[:, 1] = (eofs[:, 1] - eofs[:, 2]) / np.sqrt(2)
reofs[:, 2] = (eofs[:, 1] + eofs[:, 2]) / np.sqrt(2)
# rotated EOFs
rpcs = np.zeros((pcs.shape[0], 3), dtype=float)
rpcs[:, 0] = pcs[:, 0]
rpcs[:, 1] = (pcs[:, 1] - pcs[:, 2]) / np.sqrt(2)
rpcs[:, 2] = (pcs[:, 1] + pcs[:, 2]) / np.sqrt(2)
# PCs corresponding to the rotated EOFs
rexpvars = np.zeros(3)
rexpvars[0] = expvars[0]
rexpvars[1] = (expvars[1] + expvars[2]) / 2
rexpvars[2] = (expvars[1] + expvars[2]) / 2
# the fraction of variance explained by each rotated EOF
In [10]:
# components of the longitudinal IP variation are obtained by
# multiplying EOFs by the respective PCs
# phase separation
phase_avg_comp = np.zeros((8, eofs.shape[0], eofs.shape[1]), dtype=float)
phase_avg_pc = np.zeros((8, pcs.shape[1]), dtype=float)
phase_avg_rcomp = np.zeros((8, eofs.shape[0], 3), dtype=float)
phase_avg_rpc = np.zeros((8, 3), dtype=float)
counter = np.zeros((8), dtype=int)
for i in range(len(phase_rmm)):
counter[phase_rmm[i]] += 1
phase_avg_comp[phase_rmm[i], :, :] += eofs[:, :] * pcs[i, np.newaxis, :]
phase_avg_pc[phase_rmm[i], :] += pcs[i, :]
phase_avg_rcomp[phase_rmm[i], :, :] += reofs[:, :] * rpcs[i, np.newaxis, :]
phase_avg_rpc[phase_rmm[i], :] += rpcs[i, :]
# summing over each phase
phase_avg_comp[:, :, :] /= counter[:, np.newaxis, np.newaxis]
phase_avg_pc[:, :] /= counter[:, np.newaxis]
phase_avg_rcomp[:, :, :] /= counter[:, np.newaxis, np.newaxis]
phase_avg_rpc[:, :] /= counter[:, np.newaxis]
# averaging over each phase
# the first resulting array contains components of longitudinal IP variation
# the indices are (MJO phase, longitude, EOF number)
# the second resulting array contains PCs of IP variation
# the indices are (MJO phase, EOF/PC number)
# the third and fourth arrays contain the same for the rotated EOFs
phase_avg_ip_comp = np.sum(phase_avg_comp, axis=1)
phase_avg_ip_rcomp = np.sum(phase_avg_rcomp, axis=1)
# summing over longitudes
# the first array contains total contributions to the IP from various EOFs
# the indices are (MJO phase, EOF number)
# the second array contains the same for the rotated EOFs
Plots¶
In [11]:
fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10, 12),
constrained_layout=False, gridspec_kw={'height_ratios': [3, 3, 3.5]})
for i in range(3):
for j in range(2):
for axis in ['top', 'bottom', 'left', 'right']:
ax[i, j].spines[axis].set_linewidth(0.5)
ax[i, j].tick_params(length=6, width=0.5)
ax[i, j].tick_params(length=3, width=0.5, which='minor')
for j in range(2):
ax[1, j].set_xlim(0, 360)
ax[1, j].set_xticks(np.arange(0, 361, 60))
ax[1, j].set_xticks(np.arange(0, 361, 30), minor=True)
ax[1, j].set_xticklabels(['0', '60E', '120E', '180',
'120W', '60W', '0'],
fontsize='large')
ax[1, j].set_xlabel('Longitude', fontsize='large')
ax[1, j].set_ylim(-0.2, 0.2)
ax[1, j].set_yticks(np.arange(-0.2, 0.21, 0.1))
ax[1, j].set_yticklabels([f'−{-y:.1f}' if y < 0
else f'{y:.1f}'
for y in np.arange(-0.2, 0.21, 0.1)],
fontsize='large')
ax[1, j].grid(color='0.8', linewidth=0.5, which='both')
ax[1, j].axhline(color='0.', linewidth=0.5)
for i in [0, 2]:
ax[i, j].set_xlim(0.5, 8.5)
ax[i, j].set_xticks(np.arange(1, 9))
ax[i, j].set_xticklabels(np.arange(1, 9).astype(int),
fontsize='large')
ax[i, j].set_xlabel('MJO phase', fontsize='large')
ax[0, j].set_ylim(-9, 9)
ax[0, j].set_yticks(np.arange(-9, 10, 3))
ax[0, j].set_yticklabels([f'−{-y:d}' if y < 0
else f'+{y:d}' if y > 0
else f'{y:d}'
for y in np.arange(-9, 10, 3)],
fontsize='large')
ax[2, j].set_ylim(-1.5, 1.5)
ax[2, j].set_yticks(np.arange(-1.5, 1.6, 0.5))
ax[2, j].set_yticklabels([f'−{-y:.1f}' if y < 0
else f'{y:.1f}'
for y in np.arange(-1.5, 1.6, 0.5)],
fontsize='large')
for i in [0, 2]:
ax[i, j].grid(color='0.8', linewidth=0.5, axis='y')
ax[i, j].axhline(color='0.', linewidth=0.5)
ax[0, 0].set_ylabel('Anomaly in the\nionospheric potential, kV',
fontsize='large')
ax[1, 0].set_ylabel('Normalised magnitude', fontsize='large')
ax[2, 0].set_ylabel('Magnitude, kV', fontsize='large')
fig.align_ylabels([ax[0, 0], ax[1, 0], ax[2, 0]])
ax[0, 0].bar(np.arange(1, 9) - 0.3, phase_avg_ip_comp[:, 0] / 1e3,
width=0.3, color='orangered', label='PC1 · EOF1')
ax[0, 0].bar(np.arange(1, 9), phase_avg_ip_comp[:, 1] / 1e3,
width=0.3, color='olive', label='PC2 · EOF2')
ax[0, 0].bar(np.arange(1, 9) + 0.3, phase_avg_ip_comp[:, 2] / 1e3,
width=0.3, color='mediumvioletred', label='PC3 · EOF3')
# hair spaces around '·'
ax[1, 0].plot(np.arange(0, 361), np.append(eofs[:, 0], eofs[0, 0]),
linewidth=1.5, color='orangered', clip_on=False,
zorder = 6, label=f'EOF1 ({expvars[0]:.2%})')
ax[1, 0].plot(np.arange(0, 361), np.append(eofs[:, 1], eofs[0, 1]),
linewidth=1.5, color='olive', clip_on=False,
zorder = 5, label=f'EOF2 ({expvars[1]:.2%})')
ax[1, 0].plot(np.arange(0, 361), np.append(eofs[:, 2], eofs[0, 2]),
linewidth=1.5, color='mediumvioletred', clip_on=False,
zorder = 4, label=f'EOF3 ({expvars[2]:.2%})')
ax[2, 0].bar(np.arange(1, 9) - 0.3, phase_avg_pc[:, 0] / 1e3,
width=0.3, color='orangered', label='PC1')
ax[2, 0].bar(np.arange(1, 9), phase_avg_pc[:, 1] / 1e3,
width=0.3, color='olive', label='PC2')
ax[2, 0].bar(np.arange(1, 9) + 0.3, phase_avg_pc[:, 2] / 1e3,
width=0.3, color='mediumvioletred', label='PC3')
ax[0, 1].bar(np.arange(1, 9) - 0.3, phase_avg_ip_rcomp[:, 0] / 1e3,
width=0.3, color='orangered', label='PC1 · EOF1')
ax[0, 1].bar(np.arange(1, 9), phase_avg_ip_rcomp[:, 1] / 1e3,
width=0.3, color='teal', label='PC2′ · EOF2′')
ax[0, 1].bar(np.arange(1, 9) + 0.3, phase_avg_ip_rcomp[:, 2] / 1e3,
width=0.3, color='darkgoldenrod', label='PC3′ · EOF3′')
# hair spaces around '·'
ax[1, 1].plot(np.arange(0, 361), np.append(reofs[:, 0], reofs[0, 0]),
linewidth=1.5, color='orangered', clip_on=False,
zorder = 6, label=f'EOF1 ({rexpvars[0]:.2%})')
ax[1, 1].plot(np.arange(0, 361), np.append(reofs[:, 1], reofs[0, 1]),
linewidth=1.5, color='teal', clip_on=False,
zorder = 5, label=f'EOF2′ ({rexpvars[1]:.2%})')
ax[1, 1].plot(np.arange(0, 361), np.append(reofs[:, 2], reofs[0, 2]),
linewidth=1.5, color='darkgoldenrod', clip_on=False,
zorder = 4, label=f'EOF3′ ({rexpvars[2]:.2%})')
ax[2, 1].bar(np.arange(1, 9) - 0.3, phase_avg_rpc[:, 0] / 1e3,
width=0.3, color='orangered', label='PC1')
ax[2, 1].bar(np.arange(1, 9), phase_avg_rpc[:, 1] / 1e3,
width=0.3, color='teal', label='PC2′')
ax[2, 1].bar(np.arange(1, 9) + 0.3, phase_avg_rpc[:, 2] / 1e3,
width=0.3, color='darkgoldenrod', label='PC3′')
for j in range(2):
leg = ax[1, j].legend(fontsize='large', framealpha=1,
edgecolor='0.', handlelength=1.)
leg.get_frame().set_linewidth(0.5)
for i in [0, 2]:
leg = ax[i, j].legend(fontsize='large', framealpha=1,
edgecolor='0.', handlelength=1.5)
leg.get_frame().set_linewidth(0.5)
for i in range(3):
ax[i, 0].text(-0.3, 1.05, chr(ord('a') + i), fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[i, 0].transAxes)
ax[i, 1].text(-0.18, 1.05, chr(ord('a') + 3 + i), fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[i, 1].transAxes)
fig.subplots_adjust(hspace=0.3, wspace=0.25)
fig.savefig('figures/eofs_and_pcs.eps', bbox_inches='tight')
fig.savefig('figures/eofs_and_pcs.png', dpi=300, bbox_inches='tight')
In [12]:
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 3.5),
constrained_layout=False)
for j in range(2):
for axis in ['top', 'bottom', 'left', 'right']:
ax[j].spines[axis].set_linewidth(0.5)
ax[j].tick_params(length=6, width=0.5)
ax[j].set_xlim(0.5, 8.5)
ax[j].set_xticks(np.arange(1, 9))
ax[j].set_xticklabels(np.arange(1, 9).astype(int),
fontsize='large')
ax[j].set_xlabel('MJO phase', fontsize='large')
ax[j].grid(color='0.8', linewidth=0.5, axis='y')
ax[j].axhline(color='0.', linewidth=0.5)
ax[0].set_ylim(-15, 15)
ax[0].set_yticks(np.arange(-15, 16, 5))
ax[0].set_yticklabels([f'−{-y:d}' if y < 0
else f'+{y:d}' if y > 0
else f'{y:d}'
for y in np.arange(-15, 16, 5)],
fontsize='large')
ax[1].set_ylim(-12, 12)
ax[1].set_yticks(np.arange(-12, 13, 4))
ax[1].set_yticklabels([f'−{-y:d}' if y < 0
else f'+{y:d}' if y > 0
else f'{y:d}'
for y in np.arange(-12, 13, 4)],
fontsize='large')
ax[0].set_ylabel('Anomaly in the\nionospheric potential, kV',
fontsize='large')
ax[0].bar(np.arange(1, 9) - 0.2, phase_avg_ip / 1e3 - 240,
width=0.4, color='coral',
label='Total ionospheric potential')
ax[0].bar(np.arange(1, 9) + 0.2, np.sum(phase_avg_ip_comp, axis=1) / 1e3,
width=0.4, color='royalblue',
label='Processed contrib. of 15° S–15° N')
# thin spaces after '°'
ax[1].bar(np.arange(1, 9) - 0.2, np.sum(phase_avg_ip_comp, axis=1) / 1e3,
width=0.4, color='royalblue',
label='Total anomaly (all EOFs and PCs)')
ax[1].bar(np.arange(1, 9) + 0.2, np.sum(phase_avg_ip_comp[:, :3], axis=1) / 1e3,
width=0.4, color='peru',
label='PC1 · EOF1 + PC2 · EOF2 + PC3 · EOF3')
# hair spaces around '·', thin spaces around '+'
for j in range(2):
leg = ax[j].legend(bbox_to_anchor=(0., -0.45, 1., 0.2),
loc='upper center', borderaxespad=0.,
mode = 'expand', fontsize='large',
framealpha=1, edgecolor='0.', handlelength=1.5)
leg.get_frame().set_linewidth(0.5)
ax[0].text(-0.3, 1.05, 'a', fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[0].transAxes)
ax[1].text(-0.18, 1.05, 'b', fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[1].transAxes)
fig.subplots_adjust(wspace=0.25)
fig.savefig('figures/equatorial_variation.eps', bbox_inches='tight')
fig.savefig('figures/equatorial_variation.png', dpi=300, bbox_inches='tight')
In [13]:
fig, ax = plt.subplots(nrows=8, ncols=3, figsize=(10, 12),
constrained_layout=False)
for i in range(8):
for j in range(3):
for axis in ['top', 'bottom', 'left', 'right']:
ax[i, j].spines[axis].set_linewidth(0.5)
ax[i, j].tick_params(length=6, width=0.5)
ax[i, j].set_xlim(0, 360)
ax[i, j].set_xticks(np.arange(0, 361, 60))
if i == 7:
ax[i, j].set_xticklabels(['0', '60E', '120E', '180',
'120W', '60W', '0'],
fontsize='large', rotation=315)
if j == 1:
ax[i, j].set_xlabel('Longitude', fontsize='large')
else:
ax[i, j].set_xticklabels([])
ax[i, j].set_ylim(-300, 300)
ax[i, j].set_yticks(np.arange(-300, 301, 150))
if j == 0:
ax[i, j].set_yticklabels([f'−{-y:d}' if y < 0
else f'+{y:d}' if y > 0
else f'{y:d}'
for y in np.arange(-300, 301, 150)],
fontsize='large')
if i == 3:
ax[i, j].yaxis.set_label_coords(-0.25, -0.125)
ax[i, j].set_ylabel('Anomaly in the contribution to the '
'ionospheric potential, V',
fontsize='large', ha='center')
else:
ax[i, j].set_yticklabels([])
ax[i, j].grid(color='0.8', linewidth=0.5)
ax[i, j].axhline(color='0.', linewidth=0.5)
ax[i, 2].yaxis.set_label_position('right')
ax[i, 2].set_ylabel(f'Phase {i + 1}', fontsize='large',
rotation=270, va='bottom')
ys = np.sum(phase_avg_comp[i, :, :], axis=1)
ax[i, 0].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,
color='royalblue', clip_on=False, zorder = 4)
ys = np.sum(phase_avg_rcomp[i, :, :2], axis=1)
ax[i, 1].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,
color='darkgreen', clip_on=False, zorder = 4)
ys = phase_avg_rcomp[i, :, 0]
ax[i, 2].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,
color='orangered', clip_on=False, zorder = 5)
ys = phase_avg_rcomp[i, :, 1]
ax[i, 2].plot(np.arange(0, 361), np.append(ys, ys[0]), linewidth=1.5,
color='teal', clip_on=False, zorder = 4)
box1 = TextArea('Total (all EOFs and PCs)',
textprops=dict(color='royalblue', fontsize='large'))
box2 = TextArea('PC1 · EOF1 + PC2′ · EOF2′',
textprops=dict(color='darkgreen', fontsize='large'))
box3 = TextArea('PC1 · EOF1',
textprops=dict(color='orangered', fontsize='large'))
box4 = TextArea(' vs ',
textprops=dict(color='0.', fontsize='large'))
box5 = TextArea('PC2′ · EOF2′',
textprops=dict(color='teal', fontsize='large'))
box6 = HPacker(children=[box3, box4, box5], align='center',
pad=0., sep=0.)
# hair spaces around '·', thin spaces around '+'
abox1 = AnchoredOffsetbox(loc='lower center', child=box1, pad=0.,
borderpad=0.3, frameon=False,
bbox_to_anchor=(0.5, 1.),
bbox_transform=ax[0, 0].transAxes)
abox2 = AnchoredOffsetbox(loc='lower center', child=box2, pad=0.,
borderpad=0.3, frameon=False,
bbox_to_anchor=(0.5, 1.),
bbox_transform=ax[0, 1].transAxes)
abox3 = AnchoredOffsetbox(loc='lower center', child=box6, pad=0.,
borderpad=0.3, frameon=False,
bbox_to_anchor=(0.5, 1.),
bbox_transform=ax[0, 2].transAxes)
ax[0, 0].add_artist(abox1)
ax[0, 1].add_artist(abox2)
ax[0, 2].add_artist(abox3)
fig.subplots_adjust(hspace=0.25, wspace=0.1)
fig.savefig('figures/longitudinal_structure.eps', bbox_inches='tight')
fig.savefig('figures/longitudinal_structure.png', dpi=300, bbox_inches='tight')
