sst = ncin.variables['sst'][:]
lons = ncin.variables['longitude'][:]
lats = ncin.variables['latitude'][:]
ncin.close()
# Create an EOF solver to do the EOF analysis. Square-root of cosine of
# latitude weights are applied before the computation of EOFs.
coslat = np.cos(np.deg2rad(lats))
wgts = np.sqrt(coslat)[..., np.newaxis]
solver = EofSolver(sst, weights=wgts)
# Retrieve the leading EOF, expressed as the correlation between the leading
# PC time series and the input SST anomalies at each grid point, and the
# leading PC time series itself.
eof1 = solver.eofsAsCorrelation(neofs=1)
pc1 = solver.pcs(npcs=1, pcscaling=1)
# Plot the leading EOF expressed as correlation in the Pacific domain.
m = Basemap(projection='cyl', llcrnrlon=120, llcrnrlat=-20,
urcrnrlon=260, urcrnrlat=60)
x, y = m(*np.meshgrid(lons, lats))
clevs = np.linspace(-1, 1, 11)
m.contourf(x, y, eof1.squeeze(), clevs, cmap=plt.cm.RdBu_r)
m.drawcoastlines()
m.drawparallels([-20, 0, 20, 40, 60])
m.drawmeridians([120, 140, 160, 180, 200, 220, 240, 260])
cb = plt.colorbar(orientation='horizontal')
cb.set_label('correlation coefficient', fontsize=12)
plt.title('EOF1 expressed as correlation', fontsize=16)
# Plot the leading PC time series.
sst = ncin.variables['sst'][:]
lons = ncin.variables['longitude'][:]
lats = ncin.variables['latitude'][:]
ncin.close()
# Create an EOF solver to do the EOF analysis. Square-root of cosine of
# latitude weights are applied before the computation of EOFs.
coslat = np.cos(np.deg2rad(lats))
wgts = np.sqrt(coslat)[..., np.newaxis]
solver = EofSolver(sst, weights=wgts)
# Retrieve the leading EOF, expressed as the correlation between the leading
# PC time series and the input SST anomalies at each grid point, and the
# leading PC time series itself.
eof1 = solver.eofsAsCorrelation(neofs=1)
pc1 = solver.pcs(npcs=1, pcscaling=1)
# Plot the leading EOF expressed as correlation in the Pacific domain.
m = Basemap(projection='cyl',
llcrnrlon=120,
llcrnrlat=-20,
urcrnrlon=260,
urcrnrlat=60)
x, y = m(*np.meshgrid(lons, lats))
clevs = np.linspace(-1, 1, 11)
m.contourf(x, y, eof1.squeeze(), clevs, cmap=plt.cm.RdBu_r)
m.drawcoastlines()
m.drawparallels([-20, 0, 20, 40, 60])
m.drawmeridians([120, 140, 160, 180, 200, 220, 240, 260])
cb = plt.colorbar(orientation='horizontal')
cb.set_label('correlation coefficient', fontsize=12)
