def abs_vort_hm(run,
lev=150,
filename='plev_daily',
timeav='pentad',
period_fac=1.,
latin=25.):
rcParams['figure.figsize'] = 9, 9
rcParams['font.size'] = 18
rcParams['text.usetex'] = True
plot_dir = '/scratch/rg419/plots/clean_diags/' + run + '/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
name_temp = '/scratch/rg419/Data_moist/' + run + '/run%03d/' + filename + '.nc'
names = [name_temp % m for m in range(397, 409)]
#read data into xarray
data = xr.open_mfdataset(
names,
decode_times=False, # no calendar so tell netcdf lib
# choose how data will be broken down into manageable chunks.
chunks={'time': 30})
uwnd = data.ucomp.sel(pfull=lev)
vwnd = data.vcomp.sel(pfull=lev)
# Create a VectorWind instance to handle the computation of streamfunction and
# velocity potential.
w = VectorWind(uwnd, vwnd)
# Compute the streamfunction and velocity potential.
data['vor'], data['div'] = w.vrtdiv()
#data = xr.open_dataset('/scratch/rg419/Data_moist/'+run+'climatologies/'+run+'.nc')
#Coriolis
omega = 7.2921150e-5
f = 2 * omega * np.sin(data.lat * np.pi / 180)
lat_hm = data.lat[np.argmin(np.abs(data.lat - latin))]
abs_vort = (f + data.vor).sel(lat=lat_hm) * 86400.
levels = np.arange(0., 14.1, 2.)
mn_dic = month_dic(1)
tickspace = range(13, 72, 18)
labels = [mn_dic[(k + 5) / 6] for k in tickspace]
# Plot
f1 = abs_vort.plot.contourf(x='lon',
y='time',
extend='both',
levels=levels,
add_colorbar=False,
add_labels=False)
plt.set_cmap('inferno_r')
plt.ylabel('Time')
#plt.yticks(tickspace, labels, rotation=25)
plt.xlabel('Longitude')
plt.xlim(60, 150)
plt.ylim(240 + 33 * 360, 90 + 33 * 360)
plt.grid(True, linestyle=':')
plt.tight_layout()
#Colorbar
cb1 = plt.colorbar(f1,
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.1,
aspect=30)
cb1.set_label('$day^{-1}$')
figname = 'abs_vort_lon_hm.pdf'
plt.savefig(plot_dir + figname, format='pdf')
plt.close()
import xarray as xr
from windspharm.xarray import VectorWind
f = xr.open_dataset(
"/Users/brianpm/Documents/www.ncl.ucar.edu/Applications/Data/cdf/uv300.nc")
u = f["U"]
v = f["V"]
w = VectorWind(u, v)
## VERY IMPORTANT: VectorWind apparently reverses latitude to be decreasing (90 to -90)
vort, div = w.vrtdiv() # Relative vorticity and horizontal divergence.
sf, vp = w.sfvp() # The streamfunction and velocity potential respectively.
uchi, vchi, upsi, vpsi = w.helmholtz()
# plot the results
import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
fig, ax = plt.subplots(figsize=(12, 12),
nrows=2,
subplot_kw={"projection": ccrs.PlateCarree()},
constrained_layout=True)
N = mpl.colors.Normalize(vmin=-8e6, vmax=8e6)
x, y = np.meshgrid(f['lon'], f['lat'])
im0 = ax[0].contourf(x,
y,
vp[0, ::-1, :],
norm=N,
transform=ccrs.PlateCarree(),
