def testing(data, lat1, lat2, lon1, lon2, t, k):
latname = get_coord(data, 'lat', 'name')
lonname = get_coord(data, 'lon', 'name')
data_sub = dat.subset(data, latname, lat1, lat2, lonname, lon1, lon2)
plt.figure()
ap.pcolor_latlon(data_sub[t, k],
axlims=(lat1, lat2, lon1, lon2),
cmap='jet')
avg0 = data_sub.mean(axis=-1).mean(axis=-1)
avg1 = mean_over_geobox(data, lat1, lat2, lon1, lon2, area_wtd=False)
avg2 = mean_over_geobox(data, lat1, lat2, lon1, lon2, area_wtd=True)
avg3 = mean_over_geobox(data,
lat1,
lat2,
lon1,
lon2,
area_wtd=True,
land_only=True)
print(avg0[t, k].values)
print(avg1[t, k].values)
print(avg2[t, k].values)
print(avg3[t, k].values)
def testing(data, lat1, lat2, lon1, lon2, t, k):
latname = get_coord(data, 'lat', 'name')
lonname = get_coord(data, 'lon', 'name')
data_sub = dat.subset(data, latname, lat1, lat2, lonname, lon1, lon2)
plt.figure()
ap.pcolor_latlon(data_sub[t,k], axlims=(lat1,lat2,lon1,lon2), cmap='jet')
avg0 = data_sub.mean(axis=-1).mean(axis=-1)
avg1 = mean_over_geobox(data, lat1, lat2, lon1, lon2, area_wtd=False)
avg2 = mean_over_geobox(data, lat1, lat2, lon1, lon2, area_wtd=True)
avg3 = mean_over_geobox(data, lat1, lat2, lon1, lon2, area_wtd=True,
land_only=True)
print(avg0[t, k].values)
print(avg1[t, k].values)
print(avg2[t, k].values)
print(avg3[t, k].values)
u = ds['u'] v = ds['v'] T = ds['T'] ps = ds['ps'] lat = get_coord(ps, 'lat') lon = get_coord(ps, 'lon') plev = get_coord(u, 'plev') # ---------------------------------------------------------------------- # interp_latlon lat_new = np.arange(-90, 90, 1.) lon_new = np.arange(0, 360, 1.) # DataArray input ps_i = interp_latlon(ps, lat_new, lon_new) # ndarray input ps_i2 = interp_latlon(ps.values, lat_new, lon_new, lat, lon) t = 0 cmap = 'jet' plt.figure(figsize=(7, 10)) plt.subplot(311) ap.pcolor_latlon(ps[t], cmap=cmap) plt.subplot(312) ap.pcolor_latlon(ps_i[t], cmap=cmap) plt.subplot(313) ap.pcolor_latlon(ps_i2[t], lat_new, lon_new, cmap=cmap)
# ndarray
mfc2 = moisture_flux_conv(uq.values, vq.values, lat, lon, plev * 100)
# components
mfc3, mfc_x, mfc_y, uq_int, vq_int = moisture_flux_conv(uq,
vq,
return_comp=True)
# pmin, pmax
mfc_sub = moisture_flux_conv(uq, vq, pmin=600e2, pmax=900e2)
cmax = 12
plt.figure(figsize=(7, 10))
plt.subplot(311)
ap.pcolor_latlon(mfc)
plt.clim(-cmax, cmax)
plt.subplot(312)
ap.pcolor_latlon(mfc2, lat, lon)
plt.clim(-cmax, cmax)
plt.subplot(313)
ap.pcolor_latlon(mfc_sub)
plt.clim(-cmax, cmax)
plt.figure(figsize=(7, 8))
plt.subplot(211)
ap.pcolor_latlon(mfc_x)
plt.clim(-cmax, cmax)
plt.subplot(212)
ap.pcolor_latlon(mfc_y)
plt.clim(-cmax, cmax)
lon = get_coord(T, 'lon')
plev = get_coord(T, 'plev')
pname = get_coord(T, 'plev', 'name')
units_in = dat.pres_units(T[pname].units)
plev = dat.pres_convert(plev, units_in, 'Pa')
p0 = 1e5
print('Calculating potential temperature')
theta = av.potential_temp(T, plev, p0)
print('Calculating equivalent potential temperature')
theta_e = av.equiv_potential_temp(T, plev, q, p0)
# t, k = 0, 6
# t, k = 0, 22
t, k = 0, 14
pstr = '%d hPa' % (plev[k]/100)
plt.figure(figsize=(7,10))
plt.subplot(311)
ap.pcolor_latlon(T[t,k])
plt.title('Temperature ' + pstr)
plt.subplot(312)
ap.pcolor_latlon(theta[t,k])
plt.title('Potential Temperature ' + pstr)
plt.subplot(313)
ap.pcolor_latlon(theta_e[t,k])
plt.title('Equiv Potential Temperature ' + pstr)
ps.dims
plt.figure()
ap.pcolor_latlon(ps,cmap='hot')
x1, y1 = np.meshgrid(lon_m, lat_m) x2, y2 = np.meshgrid(ps_m2.XDim, ps_m2.YDim) cmap='jet' ncontours = 30 plt.figure(figsize=(7,8)) plt.subplot(211) plt.contourf(x1, y1, ps_m[t].values, ncontours, cmap=cmap) plt.colorbar() plt.subplot(212) plt.contourf(x2, y2, ps_m2[t].values, ncontours, cmap=cmap) plt.colorbar() plt.figure(figsize=(7,8)) plt.subplot(211) ap.pcolor_latlon(ps_m[t], cmap=cmap) plt.subplot(212) ap.pcolor_latlon(ps_m2[t], cmap=cmap) # # ---------------------------------------------------------------------- # # Correct for topography # # u_orig = u # u = dat.correct_for_topography(u_orig, topo) # # # ---------------------------------------------------------------------- # # Interpolate onto new pressure grid # # plev_new = np.arange(1000,0,-50.) # u_i = dat.interp_plevels(u, plev_new, pdim=-3) #
ps = ds['ps'] topo = get_ps_clim(lat, lon) topo.values /= 100 topo.attrs['units'] = 'hPa' # ---------------------------------------------------------------------- # Correct for topography u_orig = u u = correct_for_topography(u_orig, topo) m, k = 3, 1 plt.figure(figsize=(7,8)) plt.subplot(211) ap.pcolor_latlon(u_orig[m,k], cmap='jet') plt.subplot(212) ap.pcolor_latlon(u[m,k], cmap='jet') # ---------------------------------------------------------------------- # Zonal mean zonal wind season='jjas' lon1, lon2 = 60, 100 cint = 5 months = utils.season_months(season) uplot = dat.subset(u, 'lon', lon1, lon2, 'mon', months) uplot = uplot.mean(['lon', 'mon']) ps_plot = dat.subset(topo, 'lon', lon1, lon2)
ps_m2 = set_lon(ps_m, lonmax=360) # ndarray as input ps_m3, lon_out = set_lon(ps_m.values, lonmax=360, lon=lon_m) t = 0 x1, y1 = np.meshgrid(lon_m, lat_m) x2, y2 = np.meshgrid(ps_m2.XDim, ps_m2.YDim) x3, y3 = np.meshgrid(lon_out, lat_m) cmap='jet' ncontours = 30 plt.figure(figsize=(7,10)) plt.subplot(311) plt.contourf(x1, y1, ps_m[t].values, ncontours, cmap=cmap) plt.colorbar() plt.subplot(312) plt.contourf(x2, y2, ps_m2[t].values, ncontours, cmap=cmap) plt.colorbar() plt.subplot(313) plt.contourf(x3, y3, ps_m3[t], ncontours, cmap=cmap) plt.colorbar() plt.figure(figsize=(7,10)) plt.subplot(311) ap.pcolor_latlon(ps_m[t], cmap=cmap) plt.subplot(312) ap.pcolor_latlon(ps_m2[t], cmap=cmap) plt.subplot(313) ap.pcolor_latlon(ps_m3[t], lat_m, lon_out, cmap=cmap)
topo.units = 'hPa' # ---------------------------------------------------------------------- # Relative and absolute vorticity # DataArray rel_vort, abs_vort, f = vorticity(u, v) # ndarray rel_vort2, abs_vort2, f2 = vorticity(u.values, v.values, lat, lon) t, k = 0, 22 plt.figure(figsize=(12,8)) plt.subplot(221) ap.pcolor_latlon(rel_vort[t,k]) plt.subplot(222) ap.pcolor_latlon(abs_vort[t,k]) plt.subplot(223) ap.pcolor_latlon(rel_vort2[t,k], lat, lon) plt.subplot(224) ap.pcolor_latlon(abs_vort2[t,k], lat, lon) # ---------------------------------------------------------------------- # Rossby number Ro = rossby_num(u, v) Ro2 = rossby_num(u.values, v.values, lat, lon) t, k = 0, 22 lon1, lon2 = 60, 100
ps = ds['ps'] topo = dat.get_ps_clim(lat, lon) / 100 topo.units = 'hPa' # ---------------------------------------------------------------------- # Correct for topography u_orig = u u = dat.correct_for_topography(u_orig, topo) # ---------------------------------------------------------------------- # Near-surface data # DataArray u_s, ind_s = near_surface(u, pdim=-3, return_inds=True) # ndarray u_s2 = near_surface(u.values, pdim=-3, return_inds=False) m = 0 plt.figure(figsize=(12, 10)) plt.subplot(221) ap.pcolor_latlon(u[m, 0], cmap='jet') plt.subplot(222) ap.pcolor_latlon(u_s[m], cmap='jet') plt.subplot(223) ap.pcolor_latlon(u_s2[m], lat, lon, cmap='jet') plt.subplot(224) ap.pcolor_latlon(ind_s[m], lat, lon, cmap='jet')
lon = get_coord(T, 'lon')
plev = get_coord(T, 'plev')
pname = get_coord(T, 'plev', 'name')
units_in = dat.pres_units(T[pname].units)
plev = dat.pres_convert(plev, units_in, 'Pa')
p0 = 1e5
print('Calculating potential temperature')
theta = av.potential_temp(T, plev, p0)
print('Calculating equivalent potential temperature')
theta_e = av.equiv_potential_temp(T, plev, q, p0)
# t, k = 0, 6
# t, k = 0, 22
t, k = 0, 14
pstr = '%d hPa' % (plev[k] / 100)
plt.figure(figsize=(7, 10))
plt.subplot(311)
ap.pcolor_latlon(T[t, k])
plt.title('Temperature ' + pstr)
plt.subplot(312)
ap.pcolor_latlon(theta[t, k])
plt.title('Potential Temperature ' + pstr)
plt.subplot(313)
ap.pcolor_latlon(theta_e[t, k])
plt.title('Equiv Potential Temperature ' + pstr)
ps.dims
plt.figure()
ap.pcolor_latlon(ps, cmap='hot')
# ---------------------------------------------------------------------- # Initial plots ubar = u.mean(axis=-1) vbar = v.mean(axis=-1) plt.figure(figsize=(7,8)) plt.subplot(211) ap.contour_latpres(ubar, clev=5) plt.subplot(212) ap.contour_latpres(vbar, clev=0.5) t, k = 0, 4 plt.figure(figsize=(7,8)) plt.subplot(211) ap.pcolor_latlon(u[t,k]) plt.subplot(212) ap.pcolor_latlon(v[t,k]) # ---------------------------------------------------------------------- # Moisture fluxes uq = u * q vq = v * q uq_int = dat.int_pres(uq, pdim=-3) vq_int = dat.int_pres(vq, pdim=-3) t, k = 0, 4 plt.figure(figsize=(7,8)) plt.subplot(211)
# ---------------------------------------------------------------------- # mask_oceans # DataArray input ps_land = mask_oceans(ps) # ndarray input ps_land2 = mask_oceans(ps, lat, lon) t = 0 cmap='jet' # pcolor plt.figure(figsize=(7,10)) plt.subplot(311) ap.pcolor_latlon(ps[t], cmap=cmap) plt.subplot(312) ap.pcolor_latlon(ps_land[t], cmap=cmap) plt.subplot(313) ap.pcolor_latlon(ps_land2[t], lat, lon, cmap=cmap) # contourf, contour ps_plot = ps[0]/100 cint = 100 plt.figure(figsize=(7,8)) plt.subplot(211) ap.contourf_latlon(ps_plot, clev=cint, cmap=cmap, symmetric=False) plt.subplot(212) ap.contour_latlon(ps_plot, clev=cint)
# ---------------------------------------------------------------------- # mask_oceans # DataArray input ps_land = mask_oceans(ps) # ndarray input ps_land2 = mask_oceans(ps, lat, lon) t = 0 cmap = 'jet' # pcolor plt.figure(figsize=(7, 10)) plt.subplot(311) ap.pcolor_latlon(ps[t], cmap=cmap) plt.subplot(312) ap.pcolor_latlon(ps_land[t], cmap=cmap) plt.subplot(313) ap.pcolor_latlon(ps_land2[t], lat, lon, cmap=cmap) # contourf, contour ps_plot = ps[0] / 100 cint = 100 plt.figure(figsize=(7, 8)) plt.subplot(211) ap.contourf_latlon(ps_plot, clev=cint, cmap=cmap, symmetric=False) plt.subplot(212) ap.contour_latlon(ps_plot, clev=cint)
x1, y1 = np.meshgrid(lon_m, lat_m) x2, y2 = np.meshgrid(ps_m2.XDim, ps_m2.YDim) cmap = 'jet' ncontours = 30 plt.figure(figsize=(7, 8)) plt.subplot(211) plt.contourf(x1, y1, ps_m[t].values, ncontours, cmap=cmap) plt.colorbar() plt.subplot(212) plt.contourf(x2, y2, ps_m2[t].values, ncontours, cmap=cmap) plt.colorbar() plt.figure(figsize=(7, 8)) plt.subplot(211) ap.pcolor_latlon(ps_m[t], cmap=cmap) plt.subplot(212) ap.pcolor_latlon(ps_m2[t], cmap=cmap) # # ---------------------------------------------------------------------- # # Correct for topography # # u_orig = u # u = dat.correct_for_topography(u_orig, topo) # # # ---------------------------------------------------------------------- # # Interpolate onto new pressure grid # # plev_new = np.arange(1000,0,-50.) # u_i = dat.interp_plevels(u, plev_new, pdim=-3) #
ps_m2 = set_lon(ps_m, lonmax=360) # ndarray as input ps_m3, lon_out = set_lon(ps_m.values, lonmax=360, lon=lon_m) t = 0 x1, y1 = np.meshgrid(lon_m, lat_m) x2, y2 = np.meshgrid(ps_m2.XDim, ps_m2.YDim) x3, y3 = np.meshgrid(lon_out, lat_m) cmap = 'jet' ncontours = 30 plt.figure(figsize=(7, 10)) plt.subplot(311) plt.contourf(x1, y1, ps_m[t].values, ncontours, cmap=cmap) plt.colorbar() plt.subplot(312) plt.contourf(x2, y2, ps_m2[t].values, ncontours, cmap=cmap) plt.colorbar() plt.subplot(313) plt.contourf(x3, y3, ps_m3[t], ncontours, cmap=cmap) plt.colorbar() plt.figure(figsize=(7, 10)) plt.subplot(311) ap.pcolor_latlon(ps_m[t], cmap=cmap) plt.subplot(312) ap.pcolor_latlon(ps_m2[t], cmap=cmap) plt.subplot(313) ap.pcolor_latlon(ps_m3[t], lat_m, lon_out, cmap=cmap)
ps = ds['ps'] topo = dat.get_ps_clim(lat, lon) / 100 topo.units = 'hPa' # ---------------------------------------------------------------------- # Correct for topography u_orig = u u = dat.correct_for_topography(u_orig, topo) # ---------------------------------------------------------------------- # Near-surface data # DataArray u_s, ind_s = near_surface(u, pdim=-3, return_inds=True) # ndarray u_s2 = near_surface(u.values, pdim=-3, return_inds=False) m = 0 plt.figure(figsize=(12,10)) plt.subplot(221) ap.pcolor_latlon(u[m,0], cmap='jet') plt.subplot(222) ap.pcolor_latlon(u_s[m], cmap='jet') plt.subplot(223) ap.pcolor_latlon(u_s2[m], lat, lon, cmap='jet') plt.subplot(224) ap.pcolor_latlon(ind_s[m], lat, lon, cmap='jet')
u = ds['u'] v = ds['v'] T = ds['T'] ps = ds['ps'] lat = get_coord(ps, 'lat') lon = get_coord(ps, 'lon') plev = get_coord(u, 'plev') # ---------------------------------------------------------------------- # interp_latlon lat_new = np.arange(-90, 90, 1.) lon_new = np.arange(0, 360, 1.) # DataArray input ps_i = interp_latlon(ps, lat_new, lon_new) # ndarray input ps_i2 = interp_latlon(ps.values, lat_new, lon_new, lat, lon) t = 0 cmap='jet' plt.figure(figsize=(7,10)) plt.subplot(311) ap.pcolor_latlon(ps[t], cmap=cmap) plt.subplot(312) ap.pcolor_latlon(ps_i[t], cmap=cmap) plt.subplot(313) ap.pcolor_latlon(ps_i2[t], lat_new, lon_new, cmap=cmap)
topo = dat.get_ps_clim(lat, lon) / 100 topo.units = 'hPa' # ---------------------------------------------------------------------- # Relative and absolute vorticity # DataArray rel_vort, abs_vort, f = vorticity(u, v) # ndarray rel_vort2, abs_vort2, f2 = vorticity(u.values, v.values, lat, lon) t, k = 0, 22 plt.figure(figsize=(12, 8)) plt.subplot(221) ap.pcolor_latlon(rel_vort[t, k]) plt.subplot(222) ap.pcolor_latlon(abs_vort[t, k]) plt.subplot(223) ap.pcolor_latlon(rel_vort2[t, k], lat, lon) plt.subplot(224) ap.pcolor_latlon(abs_vort2[t, k], lat, lon) # ---------------------------------------------------------------------- # Rossby number Ro = rossby_num(u, v) Ro2 = rossby_num(u.values, v.values, lat, lon) t, k = 0, 22 lon1, lon2 = 60, 100
ps = ds['ps']
topo = get_ps_clim(lat, lon)
topo.values /= 100
topo.attrs['units'] = 'hPa'
# ----------------------------------------------------------------------
# Correct for topography
u_orig = u
u = correct_for_topography(u_orig, topo)
m, k = 3, 1
plt.figure(figsize=(7, 8))
plt.subplot(211)
ap.pcolor_latlon(u_orig[m, k], cmap='jet')
plt.subplot(212)
ap.pcolor_latlon(u[m, k], cmap='jet')
# ----------------------------------------------------------------------
# Zonal mean zonal wind
season = 'jjas'
lon1, lon2 = 60, 100
cint = 5
months = utils.season_months(season)
uplot = dat.subset(u, 'lon', lon1, lon2, 'mon', months)
uplot = uplot.mean(['lon', 'mon'])
ps_plot = dat.subset(topo, 'lon', lon1, lon2)
ps_plot = ps_plot.mean('lon')
mfc = moisture_flux_conv(uq,vq)
# ndarray
mfc2 = moisture_flux_conv(uq.values, vq.values, lat, lon, plev*100)
# components
mfc3, mfc_x, mfc_y, uq_int, vq_int = moisture_flux_conv(
uq, vq, return_comp=True)
# pmin, pmax
mfc_sub = moisture_flux_conv(uq, vq, pmin=600e2, pmax=900e2)
cmax = 12
plt.figure(figsize=(7,10))
plt.subplot(311)
ap.pcolor_latlon(mfc)
plt.clim(-cmax, cmax)
plt.subplot(312)
ap.pcolor_latlon(mfc2, lat, lon)
plt.clim(-cmax, cmax)
plt.subplot(313)
ap.pcolor_latlon(mfc_sub)
plt.clim(-cmax, cmax)
plt.figure(figsize=(7,8))
plt.subplot(211)
ap.pcolor_latlon(mfc_x)
plt.clim(-cmax, cmax)
plt.subplot(212)
ap.pcolor_latlon(mfc_y)
plt.clim(-cmax, cmax)