def plot(self, ax): # Get global u/v wind data ug, vg = self.chart.get_data(self.gfs_vars, apply_domain=False) # Get level coordinates for U/V wind components ug_lv_coord = gfs_utils.get_level_coord(ug, self.level_units) vg_lv_coord = gfs_utils.get_level_coord(vg, self.level_units) # Constrain to specified level(s) ugc = gfs_utils.get_coord_constraint(ug_lv_coord.name(), self.level) ug = ug.extract(ugc) vgc = gfs_utils.get_coord_constraint(vg_lv_coord.name(), self.level) vg = vg.extract(vgc) # Apply smoothing ug_data = wrf_smooth2d(ug.data, 6) ug.data = ug_data.data vg_data = wrf_smooth2d(vg.data, 6) vg.data = vg_data.data # Set up windspharm wind vector object w = VectorWind(ug, vg) xi = w.vorticity() div = w.divergence() # Constrain to specified domain domain_constraint = gfs_utils.get_domain_constraint(self.chart.domain) xi = xi.extract(domain_constraint) div = div.extract(domain_constraint) # Calculate strain S following Schielicke et al 2016 u_x, u_y = w.gradient(ug) v_x, v_y = w.gradient(vg) D_h = u_x + v_y # horizontal divergence Def = u_x - v_y # stretching deformation Def_s = u_y + v_x # shearing deformation ss = D_h**2 + Def**2 + Def_s**2 ss = ss.extract(domain_constraint) S = np.sqrt(ss.data) / math.sqrt(2) # Okubo-Weiss parameter # vorticity - (div + strain) okw = (div + S) - xi # Set mask to inspect O-W parameter in relevant region i.e. < 20N self.set_coord_mask(lat_max=20) # Define mask for O-W parameter mask = self.coord_mask | (okw.data < self.thres) # Apply mask okw_masked = iris.util.mask_cube(okw, mask) ax.contourf(self.lon, self.lat, okw_masked.data, **self.options)
def plot(self, ax): # Get global u/v wind data ug, vg = self.chart.get_data(self.gfs_vars, apply_domain=False) # Get level coordinates for U/V wind components ug_lv_coord = gfs_utils.get_level_coord(ug, self.level_units) vg_lv_coord = gfs_utils.get_level_coord(vg, self.level_units) # Constrain to specified level(s) ugc = gfs_utils.get_coord_constraint(ug_lv_coord.name(), self.level) ug = ug.extract(ugc) vgc = gfs_utils.get_coord_constraint(vg_lv_coord.name(), self.level) vg = vg.extract(vgc) # Apply smoothing ug_data = wrf_smooth2d(ug.data, 6) ug.data = ug_data.data vg_data = wrf_smooth2d(vg.data, 6) vg.data = vg_data.data # Set up windspharm wind vector object w = VectorWind(ug, vg) # Get divergence div = w.divergence() # Constrain to specified domain domain_constraint = gfs_utils.get_domain_constraint(self.chart.domain) div = div.extract(domain_constraint) # Mask absolute values below threshold div_masked = np.ma.masked_where( np.abs(div.data) < self.thres, div.data) # Set norm to match centre of colour scale to zero value self.options['norm'] = mcolors.TwoSlopeNorm(vmin=np.min(div_masked), vcenter=0, vmax=np.max(div_masked)) ax.contourf(self.lon, self.lat, div_masked, **self.options)
# The components are in separate files. We catch warnings here because the # files are not completely CF compliant. with warnings.catch_warnings(): warnings.simplefilter('ignore', UserWarning) uwnd = iris.load_cube(example_data_path('uwnd_mean.nc')) vwnd = iris.load_cube(example_data_path('vwnd_mean.nc')) uwnd.coord('longitude').circular = True vwnd.coord('longitude').circular = True # Create a VectorWind instance to handle the computations. w = VectorWind(uwnd, vwnd) # Compute components of rossby wave source: absolute vorticity, divergence, # irrotational (divergent) wind components, gradients of absolute vorticity. eta = w.absolutevorticity() div = w.divergence() uchi, vchi = w.irrotationalcomponent() etax, etay = w.gradient(eta) etax.units = 'm**-1 s**-1' etay.units = 'm**-1 s**-1' # Combine the components to form the Rossby wave source term. S = eta * -1. * div - (uchi * etax + vchi * etay) S.coord('longitude').attributes['circular'] = True # Pick out the field for December at 200 hPa. time_constraint = iris.Constraint(month='Dec') add_month(S, 'time') S_dec = S.extract(time_constraint) # Plot Rossby wave source.
lag = 0 max_i = 35 + lag max_f = max_i + mons min_i = 11 + lag min_f = min_i + mons plt.clf() plt.ion() lsmask = iris.load_cube(ncfile_path + 'lsmask.nc')[0,0,::] lsmask.coord('latitude').guess_bounds() lsmask.coord('longitude').guess_bounds() # landmask = ~(ma.make_mask(lsmask.data.copy()) + np.zeros(temp_plv.shape)).astype(bool) # mask sea, show land # seamask = (ma.make_mask(lsmask.data.copy()) + np.zeros(temp_plv.shape)).astype(bool) # mask land, show sea uv = VectorWind(u,v) uv_div = uv.divergence() psi = uv.streamfunction() plev_b = 300 plev_t = 850 uv_uptrop_max = ta.iristropave(uv_div,plev_bottom=500,plev_top=200)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) uv_midtrop_max = ta.iristropave(uv_div,plev_bottom=850,plev_top=500)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) uv_lowtrop_max = ta.iristropave(uv_div,plev_bottom=1000,plev_top=850)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) u_uptrop_max = ta.iristropave(u,plev_bottom=500,plev_top=200)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) u_midtrop_max = ta.iristropave(u,plev_bottom=850,plev_top=500)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) u_lowtrop_max = ta.iristropave(u,plev_bottom=1000,plev_top=850)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) v_uptrop_max = ta.iristropave(v,plev_bottom=500,plev_top=200)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) v_midtrop_max = ta.iristropave(v,plev_bottom=850,plev_top=500)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN) v_lowtrop_max = ta.iristropave(v,plev_bottom=1000,plev_top=850)[max_i:max_f,::].collapsed('time',iris.analysis.MEAN)