Note
Go to the end to download the full example code.
Wave Activity Horizontal Flux#
In this tutorial, we’ll explore how to visualize and analyze Rossby waves - those giant meanders in high-altitude winds that shape our weather patterns. Think of them as the “weather rivers” flowing through our atmosphere!
import xarray as xr
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import easyclimate as ecl
import matplotlib.ticker as ticker
formatter = ticker.ScalarFormatter(useMathText=True, useOffset=True)
formatter.set_powerlimits((0, 0))
z500_prime_data1 = ecl.open_tutorial_dataset("era5_daily_z500_prime_201411_N15.nc").z /9.8
z500_prime_data2 = ecl.open_tutorial_dataset("era5_daily_z500_prime_202411_N15.nc").z /9.8
u500_climatology_data = ecl.open_tutorial_dataset("era5_ymean_monthly_u500_199101_202012_N15.nc").u
v500_climatology_data = ecl.open_tutorial_dataset("era5_ymean_monthly_v500_199101_202012_N15.nc").v
era5_daily_z500_prime_201411_N15.nc ━━━━━━━ 100.0% • 229.2/… • 13.8 • 0:00:00
kB MB/s
era5_daily_z500_prime_202411_N15.nc ━━━━━━━ 100.0% • 227.9/… • 13.0 • 0:00:00
kB MB/s
era5_ymean_monthly_u500_199101_202012_N15.nc ━━━━ 100.0% • 125.… • 8.8 • 0:00:…
kB MB/s
era5_ymean_monthly_v500_199101_202012_N15.nc ━━━━ 100.0% • 126.… • 8.8 • 0:00:…
kB MB/s
TN01 Wave Activity Horizontal Flux#
tn01_result1 = ecl.calc_TN_wave_activity_horizontal_flux(
z_prime_data = z500_prime_data1,
u_climatology_data = u500_climatology_data,
v_climatology_data = v500_climatology_data,
vertical_dim = "level",
vertical_dim_units = "hPa",
)
tn01_result1
tn01_mean_result1 = tn01_result1.mean(dim = "time").sortby("lat")
tn01_mean_result1
draw_shaded1 = tn01_mean_result1["psi_p"]
draw_shaded1 = ecl.plot.add_lon_cyclic(draw_shaded1, 6)
draw_quiver1 = tn01_mean_result1[["fx", "fy"]]
fig, ax = plt.subplots(
figsize = (6, 6),
subplot_kw={"projection": ccrs.NorthPolarStereo(central_longitude=140)}
)
ax.coastlines(edgecolor="black", linewidths=0.5)
ecl.plot.draw_Circlemap_PolarStereo(
ax=ax,
lon_step=30,
lat_step=30,
lat_range=[10, 90],
draw_labels=True,
gridlines_kwargs={"color": "grey", "alpha": 0.5, "linestyle": "--"},
)
fg1 = draw_shaded1.plot.contourf(
ax = ax,
levels = np.linspace(-1.5e7, 1.5e7, 21),
cbar_kwargs = {'location': 'bottom', 'aspect': 40, 'format': formatter},
transform=ccrs.PlateCarree(),
zorder = 1
)
q = draw_quiver1.sel(lat = slice(10, None)).plot.quiver(
ax = ax,
x = "lon", y = "lat", u = "fx", v = "fy",
transform = ccrs.PlateCarree(),
scale = 600,
regrid_shape = 18,
headwidth = 5,
width = 0.0045,
headlength = 8,
headaxislength = 8,
minlength = 3,
add_guide = False,
zorder = 2,
)
qk = ax.quiverkey(
q, 0, -0.1,
50, "50", labelpos = "N",
color = "k",
coordinates = "axes",
fontproperties = {"size": 12},
labelsep = 0.05,
transform = ccrs.PlateCarree(),
)
qk.set_zorder(3)
ax.set_title("TN01 WAF Analysis (500hPa, Nov. 2014)")
Text(0.5, 1.133850874991003, 'TN01 WAF Analysis (500hPa, Nov. 2014)')
tn01_result2 = ecl.calc_TN_wave_activity_horizontal_flux(
z_prime_data = z500_prime_data2,
u_climatology_data = u500_climatology_data,
v_climatology_data = v500_climatology_data,
vertical_dim = "level",
vertical_dim_units = "hPa",
)
tn01_mean_result2 = tn01_result2.mean(dim = "time").sortby("lat")
tn01_mean_result2
draw_shaded2 = tn01_mean_result2["psi_p"]
draw_shaded2 = ecl.plot.add_lon_cyclic(draw_shaded2, 6)
draw_quiver2 = tn01_mean_result2[["fx", "fy"]]
fig, ax = plt.subplots(
figsize = (12, 6),
subplot_kw={"projection": ccrs.Mercator(central_longitude=140)}
)
ax.set_extent([0, 360, 10, 85], crs = ccrs.PlateCarree())
ax.coastlines(edgecolor="black", linewidths=0.5)
ax.gridlines(draw_labels=["bottom", "left"], alpha=0)
fg1 = draw_shaded2.plot.contourf(
ax = ax,
levels = np.linspace(-1e7, 1e7, 21),
cbar_kwargs = {'location': 'bottom', 'aspect': 80, 'shrink': 0.7, 'pad': 0.1, 'format': formatter},
transform=ccrs.PlateCarree(),
zorder = 1
)
q = draw_quiver2.sel(lat = slice(5, None)).plot.quiver(
ax = ax,
x = "lon", y = "lat", u = "fx", v = "fy",
transform = ccrs.PlateCarree(),
scale = 800,
regrid_shape = 15,
add_guide = False,
)
qk = ax.quiverkey(
q, 0.95, 1.03,
50, "50", labelpos = "N",
color = "k",
coordinates = "axes",
fontproperties = {"size": 12},
labelsep = 0.05,
transform = ccrs.PlateCarree(),
)
ax.set_title("TN01 WAF Analysis (500hPa, Nov. 2024)")
Text(0.5, 1.0, 'TN01 WAF Analysis (500hPa, Nov. 2024)')
Plumb Wave Activity Horizontal Flux#
pwaf_result = ecl.calc_Plumb_wave_activity_horizontal_flux(
z_prime_data = z500_prime_data2,
vertical_dim = "level",
vertical_dim_units = "hPa",
)
pwaf_mean_result = pwaf_result.mean(dim = "time").sortby("lat")
pwaf_mean_result
draw_shaded3 = pwaf_mean_result["psi_p"]
draw_shaded3 = ecl.plot.add_lon_cyclic(draw_shaded3, 6)
draw_quiver3 = pwaf_mean_result[["fx", "fy"]]
fig, ax = plt.subplots(
figsize = (12, 6),
subplot_kw={"projection": ccrs.Mercator(central_longitude=140)}
)
ax.set_extent([0, 360, 10, 85], crs = ccrs.PlateCarree())
ax.coastlines(edgecolor="black", linewidths=0.5)
ax.gridlines(draw_labels=["bottom", "left"], alpha=0)
fg1 = draw_shaded3.plot.contourf(
ax = ax,
levels = np.linspace(-1e7, 1e7, 21),
cbar_kwargs = {'location': 'bottom', 'aspect': 80, 'shrink': 0.7, 'pad': 0.1, 'format': formatter},
transform=ccrs.PlateCarree(),
zorder = 1
)
q = draw_quiver3.sel(lat = slice(5, None)).plot.quiver(
ax = ax,
x = "lon", y = "lat", u = "fx", v = "fy",
transform = ccrs.PlateCarree(),
scale = 800,
regrid_shape = 15,
add_guide = False,
)
qk = ax.quiverkey(
q, 0.95, 1.03,
50, "50", labelpos = "N",
color = "k",
coordinates = "axes",
fontproperties = {"size": 12},
labelsep = 0.05,
transform = ccrs.PlateCarree(),
)
ax.set_title("Plumb WAF Analysis (500hPa, Nov. 2024)")
Text(0.5, 1.0, 'Plumb WAF Analysis (500hPa, Nov. 2024)')
Total running time of the script: (0 minutes 6.538 seconds)