Note
Click here to download the full example code
Curtain Plot¶
What it's for: A Curtain plot (or time-height cross-section) visualizes how a vertical profile changes over time at a fixed location, or along a moving path (like a flight track).
When to use: Use this to monitor the evolution of the planetary boundary layer, track the arrival and dispersion of a smoke plume, or analyze the vertical structure of a weather system as it passes over a station.
How to read: * X-axis: Typically represents Time (or distance along a track). * Y-axis: Represents Altitude or Pressure. * Color: Represents the magnitude of the variable being plotted (e.g., PM2.5 concentration, potential temperature). * Interpretation: Look for temporal trends at specific heights or the movement of features (like an inversion layer) upward or downward over time.
Out:
Curtain plot saved to curtain_example.png
import numpy as np
import pandas as pd
import xarray as xr
from monet_plots.plots.curtain import CurtainPlot
# Create dummy 2D data
times = pd.date_range("2023-01-01", periods=24, freq="h")
levels = np.linspace(1000, 100, 10)
data = np.exp(-((np.arange(24) - 12) ** 2) / 20)[:, np.newaxis] * np.exp(
-((np.arange(10) - 5) ** 2) / 10
)
da = xr.DataArray(
data.T, coords=[levels, times], dims=["level", "time"], name="concentration"
)
# Initialize and plot
plot = CurtainPlot(da)
plot.plot(kind="contourf", cmap="viridis")
plot.save("curtain_example.png")
print("Curtain plot saved to curtain_example.png")
Total running time of the script: ( 0 minutes 0.443 seconds)
Download Python source code: plot_curtain.py