Note
Click here to download the full example code
Meteogram¶
What it's for: A Meteogram is a time series plot that displays multiple meteorological variables (e.g., temperature, pressure, humidity, wind) for a single geographical location.
When to use: Use this to visualize the evolution of local weather conditions over a specific period. It is commonly used for weather forecasting, climate monitoring, and analyzing site-specific observational data.
How to read: * X-axis: Represents time (usually UTC or local time). * Y-axes: Each variable is typically plotted on its own axis or in a stacked sub-plot. * Interpretation: Look for correlations between variables (e.g., a drop in pressure followed by a change in wind direction and temperature, indicating a frontal passage).
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from monet_plots.plots.meteogram import Meteogram
# 1. Prepare sample data
np.random.seed(42) # for reproducibility
dates = pd.date_range("2023-01-01 00:00", periods=24, freq="h")
# Simulate temperature with a diurnal cycle
temperature = (
20 + 5 * np.sin(np.linspace(0, 2 * np.pi, 24)) + np.random.normal(0, 0.5, 24)
)
# Simulate humidity
humidity = 70 - 10 * np.cos(np.linspace(0, 2 * np.pi, 24)) + np.random.normal(0, 1, 24)
# Simulate pressure
pressure = (
1012
+ 3 * np.sin(np.linspace(0, 2 * np.pi, 24) + np.pi / 4)
+ np.random.normal(0, 0.2, 24)
)
df = pd.DataFrame(
{"Temperature": temperature, "Humidity": humidity, "Pressure": pressure},
index=dates,
)
# 2. Initialize and create the plot
plot = Meteogram(
df=df, variables=["Temperature", "Humidity", "Pressure"], figsize=(12, 9)
)
plot.plot(linewidth=1.5, marker="o", markersize=3) # Plot with lines and markers
# Add an overall title to the figure
plot.fig.suptitle("Synthetic Meteogram for a 24-hour Period", fontsize=16)
plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust layout to prevent title overlap
plt.show()
Total running time of the script: ( 0 minutes 0.416 seconds)
Download Python source code: plot_meteogram.py