Creating Composite Reflectivity Maps from Scratch

Composite reflectivity maps are vital tools in meteorology, providing insights into precipitation intensity and storm structures. They display the maximum reflectivity values from various radar elevation angles, offering a comprehensive view of atmospheric conditions. This guide outlines the steps to create composite reflectivity maps from scratch, utilizing Python and the Py-ART library.

Understanding Composite Reflectivity

Composite reflectivity represents the highest reflectivity value observed across multiple radar elevation angles for each point in the atmosphere. This approach ensures that the most intense precipitation echoes are captured, regardless of their altitude. Unlike base reflectivity, which only considers the lowest elevation angle, composite reflectivity provides a more accurate depiction of storm intensity and structure. (en.wikipedia.org)

Prerequisites

Before proceeding, ensure you have the following:

• Python: A programming language widely used in scientific computing.
• Py-ART Library: A Python package designed for weather radar data analysis.
• Matplotlib: A plotting library for creating static, animated, and interactive visualizations.

Step-by-Step Guide

1. Install Necessary Libraries

Begin by installing the required Python libraries using pip:

pip install pyart matplotlib

2. Load Radar Data

Obtain radar data in NetCDF format. Py-ART provides sample data for testing purposes. Load the data using the following code:

import pyart

# Load sample radar data
radar = pyart.testing.get_test_data('swx_20120520_0641.nc')

3. Filter Data

Apply a gate filter to exclude unreliable data, such as areas with low correlation coefficients:

gatefilter = pyart.filters.GateFilter(radar)
gatefilter.exclude_transition
gatefilter.exclude_below('copol_coeff', 0.9)

4. Calculate Composite Reflectivity

Use Py-ART's composite_reflectivity function to compute the composite reflectivity field:

compz = pyart.retrieve.composite_reflectivity(
radar, field='reflectivity_horizontal', gatefilter=gatefilter
)

5. Visualize the Data

Plot the original reflectivity and the composite reflectivity using Matplotlib:

import matplotlib.pyplot as plt

# Plot original reflectivity
fig, ax = plt.subplots(1, 2, figsize=(12, 6))

display = pyart.graph.RadarDisplay(radar)
display.plot('reflectivity_horizontal', ax=ax[0], vmin=-20, vmax=80)

# Plot composite reflectivity
display = pyart.graph.RadarDisplay(compz)
display.plot('composite_reflectivity', ax=ax[1], vmin=-20, vmax=80, cmap='pyart_HomeyerRainbow')

plt.show

Conclusion

Creating composite reflectivity maps from scratch involves processing radar data to extract the maximum reflectivity values across multiple elevation angles. By following the steps outlined above, you can generate these maps using Python and Py-ART, providing valuable insights into precipitation patterns and storm dynamics.

For more detailed information and advanced techniques, refer to the Py-ART documentation and the NOAA's resources on composite reflectivity.

(pyart.readthedocs.io)