09. MSIS Option 9: All UT Effects

This example demonstrates the analysis of MSIS Option 9, which controls Universal Time atmospheric dependencies. This option represents a key physical process that affects atmospheric density variations.

Understanding how this option affects atmospheric structure is important for atmospheric modeling, satellite operations, and space weather applications.

import matplotlib.pyplot as plt
from msis_options_utils import create_option_analysis_figure

Option 9 controls Universal Time atmospheric dependencies

This atmospheric effect includes:

• Magnetic field orientation effects

• UT-dependent geomagnetic coupling

• Diurnal variation of magnetic field geometry

• Solar wind-magnetosphere interaction timing

This analysis shows how turning OFF this option affects atmospheric density across different dimensions and conditions.

option_index = 9
option_name = "All UT Effects"

fig = create_option_analysis_figure(option_index, option_name)

Understanding the Results

Panel A (Altitude Profiles): Shows how this effect varies with altitude and between different seasonal and diurnal conditions. Look for differences between the four curves to understand temporal variability.

Panel B (Geographic Map): Reveals the global pattern of this atmospheric effect. The contour plot shows percentage changes when the option is turned OFF compared to the baseline (all options ON).

Panel C (Diurnal Cycle): Demonstrates how this effect varies throughout a 24-hour period at a fixed location (45°N, 0°E, 300 km altitude).

Panel D (Seasonal Cycle): Shows how the strength of this effect varies throughout the year, revealing seasonal dependencies.

plt.show()

Physical Importance

This atmospheric effect is important because:

• Important for geomagnetic coupling accuracy

• Essential for magnetic field orientation effects

• Critical for space weather applications

• Improves model accuracy during active conditions

When this option is turned OFF, these physical processes are removed from the atmospheric model, which can significantly impact the accuracy of density predictions depending on the specific application and conditions.