00. MSIS Option 0: F10.7 Effects

This example demonstrates the analysis of MSIS Option 0, which controls Solar EUV flux variations. 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 0 controls Solar EUV flux variations

This atmospheric effect includes:

• Solar extreme ultraviolet (EUV) radiation heating

• Solar flux proxy (F10.7 radio flux) dependencies

• Solar cycle and rotation effects on atmospheric heating

• Thermospheric temperature and density variations

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

option_index = 0
option_name = "F10.7 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:

• Primary driver of thermospheric density variations

• Essential for solar cycle modeling and predictions

• Critical for long-term orbital decay calculations

• Fundamental for understanding solar-terrestrial coupling

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.