SOLAR PARTICLE PENETRATION AT HIGH LATITUDES

Model Calculations of Cutoff Latitudes and Variations with Solar Wind Conditions

R. B. Decker and C. Paranicas

Space Department
The Johns Hopkins University
Applied Physics Laboratory

Background

This project is supported by a NASA grant under the Living with a Star (LWS) Geospace program. Our goal is to provide an improved predictive model of cutoff latitudes of solar energetic protons (SEPs) with arrival energies at Earth in the range 1-300 MeV.

This page contains links to detailed documentation and software packages. Users can download and implement these files to perform particle orbit simulations for various conditions of the model magnetic field, analyze the simulation output to determine cutoff latitude contours, and display these contours. The simulation and diagnostic codes are written in FORTRAN and the graphical display code in IDL.

Documentation and Source Code

The source codes and documents needed to (1) perform the multi-particle orbit integrations, (2) extract cutoff latitudes from the ensemble of orbits, and (3) plot cutoff latitude contours on a map of the terrestrial polar regions, are found at the following links.

1. Science Summary (pdf) : Brief summary of the model, including descriptions of the scientific and operational motivations, information on the magnetic field models used, and sample results.
2. User's Guide (pdf) : Detailed handbook on how to implement the codes that integrate particle orbits in the magnetic field model, how to interpret the results, and how to produce and display cutoff latitudes boundaries.
3. Orbit Integration and Diagnostic (text) : All source codes needed to perform orbit integrations and extract cutoff latitude contours.
4. Cutoff Latitude Graphical Display Code (text) : All source codes needed to produce graphical displays of cutoff latitude contours.

Model Results

1. Test Cases : Model output files (text) and polar plots (ps) for various test-case runs at eight proton energies.
2. Data-Driven Cases : Model predictions for specific events based on data-driven input parameters.