Predicting Solar Protons: A Statistical Approach

Abstract

A small fraction of solar flares are accompanied by high energy (>10 MeV) protons. These events can cause degradation or failure of satellite systems and can be harmful to humans in space or in high altitude flight. For risk management purposes, the Air Force is interested in predicting these events. Several algorithms exist to do this operationally, but none predict when these events will occur with much accuracy. Here, we analyzed 3610 M1 and greater flares including 106 with proton events from the GOES sensors from 1 Jan 1986 to 31 Dec 2004 to produce new results, including a full scale comparison and optimization for all the algorithms. In every case, optimization leads to increased prediction ability. This research also produced a new algorithm based on the Garcia algorithm, which functions better than any other operational algorithm. This model, Garcia 2008, predicts with a skill score of .526, an improvement from .342. This new model is the best at prediction of all models measured.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA495840

Entities

People

  • Jonathan C. Spaulding

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Satellites
  • Bremsstrahlung
  • Climate Change
  • Data Sets
  • Electrons
  • Energy
  • Hard X Rays
  • High Altitude
  • Machine Learning
  • Solar Flares
  • Solar Physics
  • Space Weather
  • Spacecraft
  • Supervised Machine Learning
  • X Rays

Readers

  • Aerospace Engineering.
  • Computational Modeling and Simulation
  • Solar Physics

Technology Areas

  • Space