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Related Experiment Videos

A space weather forecasting system with multiple satellites based on a self-recognizing network.

Masahiro Tokumitsu1, Yoshiteru Ishida2

  • 1Department of Electrical and Control Engineering, Yonago National College of Technology, Hikonacho 4448, Yonago, Tottori 683-0854, Japan. tokumitsu@yonago-k.ac.jp.

Sensors (Basel, Switzerland)
|May 8, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel space weather forecasting system using interconnected satellites to predict high-energy electron flux at geostationary orbit. The dynamic relational network model enhances prediction accuracy for space weather events.

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Area of Science:

  • Space Physics
  • Astrophysics
  • Geophysics

Background:

  • Space weather events, such as coronal mass ejections and coronal holes, can impact satellite operations.
  • Accurate forecasting of high-energy electron flux (>2 MeV) at geostationary orbit is crucial for protecting space assets.

Purpose of the Study:

  • To propose and investigate a space weather forecasting system for high-energy electron flux at geostationary orbit.
  • To evaluate the effectiveness of a dynamic relational network model utilizing data from multiple, interconnected sensors on satellites.

Main Methods:

  • Development of a forecasting model using a dynamic relational network for sensor diagnosis and event monitoring.
  • Integration of data from multiple satellites, including solar monitoring satellites measuring interplanetary magnetic field and solar wind speed, and Earth-orbiting satellites measuring high-energy electron flux.
  • Comparative analysis of model performance with varying numbers of sensors, and demonstration of predictions against specific space weather events (coronal mass ejections, coronal holes).

Main Results:

  • The proposed dynamic relational network model demonstrates the capability to forecast high-energy electron flux.
  • Model performance was evaluated using typical space weather event scenarios.
  • The study investigated the impact of sensor network size on forecasting accuracy.

Conclusions:

  • A satellite network with in-situ sensing offers a viable approach for space weather forecasting.
  • The dynamic relational network model shows promise for predicting hazardous space weather conditions.
  • Further research can optimize sensor placement and network configuration for improved forecasting.