Roadmap
- Disturbance-storm-time (Dst) index: what and why
- Solar-wind based forecast of Dst
- Machine-Learning and artificial neural
networks - Modeling of Dst
- Results and conclusion
Disturbance-storm-time (Dst) index
- A measure of magnetic disturbance
- Solar-wind interaction with Earth’s magnetic field generate electric currents
- Dst index is a measure of ”ringcurrents” in the magnetosphere
- Hourly Dst index is calculated using four geomagnetic observatories
- Different flavors: Kyoto Dst, USGS Dst, Rc index
Why to predict Dst ?
- Important space -weather specification
- Ring -current is one of the major current systems in the magnetosphere
- Critical input to magnetospheric specification models
- Operational Dst forecast provides early warning
- Augment NOAA/CIRES real -time magnetic disturbance modeling
Forecasting of Dst using solar-wind data
- Solar-wind forecasting
- Less-accurate
- Lead-time
- Observatory data not needed
- Empirical relationship
- Burton et al (1975), Temerin and Li (2002), O’Brien and McPherron (2000)
- Physics-based models
- University of Michigan’s Geospace model
- Machine-learning approach
Artificial Intelligence
- An “AI”, or Artificial Intelligence is an intelligent code/machine made by human.
- AI performs cognitive functions such as learning, problem solving, Planning.
- AI progression
- Artificial Weak Intelligence
- Artificial General Intelligence
- Strong AI
- Practical applications are limited to Weak-AI
- Machine-Learning
...
View the entire Presentation:
Forecasting the Magnetic Disturbance-storm-time (Dst) Index using Machine-learning
Manoj Nair, Patrick Alken and Arnaud Chulliat