Help NOAA better forecast changes in Earth’s magnetic field with MagNet Model the Geomagnetic Field Competition.
The efficient transfer of energy from solar wind into the Earth’s magnetic field causes geomagnetic storms. The resulting variations in the magnetic field increase errors in magnetic navigation. The disturbance-storm-time index, or Dst, is a measure of the severity of the geomagnetic storm.
As a key specification of the magnetospheric dynamics, the Dst index is used to drive geomagnetic disturbance models such as NOAA/NCEI’s High Definition Geomagnetic Model – Real-Time (HDGM-RT). Additionally, magnetic surveyors, government agencies, academic institutions, satellite operators, and power grid operators use the Dst index to analyze the strength and duration of geomagnetic storms.
Empirical models have been proposed as early as in 1975 to forecast Dst solely from solar-wind observations at the Lagrangian (L1) position by satellites such as NOAA’s Deep Space Climate Observatory (DSCOVR) or NASA’s Advanced Composition Explorer (ACE). Over the past three decades, several models were proposed for solar wind forecasting of Dst, including empirical, physics-based, and machine learning approaches. While the ML models generally perform better than models based on the other approaches, there is still room to improve, especially when predicting extreme events. More importantly, we seek solutions that work on the raw, real-time data streams and are agnostic to sensor malfunctions and noise.
In this challenge, your task is to develop models for forecasting Dst that push the boundary of predictive performance, under operationally viable constraints, using the real-time solar-wind (RTSW) data feeds from NOAA’s DSCOVR and NASA’s ACE satellites. Improved models can provide more advanced warning of geomagnetic storms and reduce errors in magnetic navigation systems.