Implemented by data science competition host DrivenData and crowdsourcing platform HeroX, the challenge - MagNet: Model the Geomagnetic Field - seeks to mitigate the impact of geomagnetic storms on navigation systems through improved forecasting by increasing the accuracy in real-time magnetic field modeling and reducing errors in the magnetic navigation systems.
"As we enter the next solar cycle and the navigation technologies we rely on everyday are always advancing, it is all the more important that we are prepared and deploying the most updated environmental information services possible," says Rob Redmon, a space scientist and the lead of the NCEI Solar & Terrestrial Physics Section, NOAA National Centers for Environmental Information (NCEI), which partners with the Cooperative Institute for Research in Environmental Sciences (CIRES) to develop magnetic modeling knowledge and applications. "This is an area where we need input and insight from the crowd."
While GPS provides accurate point locations, it does not provide pointing directions. Therefore, the absolute directional information provided by Earth's magnetic field is of primary importance for navigation and for systems used by aircraft, ships, antennas, satellites, directional-drilling, and smartphones. NCEI and CIRES develop magnetic reference field models to aid navigation and scientific research.
Geomagnetic storms are caused by the transfer of energy from solar wind into Earth's magnetic field, which results in variations in the magnetic field that increase errors in magnetic navigation. Over the past three decades, models have been proposed for forecasting solar winds and variations in the magnetic field, including empirical, physics-based, and machine learning approaches.
While the machine learning models generally perform better than models based on the other approaches, say researchers, there is still much room to improve, especially when predicting extreme events. More importantly, says NOAA, it is seeking solutions that work on the raw, real-time data streams and are agnostic to sensor malfunctions and noise.
The goal of this challenge is to develop models for forecasting variations