Forecasting a solar storm 30 minutes before it occurs would require a highly advanced and accurate AI tool that can analyze and interpret data in real-time.
To develop such a tool, a vast amount of data would need to be collected and analyzed from multiple sources, including satellite observations, ground-based instruments, and historical data. Machine learning algorithms could then be trained on this data to detect patterns and correlations that are indicative of an impending solar storm.
In order to provide accurate forecasts in real-time, the AI tool would need to be highly optimized for speed and efficiency, and incorporate the latest advances in computational techniques such as parallel processing.
Additionally, the AI tool would need to be constantly updated and validated with new data to ensure that it remains accurate and reliable over time.
Overall, developing an AI tool that can forecast a solar storm 30 minutes before it occurs would be a complex and challenging task, but one that could have significant benefits for space weather forecasting and the protection of critical infrastructure on Earth.
The effects of these geomagnetic storms range from mild to extreme, but in an increasingly technology-dependent world, their effects are becoming increasingly devastating. A new model utilizes AI to inspect spacecraft calculations of the solar wind (the constant pass of matter from the sun) to forecast where on Earth an circumvent solar storm will occur with 30 minutes of advance warning. Do. This could get permitted ample time for preparation for these storms and avoid severe impacts to power grids and other critical infrastructure
“This artificial intelligence will allow us to make fast and correct global forecasts and notify decision-making in the event of a solar storm, which could be accomodate or even prevented.” .”
said Vishal Upendran of Inter-University. Indian Center for Astronomy and Astrophysics (IUCAA).
A team of experimenters at the Frontier Development Lab, a public-private partnership that includes NASA, the U.S. Geological Survey, the U.S. Department of Energy, and the IUCAA, applied the AI method “deep learning” and developed a computer model called “deep learning.” DAGGER (Formally, Deep Learning Geomagnetic Perturbation).
Forecasts can be generated in less than a second, and forecasts are updated every minute.
Using replicas like DAGGER could one day set off solar storm sirens that sound the fear in power plants and satellite control centers around the world, researchers say.
The DAGGER team examined the model against two geomagnetic tempests that occurred in August 2011 and March 2015. In each case, DAGGER was able to quickly and accurately predict the effects of storms around the world.
DAGGER is the first to combine rapid AI analytics with real world measurements from space and across the planet to generate fast, accurate and frequently updated predictions for sites around the world.
The computer code for the DAGGER model is open source, and Upendran says it can be adopted by power grid operators, satellite controllers, telecommunications companies, and others to adapt the predictions to their specific needs.
Such warnings protect assets and infrastructure from impending solar storms, such as temporarily taking sensitive systems offline or moving satellites to alternate orbits to minimize damage. You can give them time to take action.