New computer-based space weather model to improve US' solar storm warning system

Washington, Dec. 18 (ANI): A University of Michigan space weather model has been chosen to predict magnetic disturbances due to space weather.

The new model - it's the first time that computer models based on a firm understanding of physics have overtaken simpler, statistics-based models - can also give information about where the effects of a geomagnetic storm will be weaker or stronger around Earth.

Gabor Toth, a research scientist in atmospheric, oceanic and space sciences and one of the model's main developers, said that they can have eruptions such as coronal mass ejections or solar flares, and these propagate all the way from the Sun to the Earth.

In a gale-force solar wind brought on by these eruptions, Earth's magnetic field shakes.

The main fear is that this shaking could knock out the big transformers in the electrical grid.

Direct observations of the solar flares aren't clear enough to tell whether any extreme plasma from the Sun is heading toward us. For that, our first responder is on a small orbit around a point where the gravitational pull of the Sun and Earth are equal, roughly a million miles from Earth. This satellite, the Advanced Composition Explorer (ACE), measures the solar wind.

Toth said that when they start their model from this point, they are able to make quantitative predictions that are better than the empirical models.

Using measurements from ACE, the National Oceanic and Atmospheric Administration's (NOAA) Space Weather Prediction Center has been making forecasts from statistics-based, or empirical, models for space weather. These models work reasonably well for estimating the strength of magnetic fluctuations on a global level within the range of space weather observed before. However, these models cannot predict events outside their statistical comfort zones, like the 1859 storm, or where around Earth magnetic disturbances will occur.

That's why NOAA put out a call for new models that could provide more accurate predictions. They tested two empirical models with local forecast capabilities and three physics-based models, including Michigan's. (ANI)