Protect the Earth from space storms

“There are only two natural disasters that could impact the entire United States,” said Gabor Toth, professor of climate and space science and engineering at the University of Michigan. “One is a pandemic and the other is an extreme space weather event.”

We are currently seeing the effects of the former in real time.

The last major space weather event struck Earth in 1859. Smaller, but still significant, space weather events occur regularly. These fry electronics and power grids, disrupt global positioning systems, cause shifts in the range of the Northern Lights, and increase the risk of radiation to astronauts or passengers on planes crossing the poles.

“We have all of these tech assets that are at risk,” Toth said. “If an extreme event like the one in 1859 were to happen again, it would completely destroy the power grid, satellites and communications systems – the stakes are much higher. ”

Driven by the White House’s National Space Weather Strategy and Action Plan and the National Strategic Computing Initiative, the National Science Foundation (NSF) and NASA in 2020 created the Space Weather with Quantified Uncertainties (SWQU) program. . It brings together research teams from all scientific disciplines to advance the latest methods of statistical analysis and high performance computing in the field of space weather modeling.

“We are very proud to have launched the SWQU projects by bringing together expertise and support in several scientific fields as part of a joint effort between NSF and NASA,” said Vyacheslav (Slava) Lukin, director of the NSF plasma physics program. “The need has been recognized for some time, and the portfolio of six projects, including that of Gabor Toth, engages not only major academic groups, but also NASA centers, the national laboratories of the Department of Defense and the United Nations. ‘Energy, as well as the private sector. “

Toth helped develop today’s preeminent space weather forecasting model, which is used for operational forecasting by the National Oceanic and Atmospheric Administration (NOAA). On February 3, 2021, NOAA began using version 2.0 of the geospatial model, part of the University of Michigan’s space weather modeling framework, to predict geomagnetic disturbances.

“We are constantly improving our models,” Toth said. The new model replaces version 1.5, which has been operational since November 2017. “The main change in version 2 was the refinement of the digital grid in the magnetosphere, several algorithm improvements and a recalibration of the empirical parameters.”

The geospatial model is based on a global representation of the geospatial environment of the Earth which includes the magnetohydrodynamics, properties and behavior of electrically conductive fluids like plasma interacting with magnetic fields, which play a key role in the dynamics of the space weather.

The geospatial model predicts magnetic disturbances on the ground resulting from geospatial interactions with the solar wind. Such magnetic disturbances induce a geoelectric field which can damage large-scale electrical conductors, such as the power grid.

The model’s short-term early warning provides forecasters and power grid operators with situational awareness of harmful currents and allows time to mitigate the problem and maintain the integrity of the power grid, NOAA said. at the time of launch.

As advanced as the geospatial model is, it only provides about 30 minutes of advanced warning. Toth’s team is one of many groups working to increase the turnaround time to one to three days. It means understanding how activity on the Sun’s surface leads to events that can impact the Earth.

“We are currently using data from a satellite measuring plasma parameters a million kilometers from Earth,” Toth explained. The researchers hope to start from the Sun, using remote observation of the Sun’s surface, particularly coronal mass ejections that produce flares visible in x-rays and UV light. “It happens early on the Sun. From that point, we can run a model and predict the time of arrival and the impact of magnetic events.”

Improving the turnaround time of space weather forecasts requires new methods and algorithms that can compute much faster than those used today and can be efficiently deployed on high performance computers. Toth uses the Texas Advanced Computing Center’s Frontera supercomputer, the world’s fastest university system and the 10th most powerful overall, to develop and test these new methods.

“I consider myself really good at developing new algorithms,” Toth said. “I apply them to space physics, but many of the algorithms I am developing are more general and are not limited to a single application.”

A key algorithmic improvement made by Toth was to find a new way to combine the kinetic and fluid aspects of plasmas in a simulation model. “People have tried it before and failed. But we’ve made it work. We’re going a million times faster than brute force simulations by inventing smart approximations and algorithms,” Toth said.

The new algorithm dynamically adapts the location covered by the kinetic model based on the simulation results. The model identifies regions of interest and places the kinetic model and computational resources on which to focus. This can result in an acceleration of 10 to 100 for space weather models.

As part of the NSF SWQU project, Toth and his team worked to make the space weather modeling framework work effectively on future supercomputers that rely heavily on graphics processing units (GPUs). As a first objective, they set out to port the geospatial model to GPUs using the NVIDIA Fortran compiler with OpenACC directives.

They recently managed to run the full geospatial model faster than real time on a single GPU. They used TACC’s GPU-compatible Longhorn machine to achieve this milestone. To run the model at the same speed on a traditional supercomputer, you need at least 100 processor cores.

“It took a whole year of code development for this to happen,” Toth said. “The goal is to run a set of simulations quickly and efficiently to provide a probabilistic space weather forecast.”

This type of probabilistic forecasting is important for another aspect of Toth’s research: locating the predictions in terms of impact on the Earth’s surface.

“Should we be worried in Michigan or just Canada?” What is the maximum induced current that some transformers will undergo? How long will the generators have to be off? To do it accurately, you need a model that you believe in, “he said.” Whatever we predict, there is always some uncertainty. We want to give forecasts with precise probabilities, similar to terrestrial weather forecasts. ”

Toth and his team run their code in parallel on thousands of cores on Frontera for each simulation. They plan to run thousands of simulations over the coming years to see how model parameters affect the results in order to find the best model parameters and to be able to associate probabilities with the simulation results.

“Without Frontera, I don’t think we can do that research,” Toth said. “When you combine smart people and big computers, great things can happen.”

The Michigan Sun-to-Earth model, including the SWMF Geospace and the new GPU port, is available open source at https://github.com/MSTEM-QUDA.