Enhanced lead time for geomagnetic storms

We propose to improve the warning time for the prediction of the effects of solar storms at Earth. This is like meteorology, but not for the kind of weather we experience everyday on the surface of the Earth - rather, we forecast the solar wind flowing around the Earths magnetic field. Solar storms are clouds of plasma threaded by magnetic fields that are ejected from the solar atmosphere with speeds of millions of kilometers per hour. In case a solar superstorm impacts the Earth, an event to be expected every 100 years, technological infrastructures such as power grids and satellites are at risk of failure, and airline crews and astronauts would experience very high levels of radiation. These storms can transfer a part of their energy to the Earths magnetic field, leading to a temporary re-arrangement that is known as a geomagnetic storm. It can result in beautiful northern and southern lights, but may also pose hazards to technologies that we take for granted in our daily life, such as electric power and global navigation systems. To better mitigate these potentially destructive effects, an accurate forecast of solar wind at the Sun-Earth L1 point can be seen as a key technology in space weather research, similar to the groundbreaking nature of reusable rockets or gene- editing in other fields. In this project, we tap into long-term solar wind data sets, with 40 years of available data. This makes it possible to use machine learning algorithms in combination with our own simulation of solar storm magnetic fields to model the future solar wind with a warning time of up to 2 days. We will connect our forecasts with an existing model for the location of the aurora, giving the general public information when and where to see the northern lights. During geomagnetic storms, currents can temporarily be present in the Earths surface, and for very strong events they may lead to power blackouts. Therefore, we will couple the predicted solar wind to a model of these currents at the Central Institution for Meteorology and Geodynamics in Austria, in order to mitigate potential blackouts in central Europe in the future. The accuracy of the predictions will be first tested on already existing data, and later in the project used in a real-time mode. We will also show if future missions based on small spacecraft (CubeSats) could possibly further enhance the forecasts. A timely funding of this project would give Austria an edge in the prediction of geomagnetic storms to further consolidate and strengthen a position of international leadership in the field of space weather.

In this project we used in an innovative way observations by current spacecraft to improve the lead time for the prediction of geomagnetic storms due to impacts of solar storms at Earth. Forecasting the solar wind that interacts with the Earth's magnetosphere is currently severely limited, with accurate forecasts only in the range of one hour lead time. This situation should clearly be enhanced in order to be able to predict the time evolution of geomagnetic storms, which has many consequences ranging from geomagnetically induced currents in power lines on the ground, to energetic particles disturbing satellites, and enhancing radiation for flight personell. During geomagnetic storms the location of the auroral oval moves to lower latitudes, which is of high relevance for aurora tourism and public space weather outreach. The main result of this research project is that we have published a method based on machine learning that is trained and tested on the data on about 350 solar storms, returned by spacecraft such as Wind and STEREO, as they were operating close to the Earths distance to the Sun. By feeding measurements of the first few hours of observations of the magnetic field, speed and density of the solar storms into various machine learning algorithms, we find that our predictive tool can forecast the total magnetic field and its southward oriented component reasonably well. While our tool does not fully solve the problem of solar storm forecasting, it shows promise for potentially mitigating the effects of solar storms on our planet Earth and its inhabitants. We have further made progress on the modeling of the ambient solar wind and the simulation of geomagnetically induced currents, in a collaboration with the Austrian weather service. The project outcomes laid the basis for a more thorough look at how to forecast the southward magnetic field in solar storms in a large research project subsequently funded by the European Research Council. The models developed in this project are now implemented as products at the newly founded Austrian Space Weather Office at the GeoSphere Austria.