A coronal mass ejection captured in 2002, showing over a billion tons of matter being sent out into space.ESA/NASA/Soho
The Solar Spacewatch data has been put to use by running it through a new solar wind model that can ramp up the number of complex simulations it can run by a factor of 10. Where the older model could run 20 simulations at once, the newer model, developed by Reading Professor Mathew Owens can hit 200.
CMEs vary in speed, the slowest ones move around 559,234 MPH (250 kilometers per second) while the quickest can travel at approximately 6,710,809 MPH ( 3000 km/s.) While the slowpokes take weeks to reach Earth, the fastest and most potentially harmful CMES can reach the planet in just 15 to 18 hours.
For over a century, the Carrington Event was the touchstone event of astronomers discussing the potential damage of a CME. But all that changed in 2012, when an equally powerful, if not greater, solar storm barely missed Earth.
The near-miss caused scientists in 2014 to calculate the chances of a Carrington-level event hitting the Earth in the next ten years, and found the risk was at a surprisingly high 12 percent. If such a threat ever does emerge, humanity might have a head start that to the volunteers of Solar Stormwatch.
Abstract: Predicting the arrival of coronal mass ejections (CMEs) is one key objective of space weather forecasting. In operational space weather forecasting, solar wind numerical models are used for this task and ensemble techniques are being increasingly explored as a means to improve these forecasts. Currently, these forecasts are not constrained by the available in situ and remote sensing observations, such as those from the heliospheric imagers (HIs) on the National Aeronautics and Space Administration’s (NASA’s) STEREO spacecraft, which record white‐light images of solar wind and CMEs. We report case studies of four CMEs and show how HI observations can be used to improve the skill and reduce the uncertainty of ensemble hindcasts of these events. Using a computationally efficient solar wind model, we produce 200‐member ensemble hindcasts, perturbing the modeled CME parameters within uniform distributions about the best estimates. By comparing the trajectory of the modeled CME flanks with HI observations, we compute a weight for each ensemble member. Weighting the ensemble distribution of CME arrival times improves the skill and reduces the hindcast uncertainty of each event. For these four events, the weighted ensembles show a mean reduction in arrival time error of 20.1 ± 4.1%, and a mean reduction in arrival time uncertainty of 15.0 ± 7.2%, relative to the unweighted ensembles. This technique could be applied in operational space weather forecasting, if real‐time HI observations were available. Therefore, as NASA and the European Space Agency are currently planning the next space weather monitoring missions, our proof‐of‐concept study provides some evidence of the potential value of including HIs on these missions.