The solar wind, a continuous stream of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Variations such as high-speed streams and coronal mass ejections can disrupt satellites, power grids, and communications, making accurate modeling essential for space weather forecasting. While 3D magnetohydrodynamic (MHD) models are used to simulate and investigate these variations in the solar wind, they tend to be computationally expensive, limiting their usefulness in investigating the impacts of boundary condition uncertainty. In this work, we develop a surrogate for steady state solar wind modeling, using a Spherical Fourier Neural Operator (SFNO). We compare our model to a previously developed numerical surrogate for this task called HUX, and we show that the SFNO achieves comparable or better performance across several metrics. Though HUX retains advantages in physical smoothness, this underscores the need for improved evaluation criteria rather than a flaw in SFNO. As a flexible and trainable approach, SFNO enables efficient real-time forecasting and can improve with more data. The source code and more visual results are available at https://github.com/rezmansouri/solarwind-sfno-velocity.

Interplanetary coronal mass ejections (ICMEs) are major drivers of space weather disturbances, posing risks to both technological infrastructure and human activities. Automatic detection of ICMEs in solar wind in situ data is essential for early warning systems. While several methods have been proposed to identify these structures in time series data, robust real-time detection remains a significant challenge. In this work, we present ARCANE - the first framework explicitly designed for early ICME detection in streaming solar wind data under realistic operational constraints, enabling event identification without requiring observation of the full structure. Our approach evaluates the strengths and limitations of detection models by comparing a machine learning-based method to a threshold-based baseline. The ResUNet++ model, previously validated on science data, significantly outperforms the baseline, particularly in detecting high-impact events, while retaining solid performance on lower-impact cases. Notably, we find that using real-time solar wind (RTSW) data instead of high-resolution science data leads to only minimal performance degradation. Despite the challenges of operational settings, our detection pipeline achieves an F1-Score of 0.37, with an average detection delay of 24.5% of the event's duration while processing only a minimal portion of the event data. As more data becomes available, the performance increases significantly. These results mark a substantial step forward in automated space weather monitoring and lay the groundwork for enhanced real-time forecasting capabilities.

If there is extraterrestrial life in our solar system, experts have long thought it could be hiding beneath Mars' surface, in Venus' clouds or in the icy oceans of Jupiter and Saturn's moons. NASA scientists say Uranus' moons Titania and Oberon may also have oceans warm enough to support life, suggesting that we should look there too in our hunt for aliens close to home. They made their discovery after re-analysing data from Voyager 2's close flybys of Uranus in the 1980s, as well as using computer modelling to look for signs of water on five of the planet's largest icy moons. It is the first piece of research to establish how the interior makeup and structure has evolved on Ariel, Umbriel, Titania, Oberon, and Miranda. Distant ice worlds: NASA scientists say Uranus' moons Titania and Oberon may have oceans warm enough to support life.

Like a tornado siren for life-threatening storms in America's heartland, a new computer model that combines artificial intelligence (AI) and NASA satellite data could sound the alarm for dangerous space weather. The model uses AI to analyze spacecraft measurements of the solar wind (an unrelenting stream of material from the Sun) and predict where an impending solar storm will strike, anywhere on Earth, with 30 minutes of advance warning. This could provide just enough time to prepare for these storms and prevent severe impacts on power grids and other critical infrastructure. The solar wind is a gusty stream of material that flows from the Sun in all directions, all the time, carrying the Sun's magnetic field out into space. While it is much less dense than wind on Earth, it is much faster, typically blowing at speeds of one to two million miles per hour.

Intense solar storms can cause electrical blackouts. Like a tornado siren for life-threatening storms in America's heartland, a new computer model that combines artificial intelligence (AI) and NASA satellite data could sound the alarm for dangerous space weather. The model uses AI to analyze spacecraft measurements of the solar wind (an unrelenting stream of material from the Sun) and predict where an impending solar storm will strike, anywhere on Earth, with 30 minutes of advance warning. This could provide just enough time to prepare for these storms and prevent severe impacts on power grids and other critical infrastructure. The solar wind is a gusty stream of material that flows from the Sun in all directions, all the time, carrying the Sun's magnetic field out into space.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 False Positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.

The European Space Agency's Solar Orbiter spacecraft has captured the reversal of the Sun's magnetic field on camera for the first time. These reversals, known as magnetic switchbacks, have previously been hypothesised, but until now have not been observed directly. The new observation provides a full view of the structure and confirms that magnetic switchbacks have an S-shaped character. ESA hopes the footage will help to unravel the mystery of how their physical formation mechanism might help accelerate solar winds. Scientists develop a'recipe' for parents to stop babies crying Meghan Markle's handshake is ignored by member of the public Kremlin journalist admits Russia is losing'huge number of people' Thousands gather for arrival of Queen's coffin at Buckingham Palace The European Space Agency's Solar Orbiter spacecraft has captured the reversal of the Sun's magnetic field on camera for the first time.

In the current "space weather" study, an international team headed by the Central Institute of Meteorology and Geodynamics (ZAMG) and the Institute for Space Research (IWF) of the Austrian Academy of Sciences was able to create static solar wind models using new machine learning – combining algorithms and thus improving space weather forecasting. June 17, 2021 – Space weather not only ensures remarkable light processes, also known as polar lights, but can also have a huge impact on our modern technologies. So-called geomagnetic storms, for example, can have a significant impact on power supplies, GPS and other communications systems that our modern society depends on. The expansion of our space programs and the increasing human presence in space, such as the International Space Station or soon again on the Moon, require an accurate prediction of the solar wind. The solar wind is a stream of charged particles that spreads from our central star into space and also hits the Earth's magnetic field.

The space between the Sun and the Earth is not empty. Instead, it is filled with streams of plasma (ions and electrons) called the solar wind, which travels nearly radially out from the Sun. Since the earliest spacecraft measurements, the solar wind has broadly been classified into two types, fast and slow, based solely its speed (Neugebauer and Snyder, 1966; Stakhiv et al., 2015). This duality has also been observed in measurements of the elemental composition and ion charge states of the solar wind, suggesting that the fast and slow wind originate from different solar source structures (von Steiger et al., 2000; Geiss, Gloeckler, and Von Steiger, 1995). Fast wind is found to originate from coronal holes (Sheeley, Harvey, and Feldman, 1976). These are magnetically open regions of the corona where the plasma can freely escape, meaning that coronal holes appear dark in EUV emission (since there is less time for the plasma to be heated). The formation and release of the slow wind is a ...

MMS look for explosive reconnection events as it flies through the magnetopause -- the boundary region where Earth's magnetic butts up against the solar wind that flows throughout the solar system. An alert pops up in your email: The latest spacecraft observations are ready. You now have 24 hours to scour 84 hours-worth of data, selecting the most promising split-second moments you can find. The data points you choose, depending on how you rank them, will download from the spacecraft in the highest possible resolution; researchers may spend months analyzing them. Everything else will be overwritten like it was never collected at all.