Every year, the newsroom produces a list of some of the most promising climate tech firms on the planet. It's an exercise that we hope brings positive attention to companies working to decarbonize major sectors of the economy, whether by spinning up new, cleaner sources of energy or reinventing how we produce foods and distribute goods. Though the political and funding landscape has shifted dramatically in the US since last year, nothing has altered the urgency of the climate dangers the world now faces--we need to rapidly curb greenhouse gas emissions to avoid the most catastrophic impacts of climate change. This project highlights the firms making progress toward that end. Check out the third annual edition of the list, and learn more about why we selected these companies . It's a foregone conclusion that the world will not meet the goals for limiting emissions and global warming laid out in the 2015 Paris Agreement.

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.

This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar EUV spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.

April 2024 As the number of satellites in space grows, and as we rely on them for increasing numbers of vital tasks on Earth, the need to better predict stormy space weather is becoming more and more urgent. Scientists have long known that solar activity can change the density of the upper atmosphere. But it's incredibly difficult to precisely predict the sorts of density changes that a given amount of solar activity would produce. Now, experts are working on a model of the upper atmosphere to help scientists to improve their models of how solar activity affects the environment in low Earth orbit. If they succeed, they'll be able to keep satellites safe even amid turbulent space weather, reducing the risk of potentially catastrophic orbital collisions.

Observing and forecasting coronal mass ejections (CME) in real-time is crucial due to the strong geomagnetic storms they can generate that can have a potentially damaging effect, for example, on satellites and electrical devices. With its near-real-time availability, STEREO/HI beacon data is the perfect candidate for early forecasting of CMEs. However, previous work concluded that CME arrival prediction based on beacon data could not achieve the same accuracy as with high-resolution science data due to data gaps and lower quality. We present our novel pipeline entitled ''Beacon2Science'', bridging the gap between beacon and science data to improve CME tracking. Through this pipeline, we first enhance the quality (signal-to-noise ratio and spatial resolution) of beacon data. We then increase the time resolution of enhanced beacon images through learned interpolation to match science data's 40-minute resolution. We maximize information coherence between consecutive frames with adapted model architecture and loss functions through the different steps. The improved beacon images are comparable to science data, showing better CME visibility than the original beacon data. Furthermore, we compare CMEs tracked in beacon, enhanced beacon, and science images. The tracks extracted from enhanced beacon data are closer to those from science images, with a mean average error of $\sim 0.5 ^\circ$ of elongation compared to $1^\circ$ with original beacon data. The work presented in this paper paves the way for its application to forthcoming missions such as Vigil and PUNCH.

Space weather forecasting is critical for mitigating radiation risks in space exploration and protecting Earth-based technologies from geomagnetic disturbances. This paper presents the development of a Machine Learning (ML)- ready data processing tool for Near Real-Time (NRT) space weather forecasting. By merging data from diverse NRT sources such as solar imagery, magnetic field measurements, and energetic particle fluxes, the tool addresses key gaps in current space weather prediction capabilities. The tool processes and structures the data for machine learning models, focusing on time-series forecasting and event detection for extreme solar events. It provides users with a framework to download, process, and label data for ML applications, streamlining the workflow for improved NRT space weather forecasting and scientific research.

In the era of space exploration, coronal holes on the sun play a significant role due to their impact on satellites and aircraft through their open magnetic fields and increased solar wind emissions. This study employs computer vision techniques to detect coronal hole regions and estimate their sizes using imagery from the Solar Dynamics Observatory (SDO). Additionally, we utilize deep learning methods, specifically Long Short-Term Memory (LSTM) networks, to analyze trends in the area of coronal holes and predict their areas across various solar regions over a span of seven days. By examining time series data, we aim to identify patterns in coronal hole behavior and understand their potential effects on space weather. This research enhances our ability to anticipate and prepare for space weather events that could affect Earth's technological systems.

ABSTRACT The need of real-time of monitoring and alerting systems for Space Weather hazards has grown significantly in the last two decades. One of the most important challenge for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of a SPE, i.e., they are based on the classification approach. In this work we present a simple and efficient machine learning regression algorithm which is able to forecast the energetic proton flux up to 1 hour ahead by exploiting features derived from the electron flux only. This approach could be helpful to improve monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance. INTRODUCTION Solar Proton Events (SPEs) are pronounced enhancements of the energetic proton flux measured by instruments placed on different space probes across the Heliosphere. Solar protons can reach high energies, say tens of GeVs, as a consequence of different acceleration processes occurring at the Sun in association with transient phenomena like solar flares and coronal mass ejections (CMEs; Kahler et al. 1984; Shea & Smart 1990; Aschwanden 2002; Iucci et al. 2005). Then, particles travel along interplanetary magnetic field lines and can produce a geoeffective SPE that can be detected by instruments placed on Earth-orbiting satellites, such as the Geostationary Operational Environmental Satellite (GOES).

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1-minute and 5-minute resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data. When predicting the SYM-H indices (2 hours in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

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.