Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a transformer-based model that forecasts densities up to three days ahead and is intended as a drop-in replacement for an empirical baseline. Unlike recent approaches, it avoids spatial reduction and complex input pipelines, operating directly on a compact input set. Validated on real-world data, the model improves key prediction metrics and shows potential to support mission planning.

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.

The Sun's most complex mysteries could soon be solved thanks to artificial intelligence. On August 20, IBM and NASA announced the launch of Surya, a foundation model for the sun. Having been trained on large datasets of solar activity, this AI tool aims to deepen humanity's understanding of solar weather and accurately predict solar flares--bursts of electromagnetic radiation emitted by our star that threaten both astronauts in orbit and communications infrastructure on Earth. Surya was trained with nine years of data collected by NASA's Solar Dynamics Observatory (SDO), an instrument that has orbited the sun since 2010, taking high-resolution images every 12 seconds. The SDO captures observations of the sun at various different electromagnetic wavelengths to estimate the temperature of the star's layers.

An artificial intelligence model trained on NASA satellite imagery can forecast what the sun will look like hours into the future – even predicting the appearance of solar flares that may warn of dangerous space weather for Earth. "I love to think of this model as an AI telescope where you can look at the sun and you can understand the moods," says Juan Bernabé-Moreno at IBM Research Europe. The sun's moods matter because outbursts of solar activity can bombard Earth with high-energy particles, X-rays and extreme ultraviolet radiation. These can disrupt GPS and communications satellites, and potentially harm astronauts and even people on commercial airlines. Solar flares can be followed by coronal mass ejections, which may disrupt Earth's own magnetic field and create geomagnetic storms capable of knocking out power grids.

Solar extreme ultraviolet (EUV) irradiance plays a crucial role in heating the Earth's ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct SOHO/SEM EUV flux measurements in the period between 1998 and 2014 using CaII K images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using CaII K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of CaII K images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth's climate over extended periods.

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.

Accurate and efficient modeling of Earth's ionosphere has a significant impact on research and operational communities due to its effects on radio communications, radar performance, [1, 2, 3] and satellite drag [4]. Success in forecasting key parameters such as the F2 layer critical frequency (foF2) and height (hmF2) and the total electron content (TEC) allows one to anticipate and mitigate the impacts of ionospheric variability on such systems. Over the past decades, many modeling approaches have been developed to predict these ionospheric parameters with increasing accuracy and skill. These models may be broadly categorized as empirical, physics-based, and, more recently, machine learning methods. Empirical models often rely on extensive historical datasets to establish statistical relationships between ionospheric parameters and geophysical variables. The International Reference Ionosphere (IRI) model [5] is a widely used standard that provides monthly averages of various ionospheric parameters based on many decades of past observations. IRI has seen continual development and improvements over the years, adding a host of submodels used to capture specific aspects of the ionosphere such as the CCIR [6, 7] and URSI [8] foF2 models for representing the diurnal variations of the peak plasma density across the globe, the AMTB [9] and SHU-2015 [10] models for even more harmonic expansions of hmF2, and NeQuick 2 [11] for improved topside electron density accuracy and thus better estimates of TEC [12, 13]. So, while large empirical models like IRI continue to improve, the number of these available options needed to address each domain and source of variance in the ionosphere also grows, and choosing the appropriate settings may be prohibitive without expert knowledge of each submodel.

Geomagnetic indices derived from ground magnetic measurements characterize the intensity of solar-terrestrial interaction. The \textit{Kp} index derived from multiple magnetic observatories at mid-latitude has commonly been used for space weather operations. Yet, its temporal cadence is low and its intensity scale is crude. To derive a new generation of geomagnetic indices, it is desirable to establish a geomagnetic `baseline' that defines the quiet-level of activity without solar-driven perturbations. We present a new approach for deriving a baseline that represents the time-dependent quiet variations focusing on data from Chambon-la-For\^et, France. Using a filtering technique, the measurements are first decomposed into the above-diurnal variation and the sum of 24h, 12h, 8h, and 6h filters, called the daily variation. Using correlation tools and SHapley Additive exPlanations, we identify parameters that dominantly correlate with the daily variation. Here, we predict the daily `quiet' variation using a long short-term memory neural network trained using at least 11 years of data at 1h cadence. This predicted daily quiet variation is combined with linear extrapolation of the secular trend associated with the intrinsic geomagnetic variability, which dominates the above-diurnal variation, to yield a new geomagnetic baseline. Unlike the existing baselines, our baseline is insensitive to geomagnetic storms. It is thus suitable for defining geomagnetic indices that accurately reflect the intensity of solar-driven perturbations. Our methodology is quick to implement and scalable, making it suitable for real-time operation. Strategies for operational forecasting of our geomagnetic baseline 1 day and 27 days in advance are presented.

When Predicting future geomagnetic and solar storms charged particles from flares or CMEs reach Earth, and evaluating their potential impacts requires accurate atmospheric heating and transient solar wind activity solar driver forecasts. To assess forecast performance increase, sometimes resulting in geomagnetic and for a given prediction framework, Space Environment solar storms. With a history of such storms disrupting Technologies (SET) provides a benchmarking communications and power systems and significantly dataset using an archived data set spanning 6 increasing atmospheric drag for Low Earth Orbit years and 15,000 forecasts across Solar Cycle 24 [6]. In (LEO) satellites, accurate space weather activity this work, we employ a multivariate approach using forecasting presents a critical enabling technology for a transformer deep neural network to learn the mapping mitigating space weather-induced outages and satellite from historical solar drivers to future drivers, conjunction risk [1].

This paper proposes an extension to conventional regression Neural Networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the data are independent and identically distributed (i.i.d.). We evaluate the proposed method on four benchmark datasets and on the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC measurements collected over a period of 11 years. Our experimental results show that the prediction intervals produced by our method are both well-calibrated and tight enough to be useful in practice.