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Solar coronal mass ejections (CMEs) are large-scale eruptions of plasma and magnetic field from the Sun into the corona and interplanetary space. They are the most significant drivers of adverse space weather at Earth and other locations in…

Solar energetic particles (SEPs) accelerated from shocks driven by coronal mass ejections (CMEs) are one of the major causes of geomagnetic storms on Earth. Therefore, it is necessary to predict the occurrence and intensity of such…

Accurate streamflow prediction largely relies on historical meteorological records and streamflow measurements. For many regions, however, such data are only scarcely available. Facing this problem, many studies simply trained their machine…

In the era of space exploration, the implications of space weather have become increasingly evident. Central to this is the phenomenon of coronal holes, which can significantly influence the functioning of satellites and aircraft. These…

Lightning plays a crucial role in the Earth's climate system, yet existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study…

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale…

Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable…

Halo coronal mass ejections (HCMEs) are responsible of the most severe geomagnetic storms. A prediction of their geoeffectiveness and travel time to Earth's vicinity is crucial to forecast space weather. Unfortunately coronagraphic…

Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly resolve processes larger than 100 km and approximate any…

A large amount of magnetized plasma is frequently ejected from the Sun as coronal mass ejections (CMEs). Some of these ejections are detected in the solar wind as magnetic clouds (MCs) that have flux rope signatures. Magnetic clouds are…

Advances in deep learning systems have allowed large models to match or surpass human accuracy on a number of skills such as image classification, basic programming, and standardized test taking. As the performance of the most capable…

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand…

Lightning flashes are rare albeit hazardous events. Despite this scarcity, generalized additive models (GAMs) succeed in producing a climatology of lightning occurrence for the eastern Alps and surrounding lowlands at an unprecedented…

Large language models (LLMs) have been introduced to time series forecasting (TSF) to incorporate contextual knowledge beyond numerical signals. However, existing studies question whether LLMs provide genuine benefits, often reporting…

The solar cycle and its associated magnetic activity are the main drivers behind changes in the interplanetary environment and Earth's upper atmosphere (commonly referred to as space weather and climate). In recent years there has been an…

We study the evolving environment dependence of mass accretion by dark haloes in simulations of cold and warm dark matter (CDM and WDM) cosmologies. The latter allows us to probe the nature of halo growth at scales below the WDM half-mode…

The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typically activated in response to changes in ocean temperatures…

Scaling laws guide the development of large language models (LLMs) by offering estimates for the optimal balance of model size, tokens, and compute. More recently, loss-to-loss scaling laws that relate losses across pretraining datasets and…

The poleward extent of Earth's zonal-mean Hadley cells varies across seasons and years, which would be nice to capture in a simple theory. A plausible, albeit diagnostic, candidate from Hill et al (2022) combines the conventional two-layer,…

Physical and chemical conditions (kinetic temperature, volume density, molecular composition) of interstellar clouds are inherent in their mm-submm line spectra, making spectral line profiles powerful diagnostics of source conditions. We…