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The influence of solar variability on climate is currently uncertain. Recent observations have indicated a possible mechanism via the influence of solar modulated cosmic rays on global cloud cover. Surprisingly the influence of solar…
Mesoscale convective systems MCSs play a central role in tropical rainfall and are closely linked to extreme precipitation and large scale variability. However, a quantitative understanding of their environmental controls remains…
Large language models (LLMs) exhibit remarkable flexibility: they can adapt to novel tasks from in-context examples without any parameter updates, a capability known as in-context learning (ICL). Prior work on synthetic tasks has shown that…
Large Language Models (LLMs) stand at the forefront of a number of Natural Language Processing (NLP) tasks. Despite the widespread adoption of LLMs in NLP, much of their potential in broader fields remains largely unexplored, and…
Over the past few decades, the development of cellular communication technology has spanned several generations in order to add sophisticated features in the updated versions. Moreover, different high-frequency bands are considered for…
Halo coronal mass ejections (HCMEs) originating from regions close to the center of the Sun are likely to be responsible for severe geomagnetic storms. It is important to predict geo-effectiveness of HCMEs using observations when they are…
Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using Neural General…
Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. LLMs take a prompt consisting of requirement-code examples and a new requirement as input, and output new programs. Existing studies…
Large Language Models (LLMs) exhibit positional bias, struggling to utilize information from the middle or end of long contexts. Our study explores LLMs' long-context reasoning by probing their hidden representations. We find that while…
The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine…
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control…
As global climate change intensifies, accurate weather forecasting has become increasingly important, affecting agriculture, energy management, environmental protection, and daily life. This study introduces a hybrid model combining…
Generative Large Language Models (LLMs) are capable of being in-context learners. However, the underlying mechanism of in-context learning (ICL) is still a major research question, and experimental research results about how models exploit…
Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their ability to forecast future events remains understudied. A year ago, large language models struggle to come close to the accuracy of a…
While machine learning (ML) post-processing of convection-allowing model (CAM) output for severe weather hazards (large hail, damaging winds, and/or tornadoes) has shown promise for very short lead times (0-3 hours), its application to…
Near-surface atmospheric conditions can differ sharply over tens to hundreds of meters due to land cover and topography, yet this variability is absent from current weather analyses and forecasts. It is unclear whether such meter-scale…
New analysis confirms our earlier claim [1], [7] of circles of notably low temperature variance, often in concentric sets, in the cosmic microwave background (CMB), discernable in WMAP data. Their reality can be interpreted as evidence of…
Solar flares and coronal mass ejections (CMEs) cause immediate and adverse effects on the interplanetary space and geospace. The deeper understanding of the mechanisms that produce them and the construction of efficient prediction schemes…
Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern…
Knowledge about the background solar wind plays a crucial role in the framework of space weather forecasting. In-situ measurements of the background solar wind are only available for a few points in the heliosphere where spacecraft are…