Prépublication
· 2026 arXiv
EN
This paper describes an approach to constructing nowcast and forecast predictive models for the occurrence of high-latitude decameter ionospheric irregularities based on SuperDARN and SECIRA radar data and space weather parameters. The method relies on self-le...
Oleg I. Berngardt, Ivan A. Lavygin
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Prépublication
· 2026 arXiv
EN
Land use regression models relate intra-urban air temperature to spatial predictors such as building volume, tree canopy cover and topography, and are increasingly used to produce fine-scale temperature maps. However, the accuracy of these estimates can be com...
Martí Bosch, Aldo Brandi et al.
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Prépublication
· 2026 arXiv
EN
Short-range hindcasts of measured wave spectra are evaluated using a recurrent model initialized by one hourly buoy spectrum and supplied with local wind, fetch and depth. Measurements from three Baltic Sea stations are combined for training in 2017--2020, val...
Sander Rikka
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Prépublication
· 2026 arXiv
EN
Satellite precipitation products face a persistent trade-off: real-time estimates are timely but less accurate, whereas the most reliable products are often released months after observation. This delay keeps high-quality precipitation information outside the...
Zijiang Song, Shuang Yang et al.
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Prépublication
· 2026 arXiv
EN
Artificial intelligence (AI) weather models now forecast weather events skillfully, but how they represent these events internally remains unknown. Sparse autoencoders (SAEs) provide sparse features for probing hidden states, but how information about multiple...
Jiapei Liu, Ruidong Li et al.
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Prépublication
· 2026 arXiv
EN
Attribution of observed historical climate evolution to externally forced change and internal variability is fundamental to understanding climate change and evaluating climate models. Yet such attribution remains challenging because the forced response is inte...
Parvathi Kooloth, Wenyu Zhou et al.
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Prépublication
· 2026 arXiv
EN
Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly due to imperfect land surface representations in Earth system models. We propose a data-driven framework for high-resolution land-use reconstruction, treated...
Amirpasha Mozaffari, Marina Castaño et al.
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Prépublication
· 2026 arXiv
EN
Effectively preparing for changes in extreme flooding requires understanding present and future rainfall extremes with return periods up to $\sim$100-1000 years, including changes in intensity, spatial structure at km-scale and sub-daily temporal variability....
Henry Addison, Elizabeth J. Kendon et al.
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Prépublication
· 2026 arXiv
EN
Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health. Traditional numerical forecasting systems suffer from incomplete parameterizations, uncertain inputs, and high computational cost. Ex...
Yiheng Wang, Kai Zheng et al.
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Prépublication
· 2026 arXiv
EN
Radiance-based retrievals and data assimilation require radiative transfer models (RTMs) to simulate radiances and quantify their sensitivities to atmospheric and surface conditions. Conventional RTMs often rely on separately maintained forward, tangent-linear...
Zeting Li, Wei Han et al.
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Prépublication
· 2026 arXiv
EN
AI weather prediction models are often regarded as black boxes. Despite their impressive skill, there is little understanding of how the input data is processed during inference to produce the forecast. This limitation arises from the use of high-dimensional l...
Matthias Beylich, Kirsten I. Tempest et al.
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Prépublication
· 2026 arXiv
EN
Earth system models underpin the evidence that informs net-zero policy, yet their energy demands are rarely considered when judging model quality. Using archived output from 159 models spanning five generations of the Coupled Model Intercomparison Project (CMI...
Ioana Colfescu, Annalisa Bracco et al.
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