Prépublication
· 2026 arXiv
EN
Seasonal prediction of Yangtze summer rainfall is challenged by nonstationary oceanic precursors. Here we demonstrate that accounting for the evolution of spring SST and state-dependent land surface information improves predictability. Graph total variation (G...
Yifu Gao, Haishan Chen et al.
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Prépublication
· 2026 arXiv
EN
Atmospheric composition and chemistry-climate interactions are central to predicting future climate change. Yet simulating chemical and aerosol processes is typically the most expensive component of an atmospheric model and is often highly simplified or prescr...
James Weber, Makoto Kelp
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Prépublication
· 2026 arXiv
EN
In the recent AI Model Intercomparison Project (AIMIP) benchmark, the ACE2.1-ERA5 emulator was shown to underestimate the true near-surface warming trend both during the training period and the out-of-sample extrapolation period. In this work, we present M-ACE...
Alexey Yermakov, Troy Arcomano et al.
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Prépublication
· 2026 arXiv
EN
Utilities request mutual aid crews to restore power after severe storms, and requesting early secures more crews but relies on less accurate forecasts. We examined how forecast lead time affects the value of outage predictions for crew deployment, using operat...
Mohammad Kian Golkar, Luciano Alves de Oliveira et al.
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Prépublication
· 2026 arXiv
EN
Urban hydrodynamic flood models typically rely on a surveyed terrain model, an explicit pipe drainage network, and rain gauge measurements, none of which were available for the inundation following Typhoon Matmo in My Dinh and Tay Mo, Hanoi, on 7 October 2025....
Minh Hoa Nguyen, Mai Anh Tran
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Prépublication
· 2026 arXiv
EN
Conventional deep convection schemes remain a primary source of uncertainty in global climate models, yet replacing them with data-driven alternatives while maintaining physical consistency and numerical stability is an open challenge. We present a hybrid conf...
Xavier J. Levine, Griffin Mooers et al.
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Prépublication
· 2026 arXiv
EN
High-resolution precipitation datasets are essential for weather forecasting, hydrological modeling, and climate risk assessment, yet observation-based gridded products often lack the spatial detail required for regional applications. Here we present PrecipCor...
Bipin Kumar, Rajib Chattopadhyay et al.
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Prépublication
· 2026 arXiv
EN
Inferring climate fields at higher spatio-temporal resolutions from coarse representations is essential for accessing socioeconomic and environmental impacts. The downscaling of modelled climate fields faces a trade-off between data storage constraints, simula...
Yu Huang, Shangshang Yang et al.
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Prépublication
· 2026 arXiv
EN
This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different physical processes can offset each other and obscure their origins. Using a single-column model, each PA...
Yuiko Ichikawa, Edward Groot et al.
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Prépublication
· 2026 arXiv
EN
Under anthropogenic greenhouse forcing, climate models project a decrease in year-to-year temperature variability in the mid-to-high latitudes and an increase in the tropics and subtropics. Here, we examine year-to-year temperature variability in the CESM Larg...
Rory Basinski-Ferris, Ian Eisenman
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Prépublication
· 2026 arXiv
EN
We present the RemoTAP-Hybrid algorithm for global aerosol products retrieved from Multi-Angle Polarimeters (MAP), and apply it to one-year aerosol retrievals from the SPEXone MAP instrument. For the first time, Multiple Collaborated Neural Networks (MCNN) are...
Guangliang Fu, Sha Lu et al.
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Prépublication
· 2026 arXiv
EN
Fronts, eddies, jets, and meanders populate the ocean surface and play a key role in organizing transport and mixing across a wide range of scales. From a Lagrangian perspective, frontal structures are conveniently detected as surface filaments that act as bar...
Sara Cloux, Ismael Hernandez-Carrasco et al.
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