Artificial Intelligence for the Earth Systems Bulletin of the American Meteorological Society Community Science Earth Interactions Journal of Applied Meteorology and Climatology Journal of Atmospheric and Oceanic Technology Journal of Climate Journal of Hydrometeorology Journal of Physical Oceanography Journal of the Atmospheric Sciences Monthly Weather Review Weather and Forecasting Weather, Climate, and Society Meteorological Monographs

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Artificial Intelligence for the Earth Systems

Issue Journal

Early Online Release

Online ISSN:: 2769-7525

Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales

By: Nicolaas J. Annau, Alex J. Cannon, and Adam H. Monahan

Environment-aware digital twins: incorporating weather and climate information to support risk-based decision-making.

By: Kirstine I. Dale, Edward C. D. Pope, Aaron R. Hopkinson, Theo McCaie, and Jason A. Lowe

Understanding cloud systems structure and organization using a machine’s self-learning approach

By: Dwaipayan Chatterjee, Claudia Acquistapace, Hartwig Deneke, and Susanne Crewell

Improving precipitation nowcasting for high-intensity events using deep generative models with balanced loss and temperature data: a case study in the Netherlands

By: Charlotte Cambier van Nooten, Koert Schreurs, Jasper S. Wijnands, Hidde Leijnse, Maurice Schmeits, Kirien Whan, and Yuliya Shapovalova

Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning

By: Jordan Richards, Raphaël Huser, Emanuele Bevacqua, and Jakob Zscheischler

Multivariate emulation of kilometer-scale numerical weather predictions with generative adversarial networks: a proof-of-concept

By: Clément Brochet, Laure Raynaud, Nicolas Thome, Matthieu Plu, and Clément Rambour

Statistical modeling of monthly and seasonal Michigan snowfall based on machine learning: A multiscale approach

By: Lei Meng and Laiyin Zhu

Perspectives on Artificial Intelligence for Predictions in Ecohydrology

By: Elias C. Massoud, Forrest Hoffman, Zheng Shi, Jinyun Tang, Elie Alhajjar, Mallory Barnes, Renato K. Braghiere, Zoe Cardon, Nathan Collier, Octavia Crompton, P. James Dennedy-Frank, Sagar Gautam, Miquel A Gonzalez-Meler, Julia K. Green, Charles Koven, Paul Levine, Natasha MacBean, Jiafu Mao, Richard Tran Mills, Umakant Mishra, Maruti Mudunuru, Alexandre A. Renchon, Sarah Scott, Erica R. Siirila-Woodburn, Matthias Sprenger, Christina Tague, Yaoping Wang, Chonggang Xu, and Claire Zarakas

Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skill

By: Lauren Hoffman, Matthew R. Mazloff, Sarah T. Gille, Donata Giglio, Cecilia M. Bitz, Patrick Heimbach, and Kayli Matsuyoshi

Pages: 1–45

Short-Term (Seven-Day) Beaufort Sea-Ice Extent Forecasting with Deep Learning

By: Mary Ruth Keller, Christine Piatko, Mary Versa Clemens-Sewall, Rebecca Eager, Kevin Foster, Christopher Gifford, Derek Rollend, and Jennifer Sleeman

Pages: 1–36

Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Timescales?

By: Selina M. Kiefer, Sebastian Lerch, Patrick Ludwig, and Joaquim G. Pinto

Pages: 1–36

Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models

By: Pedro Ortiz, Eleanor Casas, Marko Orescanin, Scott W. Powell, Veljko Petkovic, and Micky Hall

Pages: 1–42

Cross-validation strategy impacts the performance and interpretation of machine learning models

By: Lily-belle Sweet, Christoph Müller, Mohit Anand, and Jakob Zscheischler

Pages: 1–35

Exploring Randomly Wired Neural Networks for Climate Model Emulation

By: William Yik, Sam J. Silva, Andrew Geiss, and Duncan Watson-Parris

Pages: 1–34

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