Artificial Intelligence for the Earth Systems
CURRENT ISSUE
Volume 4 (2025): Issue 1 (Jan 2025)
About the Journal
Artificial Intelligence for the Earth Systems (AIES) publishes research on the development and application of methods in Artificial Intelligence (AI), Machine Learning (ML), data science, and statistics that is relevant to meteorology, atmospheric science, hydrology, climate science, and ocean sciences. Topics include development of AI/ML, statistical, and hybrid methods and their application; development and application of methods to further the physical understanding of earth system processes from AI/ML models such as explainable and physics-based AI; the use of AI/ML to emulate components of numerical weather and climate models; incorporation of AI/ML into observation and remote sensing platforms; the use of AI/ML for data assimilation and uncertainty quantification; and societal applications of AI/ML for AIES disciplines, including ethical and responsible use of AI/ML and educational research on AI/ML.
ISSN: 2769-7525
AIES is a fully Open Access journal.
Volume 4 (2025): Issue 1 (Jan 2025)
Most cited articles since 2022:
Investigating the Fidelity of Explainable Artificial Intelligence Methods for Applications of Convolutional Neural Networks in Geoscience
Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook
Cross-Validation Strategy Impacts the Performance and Interpretation of Machine Learning Models
A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena
Application of Deep Learning to Understanding ENSO Dynamics
Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience
Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications
Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers
Emulating the Adaptation of Wind Fields to Complex Terrain with Deep Learning
Finding the Right XAI Method—A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
Most read articles since 2022:
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