Artificial Intelligence for the Earth Systems
CURRENT ISSUE
Volume 2 (2023): Issue 4 (Oct 2023)
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.
Self Supervised Cloud Classification
Physics-inspired adaptions to low-parameter neural network weather forecasts systems.
A Machine Learning Explainability Tutorial for Atmospheric Sciences
Deep Learning Image Segmentation for Atmospheric Rivers
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification
Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset
Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture
Physics-constrained deep learning postprocessing of temperature and humidity
Deep Learning Parameterization of Vertical Wind Velocity Variability via Constrained Adversarial Training
Perspectives on AI Architectures and Co-design for Earth System Predictability
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales
Volume 2 (2023): Issue 4 (Oct 2023)
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EDITORIAL
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PERSPECTIVES
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REVIEW
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ARTICLES
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
Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science?
Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning
Application of Deep Learning to Understanding ENSO Dynamics
Global Mesoscale Ocean Variability from Multiyear Altimetry: An Analysis of the Influencing Factors
Global Extreme Heat Forecasting Using Neural Weather Models
Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience
Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning
Downscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks
Most read articles since 2022:
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