Cover Artificial Intelligence for the Earth Systems
Artificial Intelligence for the Earth Systems

 

Volume 2 (2023): Issue 4 (Oct 2023)

Online ISSN:
2769-7525
Cover Artificial Intelligence for the Earth Systems
MASTHEAD
Masthead
EDITORIAL
Equity, Inclusion, and Justice: An Opportunity for Action for AMS Publications Stakeholders
AMS Publications Support for Open, Transparent, and Equitable Research
PERSPECTIVES
Environment-Aware Digital Twins: Incorporating Weather and Climate Information to Support Risk-Based Decision-Making
Perspectives on Artificial Intelligence for Predictions in Ecohydrology
REVIEW
A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena
ARTICLES
ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing
Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network
Assessing Tropical Pacific–Induced Predictability of Southern California Precipitation Using a Novel Multi-Input Multioutput Autoencoder
A Deep Learning Filter for the Intraseasonal Variability of the Tropics
Cross-Validation Strategy Impacts the Performance and Interpretation of Machine Learning Models
Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill
Exploring Randomly Wired Neural Networks for Climate Model Emulation
Multivariate Emulation of Kilometer-Scale Numerical Weather Predictions with Generative Adversarial Networks: A Proof of Concept
Can Machine Learning Models Be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Time Scales?
Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning
Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models
Statistical Modeling of Monthly and Seasonal Michigan Snowfall Based on Machine Learning: A Multiscale Approach
Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands
Understanding Cloud Systems’ Structure and Organization Using a Machine’s Self-Learning Approach
Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning
The Development and Initial Capabilities of ThunderCast, a Deep Learning Model for Thunderstorm Nowcasting in the United States
Machine Learning for Nonorographic Gravity Waves in a Climate Model
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales
Physics-Constrained Deep Learning Postprocessing of Temperature and Humidity
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