Artificial Intelligence for the Earth Systems
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ISSN: 2769-7525|Disc:Artificial Intelligence for the Earth Systems (AIES) is 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.|Priority:4|
Recent Submissions
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Airborne Radar Quality Control with Machine Learning
(American Meteorological Society, 2024) -
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
(American Meteorological Society, 2024) -
A Rose by Any Other Name: On Basic Scores from the 2 × 2 Table and the Plethora of Names Attached to Them
(American Meteorological Society, 2024) -
Part I: Improving Wildfire Occurrence Prediction for CONUS Using Deep Learning and Fire Weather Variables
(American Meteorological Society, 2024) -
Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures
(American Meteorological Society, 2024) -
A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in Kilometer-Scale Forecasts
(American Meteorological Society, 2024) -
Neural Networks to Find the Optimal Forcing for Offsetting the Anthropogenic Climate Change Effects
(American Meteorological Society, 2024) -
Forecasting Ocean Waves off the U.S. East Coast Using an Ensemble Learning Approach
(American Meteorological Society, 2024) -
Transformer-Based Nowcasting of Radar Composites from Satellite Images for Severe Weather
(American Meteorological Society, 2024) -
Automated Large-Scale Tornado Treefall Detection and Directional Analysis Using Machine Learning
(American Meteorological Society, 2024) -
Emulating Rainfall–Runoff-Inundation Model Using Deep Neural Network with Dimensionality Reduction
(American Meteorological Society, 2023) -
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
(American Meteorological Society, 2024) -
Deep Learning Image Segmentation for Atmospheric Rivers
(American Meteorological Society, 2024) -
Physics-Inspired Adaptions to Low-Parameter Neural Network Weather Forecast Systems
(American Meteorological Society, 2024) -
A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas
(American Meteorological Society, 2024) -
Limitations of XAI Methods for Process-Level Understanding in the Atmospheric Sciences
(American Meteorological Society, 2024) -
Machine Learning–Based Lightning Parameterizations for the CONUS
(American Meteorological Society, 2024) -
Environmental Justice and Lessons Learned from COVID-19 Outcomes—Uncovering Hidden Patterns with Geometric Deep Learning and New NASA Satellite Data
(American Meteorological Society, 2024) -
Storm Gust Prediction with the Integration of Machine Learning Algorithms and WRF Model Variables for the Northeast United States
(American Meteorological Society, 2024) -
Self-Supervised Cloud Classification
(American Meteorological Society, 2024)