The 23rd Conference on Artificial Intelligence for Environmental Science is sponsored by the American Meteorological Society and organized by the AMS Committee on Artificial Intelligence .
Call for Papers
The 23rd Conference on Artificial Intelligence for Environmental Science is soliciting papers and posters from the community on the following topics:
Advances in the Use of Artificial Intelligence in Support of Aviation, Range, and Aerospace Meteorology
AI Advances in Tropical Meteorology: Tropical Cyclones, Sub-seasonal Phenomena, and More
AI for Radars: Observations, Data Processing, Classification, Estimation, and Nowcasting
AI for Statistical Parameterization of Unresolved Processes in Earth System Models
Applications of Artificial Intelligence for Improved Estimation and Prediction of Extreme Precipitation and/or Drought in Weather and Climate
Applications of Artificial Intelligence to the Coastal Environment
Applications of Machine Learning to Remote Sensing of Aerosol, Cloud, and Precipitation Properties
Applying Uncertainty Quantification Methods to Environmental Artificial Intelligence Models
Artificial Intelligence for Actionable Insights and Applications in Climate Science
Artificial Intelligence for Environmental Science
Big Data, Big Computing, Bigger Science: High-Performance Computing Enabled Artificial Intelligence/Machine Learning in Earth System Science
Challenges and Opportunities for Interdisciplinary Communication in AI and Climate
Data Quality and Provenance for Artificial Intelligence (AI) and Machine Learning (ML) Applications
Explainable Artificial Intelligence (XAI) for Environmental Science
From Data to Decision: AI at the Intersection of Meteorology, Climate, and Society
Improvements to Subseasonal-to-Seasonal (S2S) Predictions using Novel Statistical and Artificial Intelligence/Machine Learning (AI/ML) Methods
Improving AI literacy for the weather, water, and climate enterprise
Language Transformer, ChatGPT and Other Natural Language Processing Machine Learning in the Weather Enterprise
Leveraging Unsupervised Machine Learning in Environmental Science
LSTM and Other Time Series Machine Learning Methods for Time Series Prediction
Machine Learning Applications in the Energy Sector
Machine Learning for Smoke and Wildfires
Narrating Artificial Intelligence Methods and Advancements for Environmental Science through Images and Storytelling
Open Datasets for Artificial-intelligence Research and Applications in Earth and Atmospheric Sciences
Physical Interpretability of Empirical Models
Predicting Sea-Level Rise Using Machine Learning Methodology and Economics Models
Pure AI and Data-Driven Weather Forecasts
Towards Operationalizing AI/ML Weather Forecast and Decision Support Products
Trustworthy, Responsible, and Ethical AI
Using Artificial Intelligence to Analyze Satellite Earth Observations
Visualization Tools and Products from Machine Learning and Artificial Intelligence Models
Student Award Opportunities
23AI will include a student competition for all oral presentations (open to undergraduate and graduate students), with award certificates and cash prizes. If you are a student and would like to enter the competition, please be sure to select this option when you submit your abstract on the AMS website.