Matthew Widlansky (University of Hawaii at Manoa): Building an AI Skills Library for Reproducible Environmental Data Analysis Jonathan Mote (NOAA/OAR/WPO): From Sticky Notes to Knowledge Graphs with AI Zachary Medley (ORNL ): Drafting Metadata Records for Atmospheric Science Datasets using an AI Agent with Model Context Protocol (MCP) Servers Chirag Shah (ORNL): ARM Data Advisor (ADA): A Revolution in Atmospheric Data Discovery and Access Kevin Lee (UCLA & NASA-JPL): An Intelligent Toolbox for Multi-Scale Oceanography Analysis
Nicholas Elmer (Arete): AI data fusion for high-resolution atmospheric characterization Somnath Luitel (Western Kentucky University): AirCast-SR: A Diffusion Based Foundation Model for Kilometer-Scale Atmospheric Super-Resolution Shuyan Liu (CISESS/STAR): Enhancing MiRS ATMS Precipitation Retrievals through Machine Learning: Development, Evaluation, and Operational Transition Tyler Gardner (CIRES / NOAA): Improving Space Weather Imagery: A Machine Learning Approach to Removing Earthshine Artifacts in CCOR-1 Data Ying-Chieh Chen (The University of Iowa): Retrieve Nighttime Cloud Microphysical Parameters from VIIRS Observations: A Physics-Informed Transformative Machine Learning Approach
Lin Qi (NOAA/STAR): Mapping global floating algae blooms with deep learning method Dustin Colson Leaning (EDF & CVision AI) & Brian Tate (CVision AI): Estimating Recreational Fishing Effort with AI and Shore-Based Camera Systems Masoud Rostami (UT Arlington): TRUST-Aqua: Trustworthy AI for Aquatic Environmental Monitoring, Forecasting, and Decision Support Caitlin Allen Akselrud (NOAA-NMFS): Hidden fragility in machine learning models: the importance of a data-driven approach to forecasting messy time-series data Yan Chen & Shayla Fitzsimmons (Canadian Integrated Ocean Observing System): From Project to Community: Building Bridges through a Community of Practice
