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Artificial Intelligence for Localized Climate Policy

Using Advanced AI for Simultaneous Bias-Correction and Downscaling of Climate Model Projections

Artificial Intelligence for Local Climate Policy Decision Making

The AI & Localized Policy project is aimed at using advanced AI for simultaneous bias-correction and downscaling of climate model projections for localized policy relevance.

This project explores the capacity of cutting-edge Artificial Intelligence (AI) to simultaneously downscale and bias-correct general circulation climate models (GCMs) and climate projections. The general task at hand is to develop AI models that take low-resolution climate maps (produced by GCMs) as input and produce high-resolution counterparts that capture the observed climatic structure (e.g., marginal distribution properties, spatiotemporal dependence, etc) and honor mass conservation. When successful, this project will lead to obtaining downscaled and bias-corrected climate projections that are more relevant and actionable at scales that matter for societal decision-making, especially for local governance and community preparedness.  

The team is currently recruiting a Postdoctoral Research Associate, who will work collaboratively with mentors from both the environmental sciences and data science fields across UVA, leveraging their combined expertise to craft new methodologies. The Postdoctoral Research Associate will help pioneer the application of state-of-the-art AI techniques, including knowledge-guided Style Generative Adversarial Networks (StyleGAN) and diffusion models, to simultaneously correct biases and downscale climate projections. 

Review details of the job advertisement here and apply.

Project Team

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Antonios Mamalakis
Antonios
Mamalakis
Assistant Professor of Data Science, Assistant Professor of Environmental Sciences
University of Virginia
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Tom Hartvigsen
Tom
Hartvigsen
Assistant Professor of Data Science
University of Virginia

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