@misc{e55aee6616df446daf3e24ecc6149317,
title = "Downscaled Earth System Model Data for Resilient Energy System Planning",
abstract = "The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. In this presentation, we explore the output characteristics of the dataset and various validation analyses. We also present and discuss plans for the integration of this data into power system planning models using a decision-making under deep uncertainty (DMDU) methodology.",
keywords = "data downscaling, decision making under deep uncertainty, earth system model, generative machine learning, meteorological data, severe weather",
author = "Grant Buster",
year = "2025",
language = "American English",
series = "Presented at the 2025 ESIG Forecasting \& Markets Workshop, 24-27 June 2025, Nashville, Tennessee",
type = "Other",
}