Comparing Extreme Rainfall Exposure to Climate-Focused Planning Efforts
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RGMA
MSD
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Kevin Reed
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Stony Brook University
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Climate Change and Its Influence on Water Systems Increases the Cost of Electricity System Decarbonization
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RGMA
MSD
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Julia Szinai
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Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory Berkeley California USA
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Tropical Cyclone Wind Shear-Relative Asymmetry in Reanalyses
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RGMA
MSD
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Colin Zarzycki
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Pennsylvania State University
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Increasing Extreme Hourly Precipitation Risk for New York City after Hurricane Ida
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RGMA
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Naresh Devineni
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The City University of New York (City College)
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Response of the Upper Ocean to Northeast Pacific Atmospheric Rivers under Climate Change
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RGMA
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Christine Shields
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National Center for Atmospheric Research (NCAR)
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Changing Windstorm Conditions Over the US Northeast (NE) from a Large Ensemble
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RGMA
MSD
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Sara C Pryor
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Cornell University
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Variability in Rain-on-Snow Event Detection: Discrepancies Across Climate Datasets
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RGMA
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Colin Zarzycki
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Penn State University
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Mapping the Impact of Freeze Events on Aspen Health with Satellite Data
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MSD
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Yoshimitsu Chikamoto (yoshi.chikamoto@usu.edu)
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Utah State University - Department of Plants, Soils and Climate
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How the last 40 years of US tropical cyclones may change under future climates
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RGMA
MSD
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Colin Zarzycki
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Penn State University
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How will the Flood Drivers of Rain-on-Snow Events Change Through the 21st Century?
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RGMA
MSD
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Alan Rhoades
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Lawrence Berkeley National Laboratory
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Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations
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RGMA
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Kathryn Lawson
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Pennsylvania State University
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When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
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RGMA
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Kathryn Lawson
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Pennsylvania State University
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Upstream Surface Roughness and Terrain are Strong Drivers of Contrast in Tornado Potential Between North and South America
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RGMA
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Brian Medeiros
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National Center for Atmospheric Research (NCAR)
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A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing
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RGMA
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Benjamin Bass
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University of California - Los Angeles
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Probing the limit of hydrologic predictability with the Transformer network
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RGMA
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Kathryn Lawson
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Pennsylvania State University
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Is Bias Correction in Dynamical Downscaling Defensible?
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RGMA
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Mark Risser
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Lawrence Berkeley National Lab
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Urban feedbacks to deep convection
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RGMA
MSD
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Professor Sara C Pryor
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Cornell University
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Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections
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RGMA
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Stefan Rahimi
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University of California - Los Angeles
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Object-Oriented Analysis Yields Compounding Short-Term Drought and Crop Heat-Stress Events
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RGMA
MSD
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William J. Gutowski, Jr.
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Iowa State University
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Improving Extreme Precipitation Predictions Through Convection-Permitting Downscaling
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RGMA
MSD
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Yoshimitsu Chikamoto (yoshi.chikamoto@usu.edu)
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Utah State University - Department of Plants, Soils and Climate
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Climate Change May Triple the Intensity of Extreme Precipitation from Mesoscale Storms
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RGMA
MSD
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Ruby Leung
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Pacific Northwest National Laboratory
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'Miracle' Drought-Busters Becoming Rarer in a Warming Colorado River Basin
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RGMA
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Simon Wang
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Utah State University
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From Fires out West to Hailstorms in the Central US: Unraveling the Link with Machine Learning
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RGMA
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Jiwen Fan
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Argonne National Laboratory
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Wind Shadows Impact Planning of Large Offshore Wind Farms
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RGMA
MSD
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Sara C Pryor
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Cornell University
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An Overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
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ESMD
RGMA
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Stefan Rahimi
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University of California Los Angeles
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Compound Hazard of Freezing Rain and Wind Gusts Across the Continental United States
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RGMA
MSD
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Professor Sara C Pryor
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Cornell University
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Offshore Wind – Powering the Future
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RGMA
MSD
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Sara C Pryor
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Cornell University
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Recurving Eastern Pacific hurricanes reduce Southwest U.S. Wildfires
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ESMD
RGMA
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Ruby Leung
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Pacific Northwest National Laboratory
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The Benefits of Moving Wind Turbines Offshore
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RGMA
MSD
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Sara C Pryor
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Cornell University
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Improving River Routing Using a Differentiable Muskingum-Cunge Model and Physics-Informed Machine Learning
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RGMA
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Kathryn Lawson
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Pennsylvania State University
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