GC049
Eye Candy or Eye Sore? The Technical and Philosophical Challenges of High-Resolution Climate Data Product Development
Session ID#: 252136
Session Description:
Be frank, where is your climate dataset good? Where is it bad? Whether produced dynamically, statistically, via machine learning, and/or through hybrid techniques, or whether it's observationally-constrained, we solicit presentations that “air the dirty laundry” of the growing number of high-resolution climate products that are being produced and used across key sectors to support decision-making. We encourage presenters to discuss philosophical dataset construction breakthroughs and challenges, such as when and where to apply bias correction, how to preserve trends in forcing datasets (or when not to if they are systematically biased), and/or how to go about ESM selection for downscaling. If you intend to present, please be ready to share the good, the bad, and the ugly about your data product and where and when it should or shouldn't be applied in decision-making contexts!
Index Terms:
1622 Earth system modeling [GLOBAL CHANGE]
1637 Regional climate change [GLOBAL CHANGE]
1807 Climate impacts [HYDROLOGY]
Primary Convener: Stefan Rahimi, University of Wyoming, Department of Atmospheric Science, Laramie, WY, United States
Conveners: Alan Rhoades, Lawrence Berkeley National Laboratory, Berkeley, CA, United States, Melissa S Bukovsky, Haub School of Environment and Natural Resources, Laramie, Wyoming, United States and Manuela Girotto, University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, United States
Student/Early Career Convener: David Conway Lafferty, University of Illinois Urbana-Champaign, Department of Climate, Meteorology & Atmospheric Sciences, Urbana, United States; Cornell University, Ithaca, United States
Advancing Downscaling with Artificial Intelligence for Next-Generation Earth System Projections (Invited) (1904378)
Deeksha Rastogi1, Haoran Niu2, Shih-Chieh Kao3, Linsey Passarella4, Salil Mahajan5, Moetasim Ashfaq2, Pouya Vahmani6, Andrew D Jones7 and Moetasim Ashfaq, (1)Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Computational Sciences and Engineering Divison, Oak Ridge, TN, United States, (3)Oak Ridge National Laboratory, Environmental Science Division, Oak Ridge, United States, (4)University of Tennessee, Knoxville, TN, United States, (5)Oak Ridge Nat'l Lab, Oak Ridge, TN, United States, (6)University of California Los Angeles, Los Angeles, United States, (7)Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA, United States
Deep Learning–Driven Downscaling of High-Resolution Precipitation and Temperature Projections for the Conterminous United States (1931093)
Haoran Niu1, Deeksha Rastogi2, Shih-Chieh Kao3 and Moetasim Ashfaq1, (1)Oak Ridge National Laboratory, Computational Sciences and Engineering Divison, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Computational Sciences and Engineering Divison, Oak Ridge, United States, (3)Oak Ridge National Laboratory, Environmental Science Division, Oak Ridge, United States
Integrating FourCastNet with RegCM for Extreme Weather Prediction: A Case Study of a Kona Low’s impact on Hawaiian Communities (1932366)
Rebecca Porter, Indiana University Bloomington, Bloomington, IN, United States, Meredith Leung, National Center for Atmospheric Research, Boulder, United States, Melissa Moulton, National Center for Atmospheric Research, Climate and Global Dynamics, Boulder, United States and Travis Allen O'Brien, Lawrence Berkeley National Lab, Climate & Ecosystem Sciences Division, Berkeley, United States; Indiana University Bloomington, Department of Earth and Atmospheric Sciences, Bloomington, IN, United States
Aligning Projections with Reality: A Framework for Adjusting GCM Projections to Historical Climate Data (1975115)
James H Thorne, University of California Davis, Davis, CA, United States, Ryan Boynton, University of California Davis, Davis, United States and Michelle A Stern, US Geological Survey, Sacramento, United States
Evaluating Vulnerabilities and Uncertainties of Hydroclimate Projections for the Great Salt Lake Basin Study (1980661)
Maribeth Kniffin1, Savanna Wolvin2, Husile Bai3, Melanie Holland1, Courtenay Strong4, Thamanna Vasan1, Jake Serago5 and Laura Vernon5, (1)US Bureau of Reclamation, Technical Service Center, Denver, United States, (2)University of Utah, Department of Atmospheric Sciences, Salt Lake City, UT, United States, (3)Vanderbilt University, Department of Earth and Environmental Sciences, Nashville, TN, United States, (4)University of Utah, Department of Atmospheric Sciences, Salt Lake City, United States, (5)Utah Division of Water Resources, Salt Lake City, United States
How Ready is AI Downscaled Meteorological Data for Hydrologic Applications? (1997505)
Xiaodong Chen, University of Oklahoma, School of Meteorology, Norman, United States, Songkun Yan, University of Oklahoma, School of Civil Engineering and Environmental Science, Norman, United States and Wenjun Cui, Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, United States
Assessing the Reliability of High-Resolution Climate Model Data in Southern Florida (2003202)
Rachel Gaal, University of Miami, Rosenstiel School of Marine, Atmospheric, and Earth Science, Department of Atmospheric Sciences, Miami, FL, United States, Benjamin P Kirtman, University of Miami - RSMAS, Atmospheric Sciences, Miami, FL, United States, Amy C Clement, Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Department of Atmospheric Sciences, Miami, United States and Brian Soden, University of Miami, Rosenstiel School of Marine, Atmospheric, and Earth Science, Miami, United States
