GC040
Downscaling and Postprocessing at Weather and Climate Scales: Development and Evaluation of Methods, Products, and Applications
Session ID#: 251737
Session Description:
Outputs from standard climate and weather models, with spatial resolutions of 10s of kilometers or more, are often ingested by applications that make predictions at much smaller scales. Downscaling methods can address this scale gap with dynamical, empirical-statistical, hybrid, and data-driven machine-learning methods. Notably, such methods may also introduce uncertainty and thus complicate dataset selection for end users. Given myriad user needs and downscaling or post-processing approaches, we invite presentations that evaluate our confidence in downscaling methods; enhance product utility with developer-user communication; develop new downscaling approaches such as with AI/ML methods; characterize techniques and standards for evaluating downscaling historical skill, statistical reliability, and representativeness; and advance the uptake of downscaled products in real-world decision-making. We especially welcome submissions that explore gaps in creating and using regional-to-local downscaling products and/or that explore the differences arising from the use of a range, or different ensembles of parent models.
Co-Sponsor(s):
Index Terms:
1630 Impacts of global change [GLOBAL CHANGE]
1637 Regional climate change [GLOBAL CHANGE]
1807 Climate impacts [HYDROLOGY]
3355 Regional modeling [ATMOSPHERIC PROCESSES]
Primary Convener: Daniel Feldman, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Conveners: Samantha Hartke, U.S. Army Corps of Engineers, Denver, United States, Jeffrey Arnold, ERT Corp, Greenbelt, United States and Ethan D Gutmann, NSF National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States
GAN-Powered Downscaling: Capturing Regional-Scale Hourly Precipitation Patterns in the Canadian Prairies (1855707)
Hebatallah Mohamed Abdelmoaty, University of Calgary, Department of Civil Engineering, Calgary, AB, Canada, Simon Michael Papalexiou, University of California Irvine, Civil and Environmental Engineering, Irvine, United States, Antonios Mamalakis, University of Virginia, Department of Environmental Sciences, Charlottesville, VA, United States, Shivam Singh, University of Virginia, Environmental Institute, Charlottesville, United States, Vincenzo Coia, BGC Engineering, Victoria, Canada, Melissa Hairabedian, BGC Engineering, Calgary, Canada, Pascal Szeftel, BGC Engineering, Vancouver, Canada and Patrick Grover, BGC Engineering, Toronto, Canada
Generative AI for Precipitation Downscaling: Enhancing Spatial Resolution and Physical Consistency (1865991)
Shivam Singh1, Antonios Mamalakis2, Simon Michael Papalexiou3, Hebatallah Mohamed Abdelmoaty3 and Tom Hartvigsen4, (1)University of Virginia, Environmental Institute, Charlottesville, United States, (2)University of Virginia, Department of Environmental Sciences; School of Data Science, Charlottesville, United States, (3)University of Calgary, Department of Civil Engineering, Calgary, AB, Canada, (4)University of Virginia, School of Data Science, Charlottesville, United States
Assessment of changes in Mid-Century Near-Surface Wind Speed for the Northeast U.S. (1869314)
Tasnim Zaman1, Marina Astitha1, Ethan D Gutmann2 and Guiling Wang1, (1)University of Connecticut, School of Civil and Environmental Engineering, Storrs, United States, (2)NSF National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States
Uncertainty Propagation from Observation‑Based Data to Statistically Downscaled Climate Projections (1879066)
Graham Taylor1, Keith W Dixon2, Nicole Zenes3, Liqiang Sun2, Samantha Hartke4, Andrew James Newman5, Flavio Lehner6, Ethan D Gutmann7 and Rachel Rose McCrary8, (1)University Corporation for Atmospheric Research, Boulder, United States, (2)NOAA, Princeton, United States, (3)Science Applications International Corporation, Reston, United States, (4)U.S. Army Corps of Engineers, Denver, United States, (5)NSF National Center for Atmospheric Research, Boulder, United States, (6)Cornell University, Department of Earth and Atmospheric Sciences, Ithaca, United States, (7)NSF National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States, (8)National Center for Atmospheric Research, Boulder, United States
Can Machine Learning Produce Climate Invariant Downscaling? (1880140)
Xingjian Yan1, Anamitra Saha1,2, Erin Dougherty3 and Sai Ravela4, (1)Massachusetts Institute of Technology, Department of Earth, Atmospheric and Planetary Sciences (EAPS), Cambridge, MA, United States, (2)Indian Institute of Technology Hyderabad, Department of Climate Change, Kandi, India, (3)NSF National Center for Atmospheric Research, Boulder, United States, (4)Massachusetts Institute of Technology, Department of Earth, Atmospheric and Planetary Sciences (EAPS), Cambridge, United States
Production of 1 km Resolution Climate Projection Data Based on IPCC AR6 GCMs Using the G-PRIDE Model (1907352)
Yeong Jun An, Kongju National University, Gongju, Korea, Republic of (South), Maeng-Ki Kim, Kongju National University, Atmospheric Science, Gongju, Korea, Republic of (South) and Jeong Sang, Kongju National University, Gongju, South Korea
The Impact of Bias Correction Workflows on Multivariate Climate Indices: A Case Study of the Heat Index (Invited) (1928334)
Keith W Dixon, NOAA, Princeton, United States, Weixuan Xu, Princeton University, Atmospheric and Oceanic Sciences, Princeton, NJ, United States, Nicole Zenes, Science Applications International Corporation, Princeton, United States and John R Lanzante, NOAA, Princeton, NJ, United States
Statistical Downscaling of Daily Precipitation over South Korea Using the MS-PRISM Method: Influence Radius Sensitivity Analysis (1943587)
Youngseok Lee, Kongju National University, Gongju, Korea, Republic of (South), Jeong Sang, Kongju National University, Gongju, South Korea and Maeng-Ki Kim, Kongju National University, Atmospheric Science, Gongju, Korea, Republic of (South)
Comparative Assessment of LOCA2 and STAR-ESDM Downscaled SurfaceTemperature Over the Contiguous United States (1950606)
Liqiang Sun1, Keith W Dixon1, Kenneth Kunkel2, Xia Sun2 and David Easterling3, (1)NOAA Geophysical Fluid Dynamics Laboratory, Princeton, United States, (2)North Carolina Institute for Climate Studies, North Carolina State University, Asheville, United States, (3)Future Climate, LLC, Hendersonville, United States
Statistically Downscaled CMIP6 Multi-Model Ensemble for the Great Salt Lake Basin (1955617)
Savanna Wolvin1, Courtenay Strong2, Maribeth Kniffin3, Melanie Holland3, Sydney Smith4 and Husile Bai5, (1)University of Utah, Department of Atmospheric Sciences, Salt Lake City, UT, United States, (2)University of Utah, Department of Atmospheric Sciences, Salt Lake City, United States, (3)US Bureau of Reclamation, Technical Service Center, Denver, United States, (4)University of Utah, David Eccles School of Business, Salt Lake City, United States, (5)Vanderbilt University, Department of Earth and Environmental Sciences, Nashville, TN, United States
EMPIRIC-DS: Towards Fast and Transferable Climate Downscaling for Health Resilience in Pacific Island Countries (1962981)
Hudson Fagan1, Christopher Horvat2, Bingjie Zhao3, Eli Winkelman3 and Abrielle Mannino3, (1)Tufts University, Medford, MA, United States, (2)Brown University, Providence, RI, United States, (3)Brown University, Providence, United States
Object-oriented evaluation of downscaled precipitation and temperature datasets: How well do they capture spatial features? (1964058)
Andrew James Newman1, Nicholas Lybarger2, Ethan D Gutmann3, Rachel Rose McCrary4, Andy Wood1, Samantha Hartke5 and Flavio Lehner6, (1)NSF National Center for Atmospheric Research, Boulder, United States, (2)National Center for Atmospheric Research, Research Applications Laboratory, Boulder, United States, (3)NSF National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States, (4)National Center for Atmospheric Research, Boulder, United States, (5)U.S. Army Corps of Engineers, Denver, United States, (6)Cornell University, Department of Earth and Atmospheric Sciences, Ithaca, United States
Hybrid Dynamical–Statistical Downscaling of Wind and SST Using CNNs for Future Projections of Marine Heatwaves in the Peruvian Upwelling System (1976089)
Adolfo Chamorro1, Jorge Tam2, Rodrigo Mogollon Aburto2, Francois Colas3, Vincent Echevin4, Sophie Giffard-Roisin5, Helmuth Villavicencio6, Nelson Jorge6 and Victor Ccori7, (1)Pontificia Universidad Católica del Perú, Lima, Peru, (2)Instituto del Mar del Perú (IMARPE), Callao, Peru, (3)LOPS, IRD/CNRS/Université Bretagne Occidentale/IFREMER, IUEM, Plouzane, France, (4)LOCEAN-IPSL, IRD/CNRS/Sorbonne Universités (UPMC)/MNHN, UMR 7159, Paris, France, (5)Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, Univ. Gustave Eiffel, ISTerre, Grenoble, France, (6)Universidad Nacional de Ingeniería, Perú, Lima, Peru, (7)Universidad Peruana Cayetano Heredia, Lima, Peru
HiRO: A Diffusion-Based Downscaling Tool for the Ai2 Climate Emulator (ACE) (1978825)
W. Andre Perkins1, Anna Kwa1, Spencer Clark1, Gideon Dresdner2, Jeremy McGibbon1, Oliver Watt-Meyer1, Brian Henn1, Elynn Wu1, James Duncan1, Troy Arcomano1 and Christopher Stephen Bretherton1, (1)Allen Institute for AI, Seattle, United States, (2)Brightband, San Francisco, United States
An AI/ML regional downscaling model based on CONUS404 dataset (1979335)
Lulin Xue1, Yongxin Liu2, Haonan Chen3, Zhe LI2, Shun Yao2 and Ethan D Gutmann4, (1)National Center for Atmospheric Research, Research Applications Laboratory, Boulder, CO, United States, (2)Colorado State University, Fort Collins, United States, (3)Colorado State University and NOAA Physical Sciences Laboratory, Boulder, United States, (4)NSF National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States
Differentiable Bias Adjustment Model for GCMs Precipitation (1986367)
Kamlesh Sawadekar, Pennsylvania State University Main Campus, Civil and Environmental Engineering, University Park, PA, United States, Peijun Li, Pennsylvania State University Main Campus, Civil and Environmental Engineering, University Park, United States, Seth A McGinnis, National Center for Atmospheric Research, Boulder, CO, United States and Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States
Downscaling Weather at Arbitrary Locations with Earth Observation Data (1994868)
Qidong Yang1, Jonathan Giezendanner2, Daniel Salles Civitarese3, Johannes Jakubik4, Eric Schmitt5, Anirban Chandra5, Jeremy Vila6, Detlef Hohl5, Christopher N Hill7, Campbell Watson8 and Sherrie Wang2, (1)Massachusetts Institute of Technology, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Cambridge, United States, (3)IBM Research, Rio de Janeiro, Brazil, (4)IBM Research, Zurich, Switzerland, (5)Shell, Boston, United States, (6)Shell Houston, Houston, United States, (7)MIT, Cambridge, United States, (8)IBM Research, Yorktown Heights, United States
