Primary Convener:
Daniel Feldman, Lawrence Berkeley National Laboratory

Convener:
Jeffrey Arnold, ERT Corp
Ethan Gutmann, NSF National Center for Atmospheric Research
Samantha Hartke, U.S. Army Corps of Engineers

Early Career Convener:
Daniel Feldman, Lawrence Berkeley National Laboratory

Chair:
Jeffrey Arnold, ERT Corp
Daniel Feldman, Lawrence Berkeley National Laboratory

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.

Index Terms
3355 Regional modeling
1630 Impacts of global change
1637 Regional climate change
1807 Climate impacts

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Machine Learning and AI
Community and People-Powered Science
Global Impacts‚ Solutions‚ & Policies

Cross-Listed:
A - Atmospheric Sciences
H - Hydrology

Neighborhoods:
3. Earth Covering