PNNL Generative Downscaling of ERA5 to 12 km over CONUS

This dataset provides hourly, 12 km meteorological fields over the contiguous United States (CONUS), produced by generatively downscaling ERA5 reanalysis (~25 km) with a consistency model — a one-step generative diffusion model — trained on 12 km WRF-TGW regional climate simulations. Conventional interpolation of a coarse reanalysis cannot create the fine-scale structure that a convection-permitting or mesoscale simulation resolves: orographic precipitation bands, sharp frontal gradients, terrain-driven wind features. The approach used here instead learns that fine-scale structure from a high-resolution regional model and then transfers it onto the large-scale state supplied by ERA5. The result retains ERA5's synoptic evolution while adding physically plausible sub-reanalysis-scale detail, at a computational cost several orders of magnitude below running the regional model directly. Eight surface variables are provided, generated jointly at each timestep so that cross-variable relationships (for example between temperature, humidity and radiation) are internally consistent rather than downscaled independently. The key characteristic users must understand is that the fine scales in this product are generated, not observed. See Assumptions and Limitations. Data The dataset is organized into the following two-year child records: Chunk IDYearsZenodo DOI1980_19811980-198110.5281/zenodo.227117841982_19831982-198310.5281/zenodo.227117861984_19851984-198510.5281/zenodo.227117881986_19871986-198710.5281/zenodo.227117901988_19891988-198910.5281/zenodo.227117921990_19911990-199110.5281/zenodo.227117941992_19931992-199310.5281/zenodo.227117961994_19951994-199510.5281/zenodo.227117981996_19971996-199710.5281/zenodo.227118021998_19991998-199910.5281/zenodo.227118042000_20012000-200110.5281/zenodo.227118062002_20032002-200310.5281/zenodo.227118082004_20052004-200510.5281/zenodo.227118102006_20072006-200710.5281/zenodo.227118122008_20092008-200910.5281/zenodo.227118142010_20112010-201110.5281/zenodo.227118162012_20132012-201310.5281/zenodo.227118182014_20152014-201510.5281/zenodo.227118202016_20172016-201710.5281/zenodo.227118222018_20192018-201910.5281/zenodo.227118252020_20212020-202110.5281/zenodo.227118272022_20232022-202310.5281/zenodo.227118292024_20252024-202510.5281/zenodo.22711831 Attribution This research was supported by the Framework for Optimizing Reliable Energy Systems and Infrastructure Given High-Uncertainty Trajectories (FORESIGHT) Initiative, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). License This data is made available under a CCBY4.0 License https://creativecommons.org/licenses/by/4.0/ Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORY operated by BATTELLE for the UNITED STATES DEPARTMENT OF ENERGY under Contract DE-AC05-76RL01830