edu.ucar.opensky::articles:25837 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:58:49.846275 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7988btm https://doi.org/10.5065/q6t7-ta06 https Related Dataset #1 Machine learning-based detection of weather fronts and associated extreme precipitation in CESM1.3 https://doi.org/10.5281/zenodo.2651361 https Related Dataset #2 National Weather Service Coded Surface Bulletins, 2003- (netCDF format) https://doi.org/10.48550/ARXIV.1605.01156 https Related Preprint #1 Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets https://doi.org/10.5281/zenodo.7126839 https Related Software #1 katiedagon/ML-extremes: Publication release Machine learning-based detection of weather fronts and associated extreme precipitation in historical and future climates 2022-11-16T00:00:00Z publication https://n2t.org/ark:/85065/d7988btm https://doi.org/10.1029/2022JD037038 Dagon, Katherine author Truesdale, John E. author Biard, J. C. author Kunkel, K. E. author Meehl, Gerald A. author Molina, Maria author UCAR/NCAR - Library publisher Extreme precipitation events, including those associated with weather fronts, have wide-ranging impacts across the world. Here we use a deep learning algorithm to identify weather fronts in high resolution Community Earth System Model (CESM) simulations over the contiguous United States (CONUS), and evaluate the results using observational and reanalysis products. We further compare results between CESM simulations using present-day and future climate forcing, to study how these features might change with climate change. We find that detected front frequencies in CESM have seasonally varying spatial patterns and responses to climate change and are found to be associated with modeled changes in large scale circulation such as the jet stream. We also associate the detected fronts with precipitation and find that total and extreme frontal precipitation mostly decreases with climate change, with some seasonal and regional differences. Decreases in Northern Hemisphere summer frontal precipitation are largely driven by changes in the frequency of different front types, especially cold and stationary fronts. On the other hand, Northern Hemisphere winter exhibits some regional increases in frontal precipitation that are largely driven by changes in frontal precipitation intensity. While CONUS mean and extreme precipitation generally increase during all seasons in these climate change simulations, the likelihood of frontal extreme precipitation decreases, demonstrating that extreme precipitation has seasonally varying sources and mechanisms that will continue to evolve with climate change. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2022 American Geophysical Union. None eng geoscientificInformation