edu.ucar.opensky::articles:26255 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:35:57.441726 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7hq43wf https://doi.org/10.5065/D62R3QFS https Related Dataset #1 3-hourly MERRA2 IVT, uIVT, vIVT, IWV data computed for ARTMIP https://doi.org/10.5065/XVAQ-2X07 https Related Dataset #2 MERRA2 Global Atmosphere Forcing Data https://doi.org/10.5281/ZENODO.4311221 https Related Dataset #3 Multi-thousand-year simulations of December-February precipitation and zonal upper-level wind https://doi.org/10.5281/zenodo.4311221 https Related Dataset #4 Multi-thousand-year simulations of December-February precipitation and zonal upper-level wind Using deep learning for an analysis of atmospheric rivers in a high-resolution large ensemble climate data set 2023-04-01T00:00:00Z publication https://n2t.org/ark:/85065/d7hq43wf https://doi.org/10.1029/2022MS003495 Higgins, T. B. author Subramanian, A. C. author Graubner, A. author Kapp-Schwoerer, L. author Watson, P. A. G. author Sparrow, S. author Kashinath, K. author Kim, S. author Delle Monache, L. author Chapman, William author UCAR/NCAR - Library publisher There is currently large uncertainty over the impacts of climate change on precipitation trends over the US west coast. Atmospheric rivers (ARs) are a significant source of US west coast precipitation and trends in ARs can provide insight into future precipitation trends. There are already a variety of different methods used to identify ARs, but many are used in contexts that are often difficult to apply to large climate datasets due to their computational cost and requirement of integrated vapor transport as an input variable, which can be expensive to compute in climate models at high temporal frequencies. Using deep learning (DL) to track ARs is a unique approach that can alleviate some of the computational challenges that exist in more traditional methods. However, some questions still remain regarding its flexibility and robustness. This research investigates the consistency of a DL methodology of tracking ARs with more established algorithms to demonstrate its high-level performance for future studies.Plain Language Summary Atmospheric rivers (ARs) are long corridors of water vapor in the lower atmosphere that are associated with a large amount of precipitation on the US west coast. They are important to investigate in future climate change scenarios. To further understand them in climate change scenarios, they must be tracked in large datasets. We demonstrate the efficiency, effectiveness, and flexibility of a machine learning tracking method by comparing it to more established existing tracking methods. This method applies particularly well to large climate datasets and can be useful for future studies. 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 author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation