Machine learning-based detection of atmospheric rivers in CESM1.3

These data are the results of high resolution simulations with the Community Earth System Model, version 1.3 (CESM1.3). These data form the basis of a publication analyzing machine learning based-detection of atmospheric rivers and associated precipitation. The CESM1.3 data include simulations with historical (years 2000-2005), RCP2.6 (years 2006-2015), and RCP8.5 (years 2086-2100) climate forcing. The temporal resolution is 3-hourly, the horizontal resolution is 0.25 degree, and the spatial domain is global.

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Temporal Range

  • Begin:  2000
    End:  2100

Keywords

Resource Type dataset
Temporal Range Begin 2000
Temporal Range End 2100
Temporal Resolution N/A
Bounding Box North Lat N/A
Bounding Box South Lat N/A
Bounding Box West Long N/A
Bounding Box East Long N/A
Spatial Representation N/A
Spatial Resolution N/A
Related Links N/A
Additional Information N/A
Resource Format NetCDF
Standardized Resource Format NetCDF
Asset Size 1240094.069 MB
Legal Constraints

Creative Commons Attribution 4.0 International License
Access Constraints None
Software Implementation Language N/A

Resource Support Name N/A
Resource Support Email datahelp@ucar.edu
Resource Support Organization
Distributor NSF NCAR Geoscience Data Exchange

Metadata Contact Name N/A
Metadata Contact Email datahelp@ucar.edu
Metadata Contact Organization NSF NCAR Geoscience Data Exchange

Author Dagon, Katherine ORCID icon
King, Teagan ORCID icon
Truesdale, John ORCID icon
Rosenbloom, Nan A. ORCID icon
Bates, Susan ORCID icon
Publisher NSF National Center for Atmospheric Research

Publication Date 2026-07-27
Digital Object Identifier (DOI) Not Assigned
Alternate Identifier d651086
Resource Version N/A
Topic Category climatologyMeteorologyAtmosphere
Progress completed
Metadata Date 2026-08-10T14:32:24Z
Metadata Record Identifier edu.ucar.gdex::d651086
Metadata Language eng; USA
Suggested Citation Dagon, Katherine, King, Teagan, Truesdale, John, Rosenbloom, Nan A., Bates, Susan. (2026). Machine learning-based detection of atmospheric rivers in CESM1.3. NSF National Center for Atmospheric Research. https://gdex.ucar.edu/datasets/d651086. Accessed 10 August 2026.

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