Machine learning-based detection of weather fronts and associated extreme precipitation in CESM1.3
d583105
| DOI: 10.5065/Q6T7-TA06
Abstract:
These data are the results of high resolution simulations with the Community Earth System Model, version 1.3 (CESM1.3). These simulations form the basis of a publication analyzing machine learning based-detection of weather fronts and associated extreme 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. Depending on the variables, the temporal resolution is 3-hourly, 6-hourly, or monthly, the horizontal resolution is 0.25 degree or 1 degree, and the spatial domain is global or centered over North America.
Temporal Range:
2000-01-01 to 2100-12-31
Variables:
| Geopotential Height | Precipitation Amount | Sea Level Pressure | Specific Humidity |
| Surface Temperature | Total Precipitable Water | U/V Wind Components |
Data Types:
Grid
Data Contributors:
UCAR/NCAR/CGD
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, University Corporation for Atmospheric Research
Total Volume:
305.69 GB
Data Formats:
HDF5/NetCDF4
Metadata Record:
Data License:
This work is licensed under a Creative Commons Attribution 4.0 International License.
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