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Data · dataset · 2026

Machine learning-based detection of weather fronts and associated extreme precipitation in CESM1.3

Listed in National Center for Atmospheric Research

These data are the results of high resolution simulations with the Community Earth System Model, version 1.3 (CESM1.3).

Description

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.

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Catalogue records · 1

Topics

Inferred from text
Atmospheric sciences 75%
Provenance · 1 source records, 8 field assertions
SourceKeyLast seenRaw
National Center for Atmospheric Research4e9e5b09-03c3-4719-a23c-f2b0fa7a3bd612 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].anzsrc:group:3701enrichment · data ucar edutaxonomy-embedding@1.1.0title+keywords+description (75%)
concepts[field].local:field:earth-environmentalmapping · data ucar educonnector:data_ucar_edu@1.0.0
concepts[field].local:field:ocean-atmosphericmapping · data ucar educonnector:data_ucar_edu@1.0.0
created_datesource · data ucar educonnector:data_ucar_edu@1.0.0
descriptionsource · data ucar educonnector:data_ucar_edu@1.0.0/notes
publication_datesource · data ucar educonnector:data_ucar_edu@1.0.0
titlesource · data ucar educonnector:data_ucar_edu@1.0.0/title
updated_datesource · data ucar educonnector:data_ucar_edu@1.0.0