Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Machine learning-based detection of weather fronts with DL-FRONT in CESM1.3

Authors: Dagon, Katie;

Machine learning-based detection of weather fronts with DL-FRONT in CESM1.3

Abstract

These data are the results of detecting weather fronts with the machine learning algorithm DL-FRONT (see Biard and Kunkel 2019) in simulations with the Community Earth System Model, version 1.3 (CESM1.3, see Meehl et al. 2019). The specific CESM1.3 simulations used here include: a historical climate simulation from 2000 to 2005, a simulation with Representative Concentration Pathway 2.6 (RCP2.6) forcing from 2006 to 2015, and a simulation with Representative Concentration Pathway 8.5 (RCP8.5) forcing from 2086–2100. See Dagon et al. 2022 for a publication analyzing these machine learning detected weather fronts and associated extreme precipitation in historical and future climates. At each 3-hourly time step of simulation output over a North American spatial domain (10-77ºN, 171-31°W), DL-FRONT produced a set of spatial grids at 1° spatial resolution, for each of the five categories: cold front, warm front, stationary front, occluded front, and no front. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region). The dataset contains three sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others. The third set contains front crossing rates (monthly, seasonal, annual) and climatologies/anomalies/standard deviations (monthly, seasonal) for each front type. The front probability files have names that follow the form cesm_fronts_<start_year>_<end_year>.nc for each simulation period. The one hot files have names that follow the form cesm_fronts__<start_year>_<end_year>_MaskedNetCDF_customgrid.nc. The rates files have names that follow the form cesm_fronts_<start_year>_<end_year>_frontRates_viaPolylines_customgrid.nc. For the one-hot and rates files, the historical and RCP2.6 simulation output have been combined into a single file.

{"references": ["https://doi.org/10.5194/ascmo-5-147-2019", "https://doi.org/10.1029/2019GL084057", "https://doi.org/10.1029/2022JD037038"]}

Keywords

machine learning, fronts, CESM, DL-FRONT

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 8
  • 8
    views
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
0
Average
Average
Average
8