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Identifying spatio-temporal drivers of extreme events [data set]

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Important usage condition: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.
Persistent identifier
doi:10.60507/FK2/RD9E33
Published version
1.0
Publication date
2024-10-21
License
CC BY 4.0

Description

This data set allows to systematically evaluate approaches for the task of identifying extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.

Creators

Keywords

anomaly detection, extreme events, deep learning, remote sensing, climate science, Earth science

Files

Before downloading: Use and redistribution are governed by CC BY 4.0. Review and comply with the license before using the data.
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Synthetic.7zapplication/x-7z-compressed25564142129MD5 0aa2fecf67c17c143376147fc20e36e3
NOAA_CERRA.7zapplication/x-7z-compressed42766102506MD5 ad2c687c8e6d00a58b80b12703f46bd8
NOAA_CORDEX.7zapplication/x-7z-compressed34087626480MD5 e4a401cb3af6b5825fa85ba1648cca2d
CERRA.7z.001application/octet-stream107374182400MD5 38eaa7688d119a470089a197990a5d24
README.txttext/plain7725MD5 4e83ef47e450562a6edd2284d0083381
CERRA.7z.002application/octet-stream102916084266MD5 15720a1f0be84ab32c7e058b9c9ac7b9
AFR-11.7zapplication/x-7z-compressed33870066390MD5 23895c5e11a26feb1b6fbdda4a7c0928
CAS-11.7zapplication/x-7z-compressed32607648460MD5 22342067b8c7d1830ebeee710e069e92
EUR-11.7zapplication/x-7z-compressed12736076997MD5 644bbc9d564a934211caa7331f6cac29
NAM-11.7zapplication/x-7z-compressed22070773410MD5 516c8be75ebf70693b2f81b06e0ac0ab
SAM-11.7zapplication/x-7z-compressed20327349448MD5 9fdf81564121478bb8782e02fca0e280
EAS-11.7zapplication/x-7z-compressed31985660281MD5 4f385d00203e698de00d46750c3b2f56
licence-to-use-copernicus-products.pdfapplication/pdf166903MD5 e7bdad701817f832396868dfc6086d01

Citation

Shams Eddin, Mohamad Hakam; Gall, Juergen, 2024-10-21, Identifying spatio-temporal drivers of extreme events [data set], doi:10.60507/FK2/RD9E33, V1.0

Additional Dataverse fields

Id273
Dataset Typedataset
Internal Version Number469
Latest Version Publishing StateRELEASED
Deaccession LinkNot supplied
Production Date2023-08-01
Release Time2024-10-21T10:45:06Z
Create Time2024-10-09T12:56:37Z
Citation Date2024-10-21
File Access RequestTrue

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Static metadata exports available for this published dataset version:

Complete Dataverse metadata

Citation Metadata

Title title
Identifying spatio-temporal drivers of extreme events [data set]
Alternative URL alternativeURL
https://hakamshams.github.io/IDE/
Author author
  1. Author Name
    Shams Eddin, Mohamad Hakam
    Author Affiliation
    University of Bonn
    Author Identifier Scheme
    ORCID
    Author Identifier
    Type Name
    authorIdentifier
    Multiple
    False
    Type Class
    primitive
    Value
    https://orcid.org/0000-0003-3136-4619
    Expandedvalue
    Person Name
    Shams Eddin, Mohamad Hakam
    @id
    https://orcid.org/0000-0003-3136-4619
    Scheme
    ORCID
    @type
    https://schema.org/Person
  2. Author Name
    Gall, Juergen
    Author Affiliation
    University of Bonn
    Author Identifier Scheme
    ORCID
    Author Identifier
    Type Name
    authorIdentifier
    Multiple
    False
    Type Class
    primitive
    Value
    https://orcid.org/0000-0002-9447-3399
    Expandedvalue
    Person Name
    Gall, Juergen
    @id
    https://orcid.org/0000-0002-9447-3399
    Scheme
    ORCID
    @type
    https://schema.org/Person
Dataset Contact datasetContact
  1. Dataset Contact Name
    Shams Eddin, Mohamad Hakam
    Dataset Contact Affiliation
    University of Bonn
    Dataset Contact Email
    shams@iai.uni-bonn.de
  2. Dataset Contact Name
    Shams Eddin, Mohamad Hakam
    Dataset Contact Affiliation
    University of Bonn
    Dataset Contact Email
    mshamsed@uni-bonn.de
Ds Description dsDescription
  1. Ds Description Value
    This data set allows to systematically evaluate approaches for the task of identifying extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.
    Ds Description Date
    2024-10-09
Subject subject
  1. Agricultural Sciences
  2. Computer and Information Science
  3. Earth and Environmental Sciences
  4. Physics
Free Keyword freeKeyword
  1. Free Keyword Value
    anomaly detection
  2. Free Keyword Value
    extreme events
  3. Free Keyword Value
    deep learning
  4. Free Keyword Value
    remote sensing
  5. Free Keyword Value
    climate science
  6. Free Keyword Value
    Earth science
Publication publication
  1. Publication Relation Type
    Cites
    Publication IDType
    arXiv
    Publication IDNumber
    2410.24075
    Publication URL
    https://doi.org/10.48550/arXiv.2410.24075
Language language
  1. English
Production Date productionDate
2023-08-01
Grant Number grantNumber
  1. Grant Number Agency
    German Research Foundation (DFG) - Collaborative Research Center (CRC)
    Grant Number Value
    SFB 1502/1–2022 – project no. 450058266
Depositor depositor
Shams Eddin, Mohamad
Date Of Deposit dateOfDeposit
2024-10-09
Data Sources dataSources
  1. CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS): https://cds.climate.copernicus.eu/datasets
  2. NOAA/NESDIS Center for Satellite Applications and Research: https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/index.php
  3. ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS): https://cds.climate.copernicus.eu/datasets

Software Metadata (CodeMeta v2.0)

Code Repository codeRepository
  1. https://github.com/HakamShams/IDEE
  2. https://github.com/HakamShams/Synthetic_Multivariate_Anomalies

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