Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ...

Author: Jacob PerezContact: scjp@leeds.ac.uk Paper title: A new characterization of the North Atlantic eddy-driven jet using2-dimensional moment analysis Code used to generate this data can be found at https://github.com/scjpleeds/EDJO-identification. Information about data: Each .npy file contains...

Full description

Bibliographic Details
Main Author: Perez, Jacob
Format: Dataset
Language:unknown
Published: Zenodo 2024
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.10053894
https://zenodo.org/doi/10.5281/zenodo.10053894
id ftdatacite:10.5281/zenodo.10053894
record_format openpolar
spelling ftdatacite:10.5281/zenodo.10053894 2024-03-31T07:54:16+00:00 Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ... Perez, Jacob 2024 https://dx.doi.org/10.5281/zenodo.10053894 https://zenodo.org/doi/10.5281/zenodo.10053894 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10053895 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 dataset Dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1005389410.5281/zenodo.10053895 2024-03-04T11:25:53Z Author: Jacob PerezContact: scjp@leeds.ac.uk Paper title: A new characterization of the North Atlantic eddy-driven jet using2-dimensional moment analysis Code used to generate this data can be found at https://github.com/scjpleeds/EDJO-identification. Information about data: Each .npy file contains the variable data for the winters (December, January and February) between 1979/80-2019/2020, in a daily format. File names containing *_lm.npz contain data defined by the largest mass EDJO on each day. File names containing *_full.npz contain EDJO data for days with two objects or more. Array lengths are 3701. Loading and Accessing the data: Accessing data from the largest mass can be done by, data = np.load(filename). To list all variables in a file, print(data.files). Once the data is loaded you can access the relevant variable by, phibar = np.squeeze(np.vstack(data['phibar'])) ... Dataset North Atlantic DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
description Author: Jacob PerezContact: scjp@leeds.ac.uk Paper title: A new characterization of the North Atlantic eddy-driven jet using2-dimensional moment analysis Code used to generate this data can be found at https://github.com/scjpleeds/EDJO-identification. Information about data: Each .npy file contains the variable data for the winters (December, January and February) between 1979/80-2019/2020, in a daily format. File names containing *_lm.npz contain data defined by the largest mass EDJO on each day. File names containing *_full.npz contain EDJO data for days with two objects or more. Array lengths are 3701. Loading and Accessing the data: Accessing data from the largest mass can be done by, data = np.load(filename). To list all variables in a file, print(data.files). Once the data is loaded you can access the relevant variable by, phibar = np.squeeze(np.vstack(data['phibar'])) ...
format Dataset
author Perez, Jacob
spellingShingle Perez, Jacob
Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ...
author_facet Perez, Jacob
author_sort Perez, Jacob
title Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ...
title_short Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ...
title_full Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ...
title_fullStr Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ...
title_full_unstemmed Eddy-Driven Jet Object data from ERA5 winters 1979-2020 ...
title_sort eddy-driven jet object data from era5 winters 1979-2020 ...
publisher Zenodo
publishDate 2024
url https://dx.doi.org/10.5281/zenodo.10053894
https://zenodo.org/doi/10.5281/zenodo.10053894
genre North Atlantic
genre_facet North Atlantic
op_relation https://dx.doi.org/10.5281/zenodo.10053895
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_doi https://doi.org/10.5281/zenodo.1005389410.5281/zenodo.10053895
_version_ 1795035022843969536