Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ...

This dataset is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 25km. It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding A...

Full description

Bibliographic Details
Main Authors: Jungho, Seo, Panahi, Mahdi, JiHyun, Kim, Bateni, Sayed, Kim, Yeonjoo
Format: Dataset
Language:English
Published: Zenodo 2024
Subjects:
antartic*
Online Access:https://dx.doi.org/10.5281/zenodo.10407061
https://zenodo.org/doi/10.5281/zenodo.10407061
id ftdatacite:10.5281/zenodo.10407061
record_format openpolar
spelling ftdatacite:10.5281/zenodo.10407061 2024-09-15T17:49:15+00:00 Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ... Jungho, Seo Panahi, Mahdi JiHyun, Kim Bateni, Sayed Kim, Yeonjoo 2024 https://dx.doi.org/10.5281/zenodo.10407061 https://zenodo.org/doi/10.5281/zenodo.10407061 en eng Zenodo https://dx.doi.org/10.5281/zenodo.10407062 MIT License Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 https://opensource.org/licenses/MIT mit Dataset dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1040706110.5281/zenodo.10407062 2024-09-02T09:19:40Z This dataset is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 25km. It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding Antartica. ... Dataset antartic* DataCite
institution Open Polar
collection DataCite
op_collection_id ftdatacite
language English
description This dataset is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 25km. It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding Antartica. ...
format Dataset
author Jungho, Seo
Panahi, Mahdi
JiHyun, Kim
Bateni, Sayed
Kim, Yeonjoo
spellingShingle Jungho, Seo
Panahi, Mahdi
JiHyun, Kim
Bateni, Sayed
Kim, Yeonjoo
Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ...
author_facet Jungho, Seo
Panahi, Mahdi
JiHyun, Kim
Bateni, Sayed
Kim, Yeonjoo
author_sort Jungho, Seo
title Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ...
title_short Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ...
title_full Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ...
title_fullStr Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ...
title_full_unstemmed Global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : Global Snow Water Equivalent Product (SWEML) ...
title_sort global snow water equivalent product derived from machine learning model trained with in situ measurement data ... : global snow water equivalent product (sweml) ...
publisher Zenodo
publishDate 2024
url https://dx.doi.org/10.5281/zenodo.10407061
https://zenodo.org/doi/10.5281/zenodo.10407061
genre antartic*
genre_facet antartic*
op_relation https://dx.doi.org/10.5281/zenodo.10407062
op_rights MIT License
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
https://opensource.org/licenses/MIT
mit
op_doi https://doi.org/10.5281/zenodo.1040706110.5281/zenodo.10407062
_version_ 1810291026597773312

Similar Items