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Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning

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Bibliographic Details
Published in:The Cryosphere (TC)
Format: Online Article RSS Article
Published: 2026
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FRELIP Feed Integration
container_title The Cryosphere (TC)
description
discipline_display Environmental Sciences
discipline_facet Environmental Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:24910
institution FRELIP
journal_source_facet The Cryosphere (TC)
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
— — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
sub_discipline_display Climate & Atmospheric Sciences
sub_discipline_facet Climate & Atmospheric Sciences
subject_display — — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
— — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
subject_facet — — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
title Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
title_auth Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
title_full Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
title_fullStr Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
title_full_unstemmed Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
title_short Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning
title_sort identification and correction of snow depth bias in era5 datasets over central europe using machine learning
topic — — — — — — Meteorology
Climate & Atmospheric Sciences
Environmental Sciences
url https://doi.org/10.5194/tc-20-2295-2026