A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
About this item
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Publisher
Katlenburg-Lindau: Copernicus GmbH
Journal title
Language
English
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Publisher
Katlenburg-Lindau: Copernicus GmbH
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Scope and Contents
Contents
High-resolution modelling of surface ozone is an essential step in the quantification of the impacts on health and ecosystems from historic and future concentrations. It also provides a principled way in which to extend analysis beyond measurement locations. Often, such modelling uses relatively coarse-resolution chemistry transport models (CTMs),...
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Full title
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Authors, Artists and Contributors
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_5ac1c626a99c46a4b4fba935a4bb9280
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_5ac1c626a99c46a4b4fba935a4bb9280
Other Identifiers
ISSN
1680-7324,1680-7316
E-ISSN
1680-7324
DOI
10.5194/acp-24-3163-2024
