EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 2: Model application to different datasets
Julian F. Quinting et al.
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1,390
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1,707
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10
HTML: 1,390
PDF: 272
XML: 45
Total: 1,707
BibTeX: 19
EndNote: 10
Views and downloads (calculated since 28 Sep 2021)
Cumulative views and downloads
(calculated since 28 Sep 2021)
Total article views: 587 (including HTML, PDF, and XML)
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433
127
27
587
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6
HTML: 433
PDF: 127
XML: 27
Total: 587
BibTeX: 12
EndNote: 6
Views and downloads (calculated since 27 Jan 2022)
Cumulative views and downloads
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Total article views: 1,120 (including HTML, PDF, and XML)
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957
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18
1,120
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HTML: 957
PDF: 145
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Total: 1,120
BibTeX: 7
EndNote: 4
Views and downloads (calculated since 28 Sep 2021)
Cumulative views and downloads
(calculated since 28 Sep 2021)
Viewed (geographical distribution)
Total article views: 1,707 (including HTML, PDF, and XML)
Thereof 1,586 with geography defined
and 121 with unknown origin.
Total article views: 587 (including HTML, PDF, and XML)
Thereof 551 with geography defined
and 36 with unknown origin.
Total article views: 1,120 (including HTML, PDF, and XML)
Thereof 1,035 with geography defined
and 85 with unknown origin.
This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.
This study applies novel artificial-intelligence-based models that allow the identification of...