Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 23
Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-8913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 23
Model experiment description paper
 | 
13 Dec 2022
Model experiment description paper |  | 13 Dec 2022

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz

Viewed

Total article views: 2,382 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,886 433 63 2,382 53 48
  • HTML: 1,886
  • PDF: 433
  • XML: 63
  • Total: 2,382
  • BibTeX: 53
  • EndNote: 48
Views and downloads (calculated since 11 May 2022)
Cumulative views and downloads (calculated since 11 May 2022)

Viewed (geographical distribution)

Total article views: 2,382 (including HTML, PDF, and XML) Thereof 2,209 with geography defined and 173 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Jan 2025
Download
Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
Read more
Special issue
Benchmark datasets and machine learning algorithms for Earth...