School of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, China
Dong Guo
Key Laboratory of Meteorological Disaster, Ministry of Education–Joint International Research Laboratory of Climate and Environment Change–Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China
Viewed
Total article views: 4,232 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,989
1,092
151
4,232
416
182
285
HTML: 2,989
PDF: 1,092
XML: 151
Total: 4,232
Supplement: 416
BibTeX: 182
EndNote: 285
Views and downloads (calculated since 14 Apr 2023)
Cumulative views and downloads
(calculated since 14 Apr 2023)
Total article views: 2,455 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,938
433
84
2,455
139
95
125
HTML: 1,938
PDF: 433
XML: 84
Total: 2,455
Supplement: 139
BibTeX: 95
EndNote: 125
Views and downloads (calculated since 16 Oct 2023)
Cumulative views and downloads
(calculated since 16 Oct 2023)
Total article views: 1,777 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,051
659
67
1,777
277
87
160
HTML: 1,051
PDF: 659
XML: 67
Total: 1,777
Supplement: 277
BibTeX: 87
EndNote: 160
Views and downloads (calculated since 14 Apr 2023)
Cumulative views and downloads
(calculated since 14 Apr 2023)
Viewed (geographical distribution)
Total article views: 4,232 (including HTML, PDF, and XML)
Thereof 4,232 with geography defined
and 0 with unknown origin.
Total article views: 2,455 (including HTML, PDF, and XML)
Thereof 2,455 with geography defined
and 0 with unknown origin.
Total article views: 1,777 (including HTML, PDF, and XML)
Thereof 1,777 with geography defined
and 0 with unknown origin.
For the first time a regularized multivariate regression model is used to estimate stratospheric ozone trends. Regularized regression avoids the over-fitting issue due to correlation among explanatory variables. We demonstrate that there are considerable differences in satellite-based and chemical-model-based ozone trends, highlighting large uncertainties in our understanding about ozone variability. We argue that caution is needed when interpreting results with different methods and datasets.
For the first time a regularized multivariate regression model is used to estimate stratospheric...