Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 4
Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1525-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-1525-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 4
Methods for assessment of models
 | 
18 Apr 2019
Methods for assessment of models |  | 18 Apr 2019

Scalable diagnostics for global atmospheric chemistry using Ristretto library (version 1.0)

Meghana Velegar, N. Benjamin Erichson, Christoph A. Keller, and J. Nathan Kutz

Viewed

Total article views: 2,337 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,529 738 70 2,337 324 101 94
  • HTML: 1,529
  • PDF: 738
  • XML: 70
  • Total: 2,337
  • Supplement: 324
  • BibTeX: 101
  • EndNote: 94
Views and downloads (calculated since 19 Dec 2018)
Cumulative views and downloads (calculated since 19 Dec 2018)

Viewed (geographical distribution)

Total article views: 2,337 (including HTML, PDF, and XML) Thereof 1,957 with geography defined and 380 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Mar 2025
Download
Short summary
We introduce a new set of algorithmic tools capable of producing scalable, low-rank decompositions of global spatiotemporal atmospheric chemistry data. By exploiting emerging randomized linear algebra algorithms, a suite of decompositions are proposed that efficiently extract the dominant features from global atmospheric chemistry at longitude, latitude, and elevation with improved interpretability. The algorithms provide a strategy for the global monitoring of atmospheric chemistry.
Read more
Share