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: 2,303 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,509 728 66 2,303 316 98 91
  • HTML: 1,509
  • PDF: 728
  • XML: 66
  • Total: 2,303
  • Supplement: 316
  • BibTeX: 98
  • EndNote: 91
Views and downloads (calculated since 19 Dec 2018)
Cumulative views and downloads (calculated since 19 Dec 2018)

Viewed (geographical distribution)

Total article views: 2,303 (including HTML, PDF, and XML) Thereof 1,920 with geography defined and 383 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Feb 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