Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1525-2019
https://doi.org/10.5194/gmd-12-1525-2019
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

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AR by Nathan Kutz on behalf of the Authors (27 Mar 2019)  Author's response    Manuscript
ED: Publish as is (29 Mar 2019) by Patrick Jöckel
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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.