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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Cited articles

Avnery, S., Mauzerall, D. L., Liu, J., and Horowitz, L. W.: Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage, Atmos. Environ., 45, 2284–2296, https://doi.org/10.1016/j.atmosenv.2010.11.045, 2011. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G. Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. a, b
Battaglino, C., Ballard, G., and Kolda, T. G.: A practical randomized CP tensor decomposition, SIAM J. Matrix Anal. A., 39, 876–901, 2018. a
Benner, P., Gugercin, S., and Willcox, K.: A survey of projection-based model reduction methods for parametric dynamical systems, SIAM Rev., 57, 483–531, 2015. a
Bian, H. and Prather, M. J.: Fast-J2: Accurate Simulation of Stratospheric Photolysis in Global Chemical Models, J. Atmos. Chem., 41, 281–296, https://doi.org/10.1023/A:1014980619462, 2002. a
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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.