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

Related authors

Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-77,https://doi.org/10.5194/gmd-2024-77, 2024
Preprint under review for GMD
Short summary
Tropospheric aerosols over the western North Atlantic Ocean during the winter and summer campaigns of ACTIVATE 2020: Life cycle, transport, and distribution
Hongyu Liu, Bo Zhang, Richard H. Moore, Luke D. Ziemba, Richard A. Ferrare, Hyundeok Choi, Armin Sorooshian, David Painemal, Hailong Wang, Michael A. Shook, Amy Jo Scarino, Johnathan W. Hair, Ewan C. Crosbie, Marta A. Fenn, Taylor J. Shingler, Chris A. Hostetler, Gao Chen, Mary M. Kleb, Gan Luo, Fangqun Yu, Jason L. Tackett, Mark A. Vaughan, Yongxiang Hu, Glenn S. Diskin, John B. Nowak, Joshua P. DiGangi, Yonghoon Choi, Christoph A. Keller, and Matthew S. Johnson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1127,https://doi.org/10.5194/egusphere-2024-1127, 2024
Short summary
Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP)
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022,https://doi.org/10.5194/gmd-15-8731-2022, 2022
Short summary
Grid-stretching capability for the GEOS-Chem 13.0.0 atmospheric chemistry model
Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021,https://doi.org/10.5194/gmd-14-5977-2021, 2021
Short summary
Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models
Haipeng Lin, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Christoph A. Keller, Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Patrick C. Campbell, Barry Baker, Rick D. Saylor, and Raffaele Montuoro
Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021,https://doi.org/10.5194/gmd-14-5487-2021, 2021
Short summary

Related subject area

Atmospheric sciences
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024,https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024,https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024,https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024,https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024,https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary

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
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.