Articles | Volume 7, issue 6
https://doi.org/10.5194/gmd-7-2817-2014
https://doi.org/10.5194/gmd-7-2817-2014
Methods for assessment of models
 | 
02 Dec 2014
Methods for assessment of models |  | 02 Dec 2014

Uncertainty in Lagrangian pollutant transport simulations due to meteorological uncertainty from a mesoscale WRF ensemble

W. M. Angevine, J. Brioude, S. McKeen, and J. S. Holloway

Related authors

Increased Dynamic Efficiency in Mesoscale Organized Trade Wind Cumulus Clouds
Isabel L. McCoy, Sunil Baidar, Paquita Zuidema, Jan Kazil, W. Alan Brewer, Wayne M. Angevine, and Graham Feingold
EGUsphere, https://doi.org/10.5194/egusphere-2025-520,https://doi.org/10.5194/egusphere-2025-520, 2025
Short summary
Demistify: a large-eddy simulation (LES) and single-column model (SCM) intercomparison of radiation fog
Ian Boutle, Wayne Angevine, Jian-Wen Bao, Thierry Bergot, Ritthik Bhattacharya, Andreas Bott, Leo Ducongé, Richard Forbes, Tobias Goecke, Evelyn Grell, Adrian Hill, Adele L. Igel, Innocent Kudzotsa, Christine Lac, Bjorn Maronga, Sami Romakkaniemi, Juerg Schmidli, Johannes Schwenkel, Gert-Jan Steeneveld, and Benoît Vié
Atmos. Chem. Phys., 22, 319–333, https://doi.org/10.5194/acp-22-319-2022,https://doi.org/10.5194/acp-22-319-2022, 2022
Short summary
Errors in top-down estimates of emissions using a known source
Wayne M. Angevine, Jeff Peischl, Alice Crawford, Christopher P. Loughner, Ilana B. Pollack, and Chelsea R. Thompson
Atmos. Chem. Phys., 20, 11855–11868, https://doi.org/10.5194/acp-20-11855-2020,https://doi.org/10.5194/acp-20-11855-2020, 2020
Short summary
Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study
Anna Karion, Thomas Lauvaux, Israel Lopez Coto, Colm Sweeney, Kimberly Mueller, Sharon Gourdji, Wayne Angevine, Zachary Barkley, Aijun Deng, Arlyn Andrews, Ariel Stein, and James Whetstone
Atmos. Chem. Phys., 19, 2561–2576, https://doi.org/10.5194/acp-19-2561-2019,https://doi.org/10.5194/acp-19-2561-2019, 2019
Short summary
In situ vertical profiles of aerosol extinction, mass, and composition over the southeast United States during SENEX and SEAC4RS: observations of a modest aerosol enhancement aloft
N. L. Wagner, C. A. Brock, W. M. Angevine, A. Beyersdorf, P. Campuzano-Jost, D. Day, J. A. de Gouw, G. S. Diskin, T. D. Gordon, M. G. Graus, J. S. Holloway, G. Huey, J. L. Jimenez, D. A. Lack, J. Liao, X. Liu, M. Z. Markovic, A. M. Middlebrook, T. Mikoviny, J. Peischl, A. E. Perring, M. S. Richardson, T. B. Ryerson, J. P. Schwarz, C. Warneke, A. Welti, A. Wisthaler, L. D. Ziemba, and D. M. Murphy
Atmos. Chem. Phys., 15, 7085–7102, https://doi.org/10.5194/acp-15-7085-2015,https://doi.org/10.5194/acp-15-7085-2015, 2015
Short summary

Related subject area

Atmospheric sciences
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025,https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025,https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025,https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025,https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025,https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary

Cited articles

Angevine, W. M., Jiang, H., and Mauritsen, T.: Performance of an eddy diffusivity – mass flux scheme for shallow cumulus boundary layers, Mon. Weather Rev., 138, 2895–2912, 2010.
Angevine, W. M., Eddington, L., Durkee, K., Fairall, C., Bianco, L., and Brioude, J.: Meteorological model evaulation for CalNex 2010, Mon. Weather Rev., 140, 3885–3906, 2012.
Angevine, W. M., Bazile, E., Legain, D., and Pino, D.: Land surface spinup for episodic modeling, Atmos. Chem. Phys., 14, 8165–8172, https://doi.org/10.5194/acp-14-8165-2014, 2014.
Brioude, J., Kim, S. W., Angevine, W. M., Frost, G. J., Lee, S. H., McKeen, S. A., Trainer, M., Fehsenfeld, F. C., Holloway, J. S., Ryerson, T. B., Williams, E. J., Petron, G., and Fast, J. D.: Top-down estimate of anthropogenic emission inventories and their interannual variability in Houston using a mesoscale inverse modeling technique, J. Geophys. Res.-Atmos., 116, D20305, https://doi.org/10.1029/2011jd016215, 2011.
Brioude, J., Angevine, W. M., McKeen, S. A., and Hsie, E.-Y.: Numerical uncertainty at mesoscale in a Lagrangian model in complex terrain, Geosci. Model Dev., 5, 1127–1136, https://doi.org/10.5194/gmd-5-1127-2012, 2012.
Download
Short summary
Uncertainty in Lagrangian particle dispersion model simulations was evaluated using an ensemble of WRF meteorological model runs. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30-40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15-20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.
Share