Articles | Volume 10, issue 8
https://doi.org/10.5194/gmd-10-3085-2017
https://doi.org/10.5194/gmd-10-3085-2017
Model evaluation paper
 | 
21 Aug 2017
Model evaluation paper |  | 21 Aug 2017

Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC4RS and DISCOVER-AQ airborne campaigns

Min Huang, Gregory R. Carmichael, James H. Crawford, Armin Wisthaler, Xiwu Zhan, Christopher R. Hain, Pius Lee, and Alex B. Guenther

Related authors

HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025,https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Reactive nitrogen in and around the northeastern and mid-Atlantic US: sources, sinks, and connections with ozone
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025,https://doi.org/10.5194/acp-25-1449-2025, 2025
Short summary
Tropospheric ozone precursors: global and regional distributions, trends, and variability
Yasin Elshorbany, Jerald R. Ziemke, Sarah Strode, Hervé Petetin, Kazuyuki Miyazaki, Isabelle De Smedt, Kenneth Pickering, Rodrigo J. Seguel, Helen Worden, Tamara Emmerichs, Domenico Taraborrelli, Maria Cazorla, Suvarna Fadnavis, Rebecca R. Buchholz, Benjamin Gaubert, Néstor Y. Rojas, Thiago Nogueira, Thérèse Salameh, and Min Huang
Atmos. Chem. Phys., 24, 12225–12257, https://doi.org/10.5194/acp-24-12225-2024,https://doi.org/10.5194/acp-24-12225-2024, 2024
Short summary
Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 2: Sensitivity to dry-deposition parameterizations
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022,https://doi.org/10.5194/acp-22-7461-2022, 2022
Short summary
Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 1: An overview
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021,https://doi.org/10.5194/acp-21-11013-2021, 2021
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

Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. P., and Kustas, W. P.: A climatological study of surface fluxes and moisture stress across the continental United States based on thermal infrared remote sensing, Part I: model formulation, J. Geophys. Res., 112, D10117, https://doi.org/10.1029/2006JD007506, 2007.
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, https://doi.org/10.1175/MWR-D-12-00042.1, 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.
Bell, J. R., Case, J. L., LaFontaine, F. J., and Kumar, S. V.: Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts, the 16th Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, New Orleans, LA, 22–26 January 2012, available at: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120004024.pdf (last access: January 2017), 2012.
Byun, D. and Schere, K. L.: Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, Appl. Mech. Rev., 59, 51–77, https://doi.org/10.1115/1.2128636, 2006.
Download
Short summary
Various sensitivity simulations during two airborne campaigns were performed to assess the impact of different initialization methods and model resolutions on NUWRF-modeled weather states, heat fluxes, and the follow-on MEGAN isoprene emission calculations. Proper land initialization is shown to be important to the coupled weather modeling and the follow-on emission modeling, which is also critical to accurately representing other processes in air quality modeling and data assimilation.
Share