Articles | Volume 8, issue 6
https://doi.org/10.5194/gmd-8-1857-2015
https://doi.org/10.5194/gmd-8-1857-2015
Model description paper
 | 
23 Jun 2015
Model description paper |  | 23 Jun 2015

Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system

J. J. Guerrette and D. K. Henze

Related authors

Four-dimensional variational inversion of black carbon emissions during ARCTAS-CARB with WRFDA-Chem
Jonathan J. Guerrette and Daven K. Henze
Atmos. Chem. Phys., 17, 7605–7633, https://doi.org/10.5194/acp-17-7605-2017,https://doi.org/10.5194/acp-17-7605-2017, 2017
Short summary

Related subject area

Atmospheric sciences
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025,https://doi.org/10.5194/gmd-18-621-2025, 2025
Short summary
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025,https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025,https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025,https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025,https://doi.org/10.5194/gmd-18-405-2025, 2025
Short summary

Cited articles

Al-Saadi, J., Soja, A. J., Pierce, R. B., Szykman, J., Wiedinmyer, C., Emmons, L., Kondragunta, S., Zhang, X., Kittaka, C., Schaack, T., and Bowman, K.: Intercomparison of near-real-time biomass burning emissions estimates constrained by satellite fire data, J. Appl. Remote Sens., 2, 021504, https://doi.org/10.1117/1.2948785, 2008.
Anenberg, S. C., Talgo, K., Arunachalam, S., Dolwick, P., Jang, C., and West, J. J.: Impacts of global, regional, and sectoral black carbon emission reductions on surface air quality and human mortality, Atmos. Chem. Phys., 11, 7253–7267, https://doi.org/10.5194/acp-11-7253-2011, 2011.
Barker, D., Lee, M.-S., Guo, Y.-R., Huang, W., Huang, H., and Rizvi, Q.: WRF-Var – a unified 3/4D-Var variational data assimilation system for WRF, in: Sixth WRF/15th MM5 Users' Workshop, Boulder, CO, NCAR, 17 pp., available at: http://www2.mmm.ucar.edu/wrf/users/workshops/WS2005/presentations/session10/1-Barker.pdf (last access: 20 February 2015), 2005.
Barker, D. M., Huang, W., Guo, Y.-R., Bourgeois, A. J., and Xiao, Q. N.: A three-dimensional variational data assimilation system for MM5: implementation and initial results, Mon. Weather Rev., 132, 897–914, 2004.
Short summary
WRFPLUS-Chem is a coupled meteorology-chemistry adjoint and tangent linear model, with applications in sensitivity analysis and four-dimensional variational data assimilation. The linearized models are verified against finite difference approximations from the nonlinear forward model, WRF-Chem. A new checkpointing scheme enables data assimilation beyond 6h. New capabilities are demonstrated in an emission sensitivity study.
Share