Assimilating chemical observations in the Mexico City Metropolitan Area using WRFDA-Chem
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Abstract
Air pollution exceedances are important air quality events for its associated effects on public health. Chemical transport models are frequently used for estimating pollution concentrations, especially in areas with limited monitoring capacity. Even though their performance may be improved by combining observations from different platforms through data assimilation, in Mexico these applications are limited, especially those aiming to assimilate chemical composition observations. In this study, surface observations of O3, CO and NO2 from the local monitoring network (RAMA) together with conventional global meteorological observations, were assimilated over the Mexico City Metropolitan Area (MCMA) using the WRFDA-Chem v4.5.2 assimilation system and the 3DVAR algorithm. The global observations were obtained in PREPBUFR format and consisted of upper-air and surface weather reports collected worldwide. The observations included pressure, temperature, dew point, wind speed and direction, from several platforms including surface, radiosondes, and aircraft reports. Two sets of experiments focusing on the impact of the chemical initial and boundary conditions were conducted. One experiment used default chemical initial and boundary conditions provided. In the experiment denoted by “default”, the WRF-Chem model was initialized through predetermined NALROM (NOAA Aeronomy Lab Regional Oxidant Model) ideal profiles that are included in the model. These profiles represent a northern-hemispheric, mid-latitude, relatively clean atmospheric composition and provide values for species such as O3, CO, NOx. (WRF-Chem user guide). The other experiment, denoted by “global”, the NALROM ideal profile was replaced with external chemical fields obtained from the global chemical transport model WACCM (Whole Atmosphere Community Climate Model). These fields provide spatially and temporally varying initial and boundary conditions that reflect realistic large-scale chemical distributions rather than a static climatology. Results showed that chemical data assimilation corrected pollutants concentrations. Regarding CO and NO2, differences between model results and observations were rather small, suggesting the need of tuning the background error covariance matrix parameters for these pollutants. However, for ozone, using global model outputs to initialize the model, tended to better represent regional maximum concentration, whilst using default conditions tended to better represent regional average concentration. However, both setups estimated similar daily ozone peaks. This suggests that using default chemical conditions cycled over a long period may optimize storage and computing resources.
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References
Ahmadov, R., McKeen, S. A., Robinson, A. L., Bahreini, R., Middlebrook, A. M., de Gouw, J. A., Meagher, J., Hsie, E. ‐Y., Edgerton, E., Shaw, S., & Trainer, M. (2012). A volatility basis set model for summertime secondary organic aerosols over the eastern United States in 2006. Journal of Geophysical Research: Atmospheres, 117(D6). Doi: https://doi.org/10.1029/2011jd016831 DOI: https://doi.org/10.1029/2011JD016831
Aleksankina, K., Reis, S., Vieno, M., & Heal, M. R. (2019). Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model. Atmospheric Chemistry and Physics, 19(5), 2881–2898. https://doi.org/10.5194/acp-19-2881-2019 DOI: https://doi.org/10.5194/acp-19-2881-2019
Baklanov, A., & Zhang, Y. (2020). Advances in air quality modeling and forecasting. Global Transitions, 2, 261–270. https://doi.org/10.1016/j.glt.2020.11.001 DOI: https://doi.org/10.1016/j.glt.2020.11.001
Ban, J., Liu, Z., Zhang, X., Huang, X. Y., & Wang, H. (2017). Precipitation data assimilation in WRFDA 4D-Var: Implementation and application to convection-permitting forecasts over United States. Tellus, Series A: Dynamic Meteorology and Oceanography, 69(1). https://doi.org/10.1080/16000870.2017.1368310 DOI: https://doi.org/10.1080/16000870.2017.1368310
Barker, D., Huang, X. Y., Liu, Z., Auligné, T., Zhang, X., Rugg, S., Ajjaji, R., Bourgeois, A., Bray, J., Chen, Y. E., Demirtas, M., Guo, Y. R., Henderson, T., Huang, W., Lin, H. C., Michalakes, J., Rizvi, S., & Zhang, X. (2012). The weather research and forecasting model’s community variational/ensemble data assimilation system: WRFDA. Bulletin of the American Meteorological Society, 93(6), 831–843. https://doi.org/10.1175/BAMS-D-11-00167.1 DOI: https://doi.org/10.1175/BAMS-D-11-00167.1
Bei, N., de Foy, B., Lei, W., Zavala, M., & Molina, L. T. (2008). Using a 3D-variational data assimilation system to improve ozone simulations in the Mexico City basin. Atmospheric Chemistry and Physics, 8(24), 7353–7365. https://doi.org/10.5194/acp-8-7353-2008 DOI: https://doi.org/10.5194/acp-8-7353-2008
Bei, N., Lei, W., Zavala, M., & Molina, L. T. (2010). Ozone predictabilities due to meteorological uncertainties in the Mexico City basin using ensemble forecasts. Atmospheric Chemistry and Physics, 10(13), 6295–6309. https://doi.org/10.5194/acp-10-6295-2010 DOI: https://doi.org/10.5194/acp-10-6295-2010
Bei, N., Li, G., & Molina, L. T. (2012). Uncertainties in SOA simulations due to meteorological uncertainties in Mexico City during MILAGRO-2006 field campaign. Atmospheric Chemistry and Physics, 12(23), 11295–11308. https://doi.org/10.5194/acp-12-11295-2012 DOI: https://doi.org/10.5194/acp-12-11295-2012
Bei, N., Li, G., Meng, Z., Weng, Y., Zavala, M., & Molina, L. T. (2014). Impacts of using an ensemble Kalman filter on air quality simulations along the California-Mexico border region during Cal-Mex 2010 field campaign. The Science of the Total Environment, 499, 141–153. https://doi.org/10.1016/j.scitotenv.2014.07.121 DOI: https://doi.org/10.1016/j.scitotenv.2014.07.121
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Aabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., & Seigneur, C. (2015). Data assimilation in atmospheric chemistry models: Current status and future prospects for coupled chemistry meteorology models. Atmospheric Chemistry and Physics, 15(10), 5325–5358. https://doi.org/10.5194/acp-15-5325-2015 DOI: https://doi.org/10.5194/acp-15-5325-2015
Chin, M., Rood, R. B., Lin, S. J., Müller, J. F., & Thompson, A. M. (2000). Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties. Journal of Geophysical Research: Atmospheres, 105(D20), 24671–24687. https://doi.org/10.1029/2000JD900384 DOI: https://doi.org/10.1029/2000JD900384
Cho, Y., Kim, H. M., Seo, M. G., & Kim, D. H. (2025). Effects of chemical and meteorological data assimilation on air-quality and meteorological forecasts in the Korean Peninsula. The Science of the Total Environment, 990, 179842. https://doi.org/10.1016/j.scitotenv.2025.179842 DOI: https://doi.org/10.1016/j.scitotenv.2025.179842
Comisión Nacional del Agua. (2024). Reporte anual del clima en México 2023 (Año 13, Número 13).
Comisión Nacional Forestal. (2024). Cierre Estadístico 2023. Recuperado el 3 de enero de 2025, de https://www.gob.mx/cms/uploads/attachment/file/879026/
Davis, B., Wang, X., & Lu, X. (2021). A comparison of HWRF Six-Hourly 4DEnVar and Hourly 3DEnVar assimilation of inner core tail doppler radar observations for the prediction of hurricane Edouard (2014). Atmosphere, 12(8), 942. https://doi.org/10.3390/atmos12080942 DOI: https://doi.org/10.3390/atmos12080942
Descombes, G., Auligné, T., Vandenberghe, F., Barker, D. M., & Barré, J. (2015). Generalized background error covariance matrix model (GEN-BE v2.0). Geoscientific Model Development, 8(3), 669–696. https://doi.org/10.5194/gmd-8-669-2015 DOI: https://doi.org/10.5194/gmd-8-669-2015
García-Reynoso, J. A., Mar-Morales, B. E., & Ruiz-Suárez, L. G. (2018). Spatial, temporal and speciation model of the Mexico national emissions inventory (base year 2008) for its use in air quality modeling (DiETE). Revista Internacional de Contaminación Ambiental, 34(4), 635–649. https://doi.org/10.20937/RICA.2018.34.04.07 DOI: https://doi.org/10.20937/RICA.2018.34.04.07
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., … Zhao, B. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1 DOI: https://doi.org/10.1175/JCLI-D-16-0758.1
Grell, G. A. (1993). Prognostic evaluation of assumptions used by cumulus parameterizations. Monthly Weather Review, 121(3), 764-787. https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2 DOI: https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2
Grell, G. A., & Dévényi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters, 29(14), 1693. https://doi.org/10.1029/2002GL015311 DOI: https://doi.org/10.1029/2002GL015311
Grell, G., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., & Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmospheric Environment, 39(37), 6957–6975. https://doi.org/10.1016/j.atmosenv.2005.04.027 DOI: https://doi.org/10.1016/j.atmosenv.2005.04.027
Ha, S. (2022). Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme. Geoscientific Model Development, 15(4), 1769–1788. https://doi.org/10.5194/gmd-15-1769-2022 DOI: https://doi.org/10.5194/gmd-15-1769-2022
Ha, S., Kumar, R., Pfister, G., Lee, Y., Lee, D., Kim, H. M., & Ryu, Y. H. (2024). Chemical data assimilation with aqueous chemistry in WRF-Chem coupled with WRFDA (V4.4.1). Journal of Advances in Modeling Earth Systems, 16(2), e2023MS003928. https://doi.org/10.1029/2023MS003928 DOI: https://doi.org/10.1029/2023MS003928
Hong, S. Y., & Lim, J. O. J. (2006). The WRF single-moment 6-class microphysics scheme (WSM6). Journal of the Korean Meteorological Society, 42(2), 129-151.
Hong, S. Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134(9), 2318-2341. https://doi.org/10.1175/mwr3199.1 DOI: https://doi.org/10.1175/MWR3199.1
Huang, X. Y., Xiao, Q., Barker, D. M., Zhang, X., Michalakes, J., Huang, W., Henderson, T., Bray, J., Chen, Y., Ma, Z., Dudhia, J., Guo, Y., Zhang, X., Won, D. J., Lin, H. C., & Kuo, Y. H. (2009). Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Monthly Weather Review, 137(1), 299–314. https://doi.org/10.1175/2008MWR2577.1 DOI: https://doi.org/10.1175/2008MWR2577.1
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres, 113(13), D13103. https://doi.org/10.1029/2008JD009944 DOI: https://doi.org/10.1029/2008JD009944
Instituto Nacional de Ecología y Cambio Climático. (2020). Informe Nacional de la Calidad del Aire 2019, México.
Instituto Nacional de Ecología y Cambio Climático. (2024). Informe Nacional de la Calidad del Aire 2021, México.
Lee, M., & Barker, D. (2005). Preliminary tests of First Guess at Appropriate Time (FGAT) with WRF 3DVAR and WRF model. Journal of the Korean Meteorological Society, 41(4), 495-505.
Lei, W., de Foy, B., Zavala, M., Volkamer, R., & Molina, L. T. (2007). Characterizing ozone production in the Mexico City Metropolitan Area: A case study using a chemical transport model. Atmospheric Chemistry and Physics, 7(5), 1347–1366. https://doi.org/10.5194/acp-7-1347-2007 DOI: https://doi.org/10.5194/acp-7-1347-2007
López-Espinoza, E. D., Zavala-Hidalgo, J., & Gómez-Ramos, O. (2012). Weather forecast sensitivity to changes in urban land covers using the WRF model for central México. Atmósfera, 25(2), 127-154.
Mallet, V., & Sportisse, B. (2006). Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling. Journal of Geophysical Research: Atmospheres, 111(1), D01302. https://doi.org/10.1029/2005JD006149 DOI: https://doi.org/10.1029/2005JD006149
Martínez-Sabari, E. E., & García-Reynoso, J. A. (2021). Meteorological data assimilation for air quality modeling with WRF-Chem: Central Mexico case study. Atmósfera, 34(3), 311–336. https://doi.org/10.20937/ATM.52804 DOI: https://doi.org/10.20937/ATM.52804
McKeen, S. A., Wotawa, G., Parrish, D. D., Holloway, J. S., Buhr, M. P., Hübler, G., Fehsenfeld, F. C., & Meagher, J. F. (2002). Ozone production from Canadian wildfires during June and July of 1995. Journal of Geophysical Research: Atmospheres, 107(D14), 4193. https://doi.org/10.1029/2001JD000697 DOI: https://doi.org/10.1029/2001JD000697
Milan, M., Macpherson, B., Tubbs, R., Dow, G., Inverarity, G., Mittermaier, M., Halloran, G., Kelly, G., Li, D., Maycock, A., Payne, T., Piccolo, C., Stewart, L., & Wlasak, M. (2020). Hourly 4D-Var in the Met Office UKV operational forecast model. Quarterly Journal of the Royal Meteorological Society, 146(728), 1281–1301. https://doi.org/10.1002/QJ.3737 DOI: https://doi.org/10.1002/qj.3737
Mizzi, A. P., Arellano, A. F., Edwards, D. P., Anderson, J. L., & Pfister, G. G. (2016). Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: A regional chemical transport/ensemble Kalman filter data assimilation system. Geoscientific Model Development, 9(3), 965–978. https://doi.org/10.5194/gmd-9-965-2016 DOI: https://doi.org/10.5194/gmd-9-965-2016
Molina, L. T., Retama, A., & Zavala, M. (2023). Diagnosis of current knowledge of the scientific bases for air quality management in the Megalopolis. Molina Center for Strategic Studies in Energy and the Environment. https://www.gob.mx/cms/uploads/attachment/file/869816/Diagnostic_Report__full_version_.pdf
National Centers for Environmental Prediction. (2008). NCEP ADP global upper air and surface weather observations (PREPBUFR format) [Dataset]. NSF National Center for Atmospheric Research. Recuperado el 12 de febrero de 2025, de https://gdex.ucar.edu/datasets/d337000/citation/
https://doi.org/10.5065/Z83F-N512
National Centers for Environmental Prediction. (2015). NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive [Dataset]. NSF National Center for Atmospheric Research. Recuperado el 10 de febrero de 2025, de https://gdex.ucar.edu/datasets/d084001/citation/
https://doi.org/10.5065/D65D8PWK
Parrish, D. F., & Derber, J. C. (1992). The National Meteorological Center's spectral statistical-interpolation analysis system. Monthly Weather Review, 120(8), 1747-1763. https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2 DOI: https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2
Peuch, V. H., Engelen, R., Rixen, M., Dee, D., Flemming, J., Suttie, M., Ades, M., Agustí-Panared, A., Ananasso, C., Andersson, E., Armstrong, D., Barré, J., Bousserez, N., Dominguez, J. J., Garrigues, S., Inness, A., Jones, L., Kipling, Z., Letertre-Dancza, J., … Thépaut, J. N. (2022). The Copernicus Atmosphere Monitoring Service from research to operations. Bulletin of the American Meteorological Society, 103(12), E2650–E2668. https://doi.org/10.1175/BAMS-D-21-0314.1 DOI: https://doi.org/10.1175/BAMS-D-21-0314.1
Risanto, C. B., Castro, C. L., Arellano, A. F., Moker, J. M., & Adams, D. K. (2021). The impact of assimilating gps precipitable water vapor in convective-permitting wrf-arw on north american monsoon precipitation forecasts over northwest mexico. Monthly Weather Review, 149(9), 3013–3035. https://doi.org/10.1175/MWR-D-20-0394.1 DOI: https://doi.org/10.1175/MWR-D-20-0394.1
Saide, P. E., Kim, J., Song, C. H., Choi, M., Cheng, Y., & Carmichael, G. R. (2014). Assimilation of next generation geostationary aerosol optical depth retrievals to improve air quality simulations. Geophysical Research Letters, 41(24), 9188–9196. https://doi.org/10.1002/2014GL062089 DOI: https://doi.org/10.1002/2014GL062089
Secretaría de Medio Ambiente y Recursos Naturales. (2019). Inventario Nacional de Emisiones 2016.
Secretaría del Medio Ambiente de la Ciudad de México. (2025). Monitoreo de Contaminantes Criterio. Recuperado el 1 de febrero de 2025, de http://www.aire.cdmx.gob.mx/
Stockwell, W. R., Kirchner, F., Kuhn, M., & Seefeld, S. (1997). A new mechanism for regional atmospheric chemistry modeling. Journal of Geophysical Research: Atmospheres, 102(D22), 25847-25879. https://doi.org/10.1029/97jd00849 DOI: https://doi.org/10.1029/97JD00849
Sun, W., Liu, Z., Chen, D., Zhao, P., & Chen, M. (2020). Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: Aiming to improve air quality forecasting and diagnose model deficiencies. Atmospheric Chemistry and Physics, 20(15), 9311–9329. https://doi.org/10.5194/acp-20-9311-2020 DOI: https://doi.org/10.5194/acp-20-9311-2020
Vendrasco, E. P., Sun, J., Herdies, D. L., & de Angelis, C. F. (2016). Constraining a 3DVAR radar data assimilation system with large-scale analysis to improve short-range precipitation forecasts. Journal of Applied Meteorology and Climatology, 55(3), 673–690. https://doi.org/10.1175/JAMC-D-15-0010.1 DOI: https://doi.org/10.1175/JAMC-D-15-0010.1
West, J. J., Zavala, M. A., Molina, L. T., Molina, M. J., San Martini, F., McRae, G. J., Sosa-Iglesias, G., & Arriaga-Colina, J. L. (2004). Modeling ozone photochemistry and evaluation of hydrocarbon emissions in the Mexico City metropolitan area. Journal of Geophysical Research: Atmospheres, 109(D19), D19312. https://doi.org/10.1029/2004JD004614 DOI: https://doi.org/10.1029/2004JD004614
Willmott, C. J., Ackleson, S. G., Davis, R. E., Feddema, J. J., Klink, K. M., Legates, D. R., O'Donnell, J., & Rowe, C. M. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research: Oceans, 90(C5), 8995–9005. https://doi.org/10.1029/JC090iC05p08995 DOI: https://doi.org/10.1029/JC090iC05p08995
World Health Organization, United Nations Children's Fund, United Nations Development Programme & United Nations Environment Programme (2024). Air pollution (Chap. 2). In Compendium of WHO and other UN guidance in health and environment, 2024 update. World Health Organization. https://iris.who.int/handle/10665/378095
Yang, C., Liu, Z., Gao, F., Childs, P. P., & Min, J. (2017). Impact of assimilating GOES imager clear-sky radiance with a rapid refresh assimilation system for convection-permitting forecast over Mexico. Journal of Geophysical Research: Atmospheres, 122(10), 5472–5490. https://doi.org/10.1002/2016JD026436 DOI: https://doi.org/10.1002/2016JD026436
Yang, Y., & Wang, X. (2024). A comparison of 3DEnVar and 4DEnVar for convective-scale direct radar reflectivity data assimilation in the context of a filter and a smoother. Monthly Weather Review, 152(1), 59–78. https://doi.org/10.1175/MWR-D-23-0082.1 DOI: https://doi.org/10.1175/MWR-D-23-0082.1
Zängl, G., Reinert, D., Rípodas, P., & Baldauf, M. (2015). The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core. Quarterly Journal of the Royal Meteorological Society, 141(687), 563–579. https://doi.org/10.1002/QJ.2378 DOI: https://doi.org/10.1002/qj.2378
Zaveri, R. A., Easter, R. C., Fast, J. D., & Peters, L. K. (2008). Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Journal of Geophysical Research: Atmospheres, 113(D13), D13204. https://doi.org/10.1029/2007JD008782 DOI: https://doi.org/10.1029/2007JD008782
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., & Baklanov, A. (2012). Real-time air quality forecasting, part I: History, techniques, and current status. Atmospheric Environment, 60, 632–655. https://doi.org/10.1016/j.atmosenv.2012.06.031 DOI: https://doi.org/10.1016/j.atmosenv.2012.06.031
Zou, X. (2025). Traditional data assimilation and its effective integration with meteorological deep learning. Engineering, 146(730), 1999–2049. https://doi.org/10.1016/j.eng.2025.11.023 DOI: https://doi.org/10.1016/j.eng.2025.11.023