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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2020-44
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-44
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 14 Feb 2020

Submitted as: model evaluation paper | 14 Feb 2020

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This preprint is currently under review for the journal GMD.

An exploratory performance assessment of the CHIMERE model (version 2017r4) for the northwestern Iberian Peninsula and the summer season

Swen Brands1,2, Guillermo Fernández-García1, Marta García-Vivanco3, Marcos Tesouro Montecelo1, Nuria Gallego Fernández4, Anthony David Saunders Estévez2,4, Pablo Enrique Carracedo García1, Anabela Neto Venancio1,2, Pedro Melo da Costa1,2, Paula Costa Tomé2,4, Cristina Otero2,4, María Luz Macho1, and Juan Taboada1,2 Swen Brands et al.
  • 1MeteoGalicia - Consellería de Medio Ambiente, Territorio e Vivenda, Xunta de Galicia, Santiago de Compostela, Spain
  • 2Tragsatec, Santiago de Compostela, Spain
  • 3Centro de Investigaciones Energéticas, Medioambientables y Tecnológicas (CIEMAT), Madrid, Spain
  • 4Servicio de Calidad del Aire - Consellería de Medio Ambiente, Territorio e Vivenda, Xunta de Galicia, Santiago de Compostela, Spain

Abstract. Here, the capability of the chemical weather forecasting model CHIMERE (version 2017r4) to reproduce surface ozone, particulate matter and nitrogen dioxide concentrations in complex terrain is investigated for the period from June 21 to August 21, 2018. The study area is the northwestern Iberian Peninsula, where both coastal and mountain climates can be found in direct vicinity and a large fraction of the land area is covered by forests. Driven by lateral boundary conditions from the ECMWF Composition Integrated Forecast System, anthropogenic emissions from two commonly used top-down inventories and meteorological data from the Weather Research and Forecasting Model, CHIMERE's performance with respect to observations is tested with a range of sensitivity experiments. We assess the effects of 1) an increase in horizontal resolution, 2) an increase in vertical resolution, 3) the use of distinct model chemistries and 4) the use of distinct anthropogenic emissions inventories, downscaling techniques and landuse databases. In comparsion with the older HTAP emission inventory downscaled with basic options, the updated and sophistically downscaled EMEP inventory only leads to partial model improvements and so does the computationally costly horizontal resolution increase. Model performance changes caused by the choice of distinct chemical mechanisms are not systematic either and rather depend on the considered anthropgenic emission configuration and pollutant. Albeit the results are thus heterogeneous in general terms, the model's response to a vertical resolution increase confined to the lower to middle troposphere is homogeneous in the sense of improving virtually all verification aspects. We conclude that, as long as the aforementioned top-down emission inventories are used, it is generally not necessary to use a horizontal model mesh much finer than the native grid of the inventories. A relatively coarse horizontal mesh combined with 20 model layers between 999 and 500 hPa is sufficient to yield balanced results. The chemical mechanism should be chosen as a function of the intended application.

Swen Brands et al.

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Swen Brands et al.

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Latest update: 28 Feb 2020
Publications Copernicus
Short summary
The capability of numerical models to predict air quality depends on many factors. Here, the role of the applied model resolution, emission configuration and model chemistry is assessed for the CHIMERE model over the northwestern Iberian Peninsula. Albeit results are heterogeneous in general, forecasts are systematically improved by increasing the vertical resolution in the lower and middle troposphere. This finding might help to as well achieve better forecasts for other regions.
The capability of numerical models to predict air quality depends on many factors. Here, the...
Citation