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Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2
Citation:
Brunner, D., N. Savage, O. Jorba, B. Eder, L. Giordano, A. Badia, A. Balzarin, R. Baró, R. Bianconi, C. Chemel, G. Curci, R. Forkel, P. Jiménez-Guerrero, M. Hirtl, A. Hodzic, L. Honzak, U. Im, C. Knote, P. Makar, A. Manders-Groot, E. van Meijgaard, L. Neal, J. Pérez, G. Pirovano, R. San Jose, W. Schroder, R. Sokhi, D. Syrakov, A. Torian, P. Tuccella, J. Werhahn, R. Wolke, K. Yahya, R. Zabkar, Y. Zhang, C. Hogrefe, AND S. Galmarini. Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 115:470-498, (2015).
Impact/Purpose:
The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation’s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Air pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups from Europe and four groups from North America operating eight different regional coupled chemistry and meteorology models participated in a coordinated model evaluation exercise. Each group simulated the year 2010 for a domain covering either Europe or North America or both. Here were present an operational analysis of model performance with respect to key meteorological variables relevant for atmospheric chemistry processes and air quality. These parameters include temperature and wind speed at the surface and in the vertical profile, incoming solar radiation at the ground, precipitation, and planetary boundary layer heights. A similar analysis was performed during AQMEII phase 1 (Vautard et al., 2012) for offline air quality models not directly coupled to the meteorological model core as the model systems investigated here. Similar to phase 1, we found significant overpredictions of 10-m wind speeds by most models, more pronounced during night than during daytime. The seasonal evolution of temperature was well captured with monthly mean biases below 2 K over all domains. Solar incoming radiation, precipitation and PBL heights, on the other hand, showed significant spread between models and observations suggesting that major challenges still remain in the simulation of meteorological parameters relevant for air quality and for chemistry–climate interactions at the regional scale.
