Investigating impacts of chemistry and transport model formulation on model performance at European scale

Pirovano, Guido; Balzarini, Alessandra; Bessagnet, Bertrand; Emery, Chris; Kallos, George; Meleux, F.; Mitsakou, Christina; Nopmongcol, Uarporn; Riva, G. M.; Yarwood, Greg

doi:10.1016/j.atmosenv.2011.12.052

Cited by 52 publications

(39 citation statements)

References 32 publications

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“…Following the methodology used in previous publications describing the AQMEII Phase 1 results, the evaluation was undertaken with respect to station type (Hogrefe et al, 2013;Nopmongcol et al, 2012;Pirovano et al, 2012) and with respect to climatic differences between geographical regions in Europe (Solazzo et al, 2012a, b;Pirovano et al, 2012;Putaud et al, 2010). However, in contrast to previous publications, four different climatic regions were chosen in our analysis.…”

Section: Modelling Resultsmentioning

confidence: 99%

“…CAMx performance was the worst at urban stations in January while in July the bias was lower. Also, Pirovano et al (2012) reported a strong underestimation of ozone concentrations during the first part of the year for CAMx and CHIMERE models. CMAQ results, described in Appel et al (2012), indicate that model performance for the daytime ozone varied seasonally.…”

Section: Ozonementioning

confidence: 94%

“…The evaluation results reported by Pirovano et al (2012) for CAMx and CHIMERE models showed that for both mod- els, ozone concentrations at rural stations are reproduced not as well as at suburban and urban stations. This has been confirmed by the GEM-AQ model results.…”

Section: Ozonementioning

confidence: 96%

“…The work by Pirovano et al (2012) shows that the best performance of CAMx and CHIMERE models in terms of PM 10 concentrations was in north-western Europe for ruralbackground stations. In southern and eastern Europe, model results were underestimated.…”

Section: Pm 10mentioning

confidence: 99%

“…Brandt et al (2012) reported modelling results with the Danish Eulerian Hemispheric Model (DEHM). Pirovano et al (2012) discussed differences in simulations with the Comprehensive Air Quality Model with Extensions (CAMx) and the CHIMERE model. CAMx predictions were discussed by Nopmongcol et al (2012), while CMAQ simulations were done and analysed by Tagaris et al (2013).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

et al. 2015

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Abstract. In the scope of the AQMEII Phase 1 project the GEM-AQ model was run over Europe for the year 2006. The modelling domain was defined using a global variable resolution grid with a rotated equator and uniform resolution of 0.2• over the European continent. Spatial distribution and temporal variability of the GEM-AQ model results were analysed for surface ozone and PM 10 concentrations. Model results were compared with measurements available in the ENSEMBLE database. Statistical measures were used to evaluate performance of the GEM-AQ model. The mean bias error, the mean absolute gross error and the Pearson correlation coefficient were calculated for the maximum 8 h running average ozone concentrations and daily mean PM 10 concentrations. The GEM-AQ model performance was characterized for station types, European climatic regions and seasons. The best performance for ozone was obtained at suburban stations, and the worst performance was obtained for rural stations where the model tends to underestimate. The best results for PM 10 were calculated for urban stations, while over most of Europe concentrations at rural sites were too high. Discrepancies between modelled and observed concentrations were discussed in the context of emission data uncertainty as well as the impact of large-scale dynamics and circulation of air masses. Presented analyses suggest that interpretation of modelling results is enhanced when regional climate characteristics are taken into consideration.

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Section: Modelling Resultsmentioning

confidence: 99%

Section: Ozonementioning

confidence: 94%

Section: Ozonementioning

confidence: 96%

Section: Pm 10mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

et al. 2015

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Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

et al. 2018

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Based on prevailing numerical forecasting models (Community Multiscale Air Quality [CMAQ] model , Comprehensive Air Quality Model with Extensions, and Nested Air Quality Prediction Modeling System) and observations from monitoring stations in Hong Kong, we employ a set of autoregressive integrated moving average (ARIMA) models with numerical forecasts (ARIMAX) to improve the forecast of air pollutants including PM2.5, NO2, and O3. The results show significant improvements in multiple evaluation metrics for daily (1–3 days) and hourly (1–72 hr) forecast. Forecasts on daily 1‐hr and 8‐hr maximum O3 are also improved. For instance, compared with CMAQ, applying CMAQ‐ARIMA reduces average root‐mean‐square errors (RMSEs) at all stations for daily average PM2.5, NO2, and O3 in the next 3 days by 14.3–21.0%, 41.2–46.3%, and 47.8–49.7%, respectively. For hourly forecasts in the next 72 hr, reductions in RMSEs brought by ARIMAX using CMAQ are 18.2% for PM2.5, 32.1% for NO2, and 36.7% for O3. Large improvements in RMSEs are achieved for nonrural PM2.5 and rural NO2 using ARIMAX with three numerical models. Dynamic hourly forecast shows that ARIMAX can be applied for forecast of 7‐ to 72‐hr PM2.5, 4‐ to 72‐hr NO2, and 4‐ to 6‐hr O3. Besides applying ARIMAX for NO2, we recommend a mixed forecast strategy to ARIMAX for normal values of PM2.5 and O3 and employ numerical models for outputs above 75th percentile of historical observations. Our hybrid ARIMAX method can combine the advantage of ARIMA and numerical modeling to assist real‐time air quality forecasting efficiently and consistently.

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The influence of synoptic weather regimes on UK air quality: analysis of satellite column NO₂

Pope

Savage

Chipperfield

et al. 2014

Atmospheric Science Letters

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Groupings of Lamb weather types (LWTs) are used to assess the influence of synoptic meteorology on UK tropospheric column NO 2 . Composite satellite maps using daily 2005-2011 data show significant NO 2 anomalies for each group. Hence, despite large day-to-day variability and significant uncertainty in individual retrievals, the satellite data can be used to determine national-scale column NO 2 variations under different meteorological conditions. Under cyclonic conditions, NO 2 is reduced, while anticyclonic conditions aid its accumulation, especially during winter, consistent with trapping of surface emissions and slower photochemical processing. This composite dataset can be used to test seasonal simulations of air quality models.

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Investigating impacts of chemistry and transport model formulation on model performance at European scale

Cited by 52 publications

References 32 publications

Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

The influence of synoptic weather regimes on UK air quality: analysis of satellite column NO₂

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Investigating impacts of chemistry and transport model formulation on model performance at European scale

Cited by 52 publications

References 32 publications

Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

The influence of synoptic weather regimes on UK air quality: analysis of satellite column NO2

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The influence of synoptic weather regimes on UK air quality: analysis of satellite column NO₂