Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII

Abstract

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting

Online coupled regional meteorology chemistry models in Europe: current status and prospects

“…The main focus of the phase 1 of AQMEII was the operational evaluation in the sense of Dennis et al (2010) as presented in a first overall model assessment against groundlevel observational measurements (Solazzo et al, 2012a, b). Capability to capture the vertical distribution of pollutants was further evaluated by Solazzo et al (2013a) for an ensemble of twelve air quality models.…”

“…6.2 can be classified as diagnostic evaluation as it investigated the sensitivity of the results to different processes. Solazzo et al (2012a) conducted a multi-model ensemble analysis which is listed by Dennis et al (2010) as one kind of probabilistic evaluation. The activity clearly demonstrated the usefulness of such multi-model activities, the necessity of collecting harmonised monitoring information for both meteorology and chemistry and the necessity of evaluating models in a global sense in three dimensional space and time as well as in the meteorological and chemical variable space.…”

“…Nevertheless, one can argue that such gradual migration towards ever stronger online coupling of CTMs with MetMs poses a challenging but attractive perspective from the scientific point of view for the sake of both high-quality meteorological and chemical weather forecasting (CWF). While NWP centres, as well as entities responsible for AQ forecasting, are only beginning to discuss whether an online approach is important enough to justify the extra cost (Baklanov, 2010;Grell and Baklanov, 2011;Kukkonen et al, 2012;Zhang et al, 2012a, b), the online integrated approach is already used in many research atmospheric models.…”

Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model

“…These uncertainties can be estimated by the comparison of simulations obtained by different models and the same sets of observations. For instance, model simulations have been compared against ozone surface observations in Europe (Vautard et al, 2007 and in the United States (Solazzo et al, 2012b) in the context of the AQMEII (Air Quality Model Evaluation International Initiative). Ensembles of model simulations and their statistical combinations have also been evaluated against satellite observations for tropospheric NO 2 (Huijnen et al, 2010) and O 3 (Zyryanov et al, 2012).…”

A Comparison Study of Ensemble Approach Using WRF/CMAQ Model - The High PM10Episode in Busan

“…Jang et al (2014)에서는 기상 자료의 불확실성 (Solazzo et al, 2012;Huijnen et al, 2010;Monache et al, 2006;Pagowski et al, 2006;McKeen et al, 2005;Monache and Stull, 2003 (28 Feb~06 Mar, 2013;blue). The azimuthal position gives the correlation, while the radial distance from the origin is proportional to the normalized standard deviation.…”