Evaluating Regional-Scale Air Quality Models

Abstract. We present in this technical note the research protocol for Phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided in two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reducing input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (Part 2). The scope of these papers is to present the scientific protocols for AQMEII4, as well to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving land use/land cover (LULC)-specific diagnostics and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air-quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

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On the Limit to the Accuracy of Regional-Scale Air Quality Models

10.5194/acp-2019-642 ◽

2019 ◽

Cited By ~ 1

Author(s):

S. Trivikrama Rao ◽

Huiying Luo ◽

Marina Astitha ◽

Christian Hogrefe ◽

Valerie Garcia ◽

...

Keyword(s):

Air Pollution ◽

Time Series ◽

Air Quality ◽

Input Data ◽

Regional Scale ◽

Atmospheric Dynamics ◽

Series Data ◽

Quality Models ◽

Averaged Model ◽

Air Pollution Models

Abstract. Regional-scale air pollution models are routinely being used world-wide for research, forecasting air quality, and regulatory purposes. It is well known that there are both reducible and irreducible uncertainties in the meteorology-atmospheric chemistry modeling systems. Inherent or irreducible uncertainties stem from our inability to properly characterize stochastic variations in atmospheric dynamics and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations in atmospheric dynamics and emissions forcing influencing the air pollutant concentrations are difficult to explicitly simulate, one can expect to find a percentile value from the distribution of measured concentrations to have much greater variability than that of the model. This paper presents an observation-based methodology to determine the expected errors from regional air quality models even when the model design, physics, chemistry, and numerical analysis techniques as well as its input data were perfect. To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8-hr ozone time series are separated from the longer-term forcings using a simple recursive moving average filter. The inherent variability attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States. The results reveal that the expected root mean square error at the median and 95th percentile is about 2 ppb and 5 ppb, respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state-of-the-science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements.

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Bi-directional air-surface exchange of atmospheric ammonia: A review of measurements and a development of a big-leaf model for applications in regional-scale air-quality models

Journal of Geophysical Research Atmospheres ◽

10.1029/2009jd013589 ◽

2010 ◽

Vol 115 (D20) ◽

Cited By ~ 97

Author(s):

L. Zhang ◽

L. P. Wright ◽

W. A. H. Asman

Keyword(s):

Air Quality ◽

Regional Scale ◽

Surface Exchange ◽

Quality Models ◽

Atmospheric Ammonia ◽

Big Leaf Model ◽

Leaf Model

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Diagnostic analysis of ozone concentrations simulated by two regional-scale air quality models

Atmospheric Environment ◽

10.1016/j.atmosenv.2011.08.011 ◽

2011 ◽

Vol 45 (33) ◽

pp. 5957-5969 ◽

Cited By ~ 17

Author(s):

Jerold A. Herwehe ◽

Tanya L. Otte ◽

Rohit Mathur ◽

S. Trivikrama Rao

Keyword(s):

Air Quality ◽

Regional Scale ◽

Diagnostic Analysis ◽

Quality Models ◽

Ozone Concentrations

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Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1, CAMx v6.50, CHIMERE v2017r4, and WRF-Chem v3.9.1)

Geoscientific Model Development ◽

10.5194/gmd-12-4603-2019 ◽

2019 ◽

Vol 12 (11) ◽

pp. 4603-4625 ◽

Cited By ~ 5

Author(s):

Siqi Ma ◽

Xuelei Zhang ◽

Chao Gao ◽

Daniel Q. Tong ◽

Aijun Xiu ◽

...

Keyword(s):

Air Quality ◽

Regional Scale ◽

Parameter Tuning ◽

Northeastern China ◽

Dust Storms ◽

Dust Particles ◽

Dust Event ◽

Local Data ◽

Dust Source ◽

Quality Models

Abstract. Mineral dust particles play an important role in the Earth system, imposing a variety of effects on air quality, climate, human health, and economy. Accurate forecasts of dust events are highly desirable to provide an early warning and inform the decision-making process. East Asia is one of the largest dust sources in the world. This study applies and evaluates four widely used regional air quality models to simulate dust storms in northeastern China. Three dust schemes in the Weather Research and Forecasting model with Chemistry (WRF-Chem) (version 3.9.1), two schemes in both CHIMERE (version 2017r4) and CMAQ (version 5.2.1), and one scheme in CAMx (version 6.50) were applied to a dust event during 4–6 May 2015 in northeastern China. Most of these models were able to capture this dust event with the exception of CAMx, which has no dust source map covering the study area; hence, another dust source mask map was introduced to replace the default one for the subsequent simulation. Although these models reproduced the spatial pattern of the dust plume, there were large discrepancies between predicted and observed PM10 concentrations in each model. In general, CHIMERE had relatively better performance among all simulations with default configurations. After parameter tuning, WRF-Chem with the Air Force Weather Agency (AFWA) scheme using a seasonal dust source map from Ginoux et al. (2012) showed the best performance, followed by WRF-Chem with the UOC_Shao2004 scheme, CHIMERE, and CMAQ. The performance of CAMx had significantly improved by substituting the default dust map and removing the friction velocity limitation. This study suggested that the dust source maps should be carefully selected on a regional scale or replaced with a new one constructed with local data. Moreover, further study and measurement of sandblasting efficiency of different soil types and locations should be conducted to improve the accuracy of estimated vertical dust fluxes in air quality models.

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Review of the paper „On the Limit to the Accuracy of Regional-Scale Air Quality Models” by ST Rao and co-authors.

10.5194/acp-2019-642-rc2 ◽

2019 ◽

Author(s):

Anonymous

Keyword(s):

Air Quality ◽

Regional Scale ◽

Quality Models

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On the limit to the accuracy of regional-scale air quality models

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-1627-2020 ◽

2020 ◽

Vol 20 (3) ◽

pp. 1627-1639 ◽

Cited By ~ 1

Author(s):

S. Trivikrama Rao ◽

Huiying Luo ◽

Marina Astitha ◽

Christian Hogrefe ◽

Valerie Garcia ◽

...

Keyword(s):

Air Pollution ◽

Time Series ◽

Air Quality ◽

Input Data ◽

Regional Scale ◽

Series Data ◽

Current Generation ◽

Quality Models ◽

Averaged Model ◽

Air Pollution Models

Abstract. Regional-scale air pollution models are routinely being used worldwide for research, forecasting air quality, and regulatory purposes. It is well recognized that there are both reducible (systematic) and irreducible (unsystematic) errors in the meteorology–atmospheric-chemistry modeling systems. The inherent (random) uncertainty stems from our inability to properly characterize stochastic variations in atmospheric dynamics and chemistry and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations are not being explicitly simulated in the current generation of regional-scale meteorology–air quality models, one should expect to find differences between the model estimates and corresponding observations. This paper presents an observation-based methodology to determine the expected errors from current-generation regional air quality models even when the model design, physics, chemistry, and numerical analysis, as well as its input data, were “perfect”. To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8 h ozone time series are separated from the longer-term forcing using a simple recursive moving average filter. The inherent uncertainty attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States (CONUS). The results reveal that the expected root mean square error (RMSE) at the median and 95th percentile is about 2 and 5 ppb, respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state of the science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements.

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Four-dimensional evaluation of regional air quality models

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-521-2013 ◽

2013 ◽

Vol 6 (1) ◽

pp. 521-584

Author(s):

E. Solazzo ◽

R. Bianconi ◽

G. Pirovano ◽

M. D. Moran ◽

R. Vautard ◽

...

Keyword(s):

Air Quality ◽

Model Evaluation ◽

Regional Scale ◽

Measurement Data ◽

High Sensitivity ◽

Air Quality Model ◽

Quality Model ◽

Quality Models ◽

Time Space ◽

Regional Air Quality

Abstract. The evaluation of regional air quality models is a challenging task, not only for the intrinsic complexity of the topic but also in view of the difficulties in finding sufficiently abundant, harmonized and time/space-well-distributed measurement data. This study, conducted in the framework of AQMEII (Air Quality Model Evaluation International Initiative), evaluates 4-D model predictions obtained from 15 modelling groups and relating to the air quality of the full year of 2006 over the North American and European continents. The modelled variables are ozone, CO, wind speed and direction, temperature, and relative humidity. Model evaluation is supported by the high quality in-flight measurements collected by instrumented commercial aircrafts in the context of the MOZAIC programme. The models are evaluated at five selected domains positioned around major airports, four in North America (Portland, Philadelphia, Atlanta, Dallas) and one in Europe (Frankfurt). Due to the extraordinary scale of the exercise (number of models and variables, spatial and temporal extent), this study is primarily aimed at illustrating the potential for using MOZAIC data for regional-scale evaluation and the capabilities of models to simulate concentration and meteorological fields in the vertical rather than just at the ground. We apply various approaches, metrics, and methods to analyze this complex dataset. Results of the investigation indicate that, while the observed meteorological fields are modelled with some success, modelling CO in and above the boundary layer remains a challenge and modelling ozone also has room for significant improvement. We note, however, that the high sensitivity of models to height, season, location, and metric makes the results rather difficult to interpret and to generalize. With this work, though, we set the stage for future process-oriented and in-depth diagnostic analyses.

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