21 research outputs found
Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions
An integrated modelling system based on the regional online coupled
meteorology–atmospheric chemistry WRF-Chem model configured with two nested
domains with horizontal resolutions of 11.1 and 3.7 km has been applied for
numerical weather prediction and for air quality forecasts in Slovenia. In the
study, an evaluation of the air quality forecasting system has been performed
for summer 2013. In the case of ozone (O3) daily maxima, the first- and
second-day model predictions have been also compared to the operational
statistical O3 forecast and to the persistence. Results of discrete and
categorical evaluations show that the WRF-Chem-based forecasting system is
able to produce reliable forecasts which, depending on monitoring site and
the evaluation measure applied, can outperform the statistical model. For
example, the correlation coefficient shows the highest skill for WRF-Chem
model O3 predictions, confirming the significance of the non-linear
processes taken into account in an online coupled Eulerian model. For some
stations and areas biases were relatively high due to highly complex terrain
and unresolved local meteorological and emission dynamics, which contributed
to somewhat lower WRF-Chem skill obtained in categorical model evaluations.
Applying a bias correction could further improve WRF-Chem model forecasting
skill in these cases
Regional effects of atmospheric aerosols on temperature: An evaluation of an ensemble of online coupled models
The climate effect of atmospheric aerosols is associated with their
influence on the radiative budget of the Earth due to the direct
aerosol–radiation interactions (ARIs) and indirect effects, resulting from
aerosol–cloud–radiation interactions (ACIs). Online coupled
meteorology–chemistry models permit the description of these effects on the
basis of simulated atmospheric aerosol concentrations, although there is
still some uncertainty associated with the use of these models. Thus,
the objective of this work is to assess whether the inclusion of atmospheric
aerosol radiative feedbacks of an ensemble of online coupled models improves
the simulation results for maximum, mean and minimum temperature at 2 m over
Europe. The evaluated models outputs originate from EuMetChem COST Action
ES1004 simulations for Europe, differing in the inclusion (or omission) of
ARI and ACI in the various models. The cases studies cover two important
atmospheric aerosol episodes over Europe in the year 2010: (i) a heat wave event
and a forest fire episode (July–August 2010) and (ii) a more humid episode
including a Saharan desert dust outbreak in October 2010. The simulation
results are evaluated against observational data from the E-OBS gridded database.
The results indicate that, although there is only a slight improvement in the
bias of the simulation results when including the radiative feedbacks, the
spatiotemporal variability and correlation coefficients are improved for the
cases under study when atmospheric aerosol radiative effects are included
An assessment of aerosol optical properties from remote-sensing observations and regional chemistry–climate coupled models over Europe
Atmospheric aerosols modify the radiative budget
of the Earth due to their optical, microphysical and chemical properties, and
are considered one of the most uncertain climate forcing agents. In order to
characterise the uncertainties associated with satellite and modelling
approaches to represent aerosol optical properties, mainly aerosol optical
depth (AOD) and Ångström exponent (AE), their representation by
different remote-sensing sensors and regional online coupled
chemistry–climate models over Europe are evaluated. This work also
characterises whether the inclusion of aerosol–radiation (ARI) or/and
aerosol–cloud interactions (ACI) help improve the skills of modelling
outputs.Two case studies were selected within the EuMetChem COST Action ES1004
framework when important aerosol episodes in 2010 all over Europe took
place: a Russian wildfire episode and a Saharan desert dust outbreak that
covered most of the Mediterranean Sea. The model data came from different
regional air-quality–climate simulations performed by working group 2 of
EuMetChem, which differed according to whether ARI or ACI was included or
not. The remote-sensing data came from three different sensors: MODIS, OMI
and SeaWIFS. The evaluation used classical statistical metrics to first
compare satellite data versus the ground-based instrument network (AERONET)
and then to evaluate model versus the observational data (both satellite and
ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS
presented the best agreement with the AERONET observations compared to other
satellite AOD observations. The differences found between remote-sensing
sensors highlighted the uncertainty in the observations, which have to be
taken into account when evaluating models. When modelling results were
considered, a common trend for underestimating high AOD levels was observed.
For the AE, models tended to underestimate its variability, except when
considering a sectional approach in the aerosol representation. The modelling
results showed better skills when ARI+ACI interactions were included; hence
this improvement in the representation of AOD (above 30 % in the model error)
and AE (between 20 and 75 %) is important to provide a better description of
aerosol–radiation–cloud interactions in regional climate models
Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models
Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM
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Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2
The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality models to simulate the year 2010 over one European and one North American domain. The MACC re-analysis has been used as chemical initial (IC) and boundary conditions (BC) by all participating regional models in AQMEII-2. The aim of the present work is to evaluate the MACC re-analysis along with the participating regional models against a set of ground-based measurements (O3, CO, NO, NO2, SO2, SO42−) and vertical profiles (O3 and CO). Results indicate different degrees of agreement between the measurements and the MACC re-analysis, with an overall better performance over the North American domain. The influence of BC on regional air quality simulations is analyzed in a qualitative way by contrasting model performance for the MACC re-analysis with that for the regional models. This approach complements more quantitative approaches documented in the literature that often have involved sensitivity simulations but typically were limited to only one or only a few regional scale models. Results suggest an important influence of the BC on ozone for which the underestimation in winter in the MACC re-analysis is mimicked by the regional models. For CO, it is found that background concentrations near the domain boundaries are rather close to observations while those over the interior of the two continents are underpredicted by both MACC and the regional models over Europe but only by MACC over North America. This indicates that emission differences between the MACC re-analysis and the regional models can have a profound impact on model performance and points to the need for harmonization of inputs in future linked global/regional modeling studies
Participative Leadership and Organizational Identification in SMEs in the MENA Region: Testing the Roles of CSR Perceptions and Pride in Membership
The aim of this research is to explore the process linking participative leadership to organizational identification. The study examines the relationship between participative leadership and internal CSR perceptions of employees and also investigates the role that pride in membership plays in the affiliation of CSR perceptions with organizational identification. By studying these relationships, the paper aspires to contemplate new presumed mediators in the association of participative leadership with organizational identification as well as determine a possible novel antecedent of employee CSR perceptions. Empirical evidence is provided from data that was collected through a survey distributed to employees working for small- and medium-sized enterprises in three countries in the Middle East and North Africa regions, particularly the United Arab Emirates, Lebanon, and Tunisia. Findings show that participative leadership leads to positive internal CSR perceptions of employees and that these CSR perceptions lead to pride in membership which, in turn, results in organizational identification. Implications of these findings are also discussed
Uncertainties of simulated aerosol optical properties induced by assumptions on aerosol physical and chemical properties: an AQMEII-2 perspective
Regional effects of atmospheric aerosols on temperature: an evaluation of an ensemble of on-line coupled models
The climate effect of atmospheric aerosols is associated with their
influence on the radiative budget of the Earth due to the direct
aerosol–radiation interactions (ARIs) and indirect effects, resulting from
aerosol–cloud–radiation interactions (ACIs). Online coupled
meteorology–chemistry models permit the description of these effects on the
basis of simulated atmospheric aerosol concentrations, although there is
still some uncertainty associated with the use of these models. Thus,
the objective of this work is to assess whether the inclusion of atmospheric
aerosol radiative feedbacks of an ensemble of online coupled models improves
the simulation results for maximum, mean and minimum temperature at 2 m over
Europe. The evaluated models outputs originate from EuMetChem COST Action
ES1004 simulations for Europe, differing in the inclusion (or omission) of
ARI and ACI in the various models. The cases studies cover two important
atmospheric aerosol episodes over Europe in the year 2010: (i) a heat wave event
and a forest fire episode (July–August 2010) and (ii) a more humid episode
including a Saharan desert dust outbreak in October 2010. The simulation
results are evaluated against observational data from the E-OBS gridded database.
The results indicate that, although there is only a slight improvement in the
bias of the simulation results when including the radiative feedbacks, the
spatiotemporal variability and correlation coefficients are improved for the
cases under study when atmospheric aerosol radiative effects are included
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Uncertainties of simulated aerosol optical properties induced by assumptions on aerosol physical and chemical properties: An AQMEII-2 perspective
The calculation of aerosol optical properties from aerosol mass is a process subject to uncertainty related to necessary assumptions on the treatment of the chemical species mixing state, density, refractive index, and hygroscopic growth. In the framework of the AQMEII-2 model intercomparison, we used the bulk mass profiles of aerosol chemical species sampled over the locations of AERONET stations across Europe and North America to calculate the aerosol optical properties under a range of common assumptions for all models. Several simulations with parameters perturbed within a range of observed values are carried out for July 2010 and compared in order to infer the assumptions that have the largest impact on the calculated aerosol optical properties. We calculate that the most important factor of uncertainty is the assumption about the mixing state, for which we estimate an uncertainty of 30–35% on the simulated aerosol optical depth (AOD) and single scattering albedo (SSA). The choice of the core composition in the core–shell representation is of minor importance for calculation of AOD, while it is critical for the SSA. The uncertainty introduced by the choice of mixing state choice on the calculation of the asymmetry parameter is the order of 10%. Other factors of uncertainty tested here have a maximum average impact of 10% each on calculated AOD, and an impact of a few percent on SSA and g. It is thus recommended to focus further research on a more accurate representation of the aerosol mixing state in models, in order to have a less uncertain simulation of the related optical properties