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Online coupled mesoscale meteorology atmospheric chemistry models have undergone a rapid evolution in recent years. Although mainly developed by the air quality modelling community, these models are also of interest for numerical weather prediction and regional climate modelling as they can consider not only the effects of meteorology on air quality, but also the potentially important effects of atmospheric composition on weather. Two ways of online coupling can be distinguished: online integrated and online access coupling. Online integrated models simulate meteorology and chemistry over the same grid in one model using one main time step for integration. Online access models use independent meteorology and chemistry modules that might even have different grids, but exchange meteorology and chemistry data on a regular and frequent basis. This article offers a comprehensive review of the current research status of online coupled meteorology and atmospheric chemistry modelling within Europe. Eighteen regional online coupled models developed or being used in Europe are described and compared. Topics discussed include a survey of processes relevant to the interactions between atmospheric physics, dynamics and composition; a brief overview of existing online mesoscale models and European model developments; an analysis on how feedback processes are treated in these models; numerical issues associated with coupled models; and several case studies and model performance evaluation methods. Finally, this article highlights selected scientific issues and emerging challenges that require proper consideration to improve the reliability and usability of these models for the three scientific communities: air quality, numerical meteorology modelling (including weather prediction) and climate modelling. This review will be of particular interest to model developers and users in all three fields as it presents a synthesis of scientific progress and provides recommendations for future research directions and priorities in the development, application and evaluation of online coupled models.

With the establishment of ceilometer networks by national weather services, a discussion commenced to which extent these simple backscatter lidars can be used for aerosol research. Though primarily designed for the detection of clouds it was shown that at least observations of the vertical structure of the boundary layer might be possible. However, an assessment of the potential of ceilometers for the quantitative retrieval of aerosol properties is still missing. In this paper we discuss different retrieval methods to derive the aerosol backscatter coefficient βp, with special focus on the calibration of the ceilometers. Different options based on forward and backward integration methods are compared with respect to their accuracy and applicability. It is shown that advanced lidar systems such as those being operated in the framework of the European Aerosol Research Lidar Network (EARLINET) are excellent tools for the calibration, and thus βp retrievals based on forward integration can readily be implemented and used for real-time applications. Furthermore, we discuss uncertainties introduced by incomplete overlap, the unknown lidar ratio, and water vapor absorption. The latter is relevant for the very large number of ceilometers operating in the spectral range around λ = 905–910 nm. The accuracy of the retrieved βp mainly depends on the accuracy of the calibration and the long-term stability of the ceilometer. Under favorable conditions, a relative error of βp on the order of 10% seems feasible. In the case of water vapor absorption, corrections assuming a realistic water vapor distribution and laser spectrum are indispensable; otherwise errors on the order of 20% could occur. From case studies it is shown that ceilometers can be used for the reliable detection of elevated aerosol layers below 5 km, and can contribute to the validation of chemistry transport models, e.g., the height of the boundary layer. However, the exploitation of ceilometer measurements is still in its infancy, so more studies are urgently needed to consolidate the present state of knowledge, which is based on a limited number of case studies.

Biosphere–atmosphere interactions play a critical role in governing atmospheric composition, mediating the concentrations of key species such as ozone and aerosol, thereby influencing air quality and climate. The exchange of reactive trace gases and their oxidation products (both gas and particle phase) is of particular importance in this process. The FORCAsT (FORest Canopy Atmosphere Transfer) 1-D model is developed to study the emission, deposition, chemistry and transport of volatile organic compounds (VOCs) and their oxidation products in the atmosphere within and above the forest canopy. We include an equilibrium partitioning scheme, making FORCAsT one of the few canopy models currently capable of simulating the formation of secondary organic aerosols (SOAs) from VOC oxidation in a forest environment. We evaluate the capability of FORCAsT to reproduce observed concentrations of key gas-phase species and report modeled SOA concentrations within and above a mixed forest at the University of Michigan Biological Station (UMBS) during the Community Atmosphere-Biosphere Interactions Experiment (CABINEX) field campaign in the summer of 2009. We examine the impact of two different gas-phase chemical mechanisms on modelled concentrations of short-lived primary emissions, such as isoprene and monoterpenes, and their oxidation products. While the two chemistry schemes perform similarly under high-NOx conditions, they diverge at the low levels of NOx at UMBS. We identify peroxy radical and alkyl nitrate chemistry as the key causes of the differences, highlighting the importance of this chemistry in understanding the fate of biogenic VOCs (bVOCs) for both the modelling and measurement communities.

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.

Vegetation emits large quantities of biogenic volatile organic compounds (BVOC). At remote sites, these compounds are the dominant precursors to ozone and secondary organic aerosol (SOA) production, yet current field studies show that atmospheric models have difficulty in capturing the observed HOx cycle and concentrations of BVOC oxidation products. In this manuscript, we simulate BVOC chemistry within a forest canopy using a one-dimensional canopy-chemistry model (Canopy Atmospheric CHemistry Emission model; CACHE) for a mixed deciduous forest in northern Michigan during the CABINEX 2009 campaign. We find that the base-case model, using fully-parameterized mixing and the simplified biogenic chemistry of the Regional Atmospheric Chemistry Model (RACM), underestimates daytime in-canopy vertical mixing by 50–70% and by an order of magnitude at night, leading to discrepancies in the diurnal evolution of HOx, BVOC, and BVOC oxidation products. Implementing observed micrometeorological data from above and within the canopy substantially improves the diurnal cycle of modeled BVOC, particularly at the end of the day, and also improves the observation-model agreement for some BVOC oxidation products and OH reactivity. We compare the RACM mechanism to a version that includes the Mainz isoprene mechanism (RACM-MIM) to test the model sensitivity to enhanced isoprene degradation. RACM-MIM simulates higher concentrations of both primary BVOC (isoprene and monoterpenes) and oxidation products (HCHO, MACR+MVK) compared with RACM simulations. Additionally, the revised mechanism alters the OH concentrations and increases HO2. These changes generally improve agreement with HOx observations yet overestimate BVOC oxidation products, indicating that this isoprene mechanism does not improve the representation of local chemistry at the site. Overall, the revised mechanism yields smaller changes in BVOC and BVOC oxidation product concentrations and gradients than improving the parameterization of vertical mixing with observations, suggesting that uncertainties in vertical mixing parameterizations are an important component in understanding observed BVOC chemistry.

Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.

The spatial structure and the progression speed of the first ash layer from the Icelandic Eyjafjallajökull volcano which reached Germany on 16/17 April is investigated from remote sensing data and numerical simulations. The ceilometer network of the German Meteorological Service was able to follow the progression of the ash layer over the whole of Germany. This first ash layer turned out to be a rather shallow layer of only several hundreds of metres thickness which was oriented slantwise in the middle troposphere and which was brought downward by large-scale sinking motion over Southern Germany and the Alps. Special Raman lidar measurements, trajectory analyses and in-situ observations from mountain observatories helped to confirm the volcanic origin of the detected aerosol layer. Ultralight aircraft measurements permitted the detection of the arrival of a second major flush of volcanic material in Southern Germany. Numerical simulations with the Eulerian meso-scale model MCCM were able to reproduce the temporal and spatial structure of the ash layer. Comparisons of the model results with the ceilometer network data on 17 April and with the ultralight aircraft data on 19 April were satisfying. This is the first example of a model validation study from this ceilometer network data.

A series of major eruptions of the Eyjafjallajökull volcano in Iceland started on 14 April 2010 and continued until the end of May 2010. The volcanic emissions moved over nearly the whole of Europe and were observed first on 16 April 2010 in Southern Germany with different remote sensing systems from the ground and space. Enhanced PM10 and SO2 concentrations were detected on 17 April at mountain stations (Zugspitze/Schneefernerhaus and Schauinsland) as well as in Innsbruck by in situ measurement devices. On 19 April intensive vertical mixing and advection along with clear-sky conditions facilitated the entrainment of volcanic material down to the ground. The subsequent formation of a stably stratified lower atmosphere with limited mixing near the ground during the evening of 19 April led to an additional enhancement of near-surface particle concentrations. Consequently, on 19 April and 20 April exceedances of the daily threshold value for particulate matter (PM10) were reported at nearly all monitoring stations of the North Alpine foothills as well as at mountain and valley stations in the northern Alps. The chemical analyses of ambient PM10 at monitoring stations of the North Alpine foothills yielded elevated Titanium concentrations on 19/20 April which prove the presence of volcanic plume material. Following this result the PM10 threshold exceedances are also associated with the volcanic plume. The entrainment of the volcanic plume material mainly affected the concentrations of coarse particles (>1 μm) – interpreted as volcanic ash – and ultrafine particles (<100 nm), while the concentrations of accumulation mode aerosol (0.1–1 μm) were not changed significantly. With regard to the occurrence of ultrafine particles, it is concluded that their formation was triggered by high sulphuric acid concentrations which are necessarily generated by the photochemical processes in a plume rich in sulphur dioxide under high solar irradiance. It became evident that during the course of several days, the Eyjafjallajökull volcanic emissions influenced the near-surface atmosphere and thus the ambient air quality. Although the volcanic plume contributed to the overall exposure of the population of the northern Alpine region on two days, only minor effects on the exacerbation of respiratory and cardiovascular symptoms can be expected.

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in . During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

The mixing layer height (MLH) is a measure for the vertical turbulent exchange within the boundary layer, which is one of the controlling factors for the dilution of pollutants emitted near the ground. Based on continuous MLH measurements with a Vaisala CL51 ceilometer and measurements from an air quality network, the relationship between MLH and near-surface pollutant concentrations has been investigated. In this context the uncertainty of the MLH retrievals and the representativeness of ground-based in situ measurements are crucial. We have investigated this topic by using data from the BAERLIN2014 campaign in Berlin, Germany, conducted from June to August 2014. To derive the MLH, three versions of the proprietary software BL-VIEW and a novel approach COBOLT were compared. It was found that the overall agreement is reasonable if mean diurnal cycles are considered. The main advantage of COBOLT is the continuous detection of the MLH with a temporal resolution of 10 min and a lower number of cases when the residual layer is misinterpreted as mixing layer. We have calculated correlations between MLH as derived from the different retrievals and concentrations of pollutants (PM10, O3 and NOx) for different locations in the metropolitan area of Berlin. It was found that the correlations with PM10 are quite different for different sites without showing a clear pattern, whereas the correlation with NOx seems to depend on the vicinity of emission sources in main roads. In the case of ozone as a secondary pollutant, a clear correlation was found. We conclude that the effects of the heterogeneity of the emission sources, chemical processing and mixing during transport exceed the differences due to different MLH retrievals. Moreover, it seems to be unrealistic to find correlations between MLH and near-surface pollutant concentrations representative for a city like Berlin (flat terrain), in particular when traffic emissions are dominant. Nevertheless it is worthwhile to use advanced MLH retrievals for ceilometer data, for example as input to dispersion models and for the validation of chemical transport models.