The European Aerosol Research Lidar Network, EARLINET, was founded in 2000 as a research project for establishing a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution over Europe. This paper gives an overview of the network's main developments since 2000 and introduces the dedicated EARLINET special issue, which reports on the present innovative and comprehensive technical solutions and scientific results related to the use of advanced lidar remote sensing techniques for the study of aerosol properties as developed within the network in the last 13 years. Since 2000, EARLINET has developed greatly in terms of number of stations and spatial distribution: from 17 stations in 10 countries in 2000 to 27 stations in 16 countries in 2013. EARLINET has developed greatly also in terms of technological advances with the spread of advanced multiwavelength Raman lidar stations in Europe. The developments for the quality assurance strategy, the optimization of instruments and data processing, and the dissemination of data have contributed to a significant improvement of the network towards a more sustainable observing system, with an increase in the observing capability and a reduction of operational costs. Consequently, EARLINET data have already been extensively used for many climatological studies, long-range transport events, Saharan dust outbreaks, plumes from volcanic eruptions, and for model evaluation and satellite data validation and integration. Future plans are aimed at continuous measurements and near-real-time data delivery in close cooperation with other ground-based networks, such as in the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) www.actris.net, and with the modeling and satellite community, linking the research community with the operational world, with the aim of establishing of the atmospheric part of the European component of the integrated global observing system.

The aerosol light absorption coefficient is an essential parameter involved in atmospheric radiation budget calculations. The Aethalometer (AE) has the great advantage of measuring the aerosol light absorption coefficient at several wavelengths, but the derived absorption coefficients are systematically too high when compared to reference methods. Up to now, four different correction algorithms of the AE absorption coefficients have been proposed by several authors. A new correction scheme based on these previously published methods has been developed, which accounts for the optical properties of the aerosol particles embedded in the filter. All the corrections have been tested on six datasets representing different aerosol types and loadings and include multi-wavelength AE and white-light AE. All the corrections have also been evaluated through comparison with a Multi-Angle Absorption Photometer (MAAP) for four datasets lasting between 6 months and five years. The modification of the wavelength dependence by the different corrections is analyzed in detail. The performances and the limits of all AE corrections are determined and recommendations are given.

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain.

Estimates of PM2.5 distributions based on satellite data depend critically on an established relation between AOD and ground level PM2.5. In this study we performed an experiment at Cabauw to establish a relation between AOD and PM2.5 for the Netherlands. A first inspection of the AERONET L1.5 AOD and PM2.5 data showed a low correlation between the two properties. The AERONET L1.5 showed relatively many observations of high AOD values paired to low PM2.5 values, which hinted cloud contamination. Various methods were used to detect cloud contamination in the AERONET data to substantiate this hypothesis. A cloud screening method based on backscatter LIDAR observations was chosen to detect cloud contaminated observations in the AERONET L1.5 AOD. A later evaluation of AERONET L2.0 showed that the most data that are excluded in the update from L1.5 to L2.0 were also excluded by our cloud screening, which provides confidence in both our cloud-screening method as well as the final screening in the AERONET procedure. The use of LIDAR measurements in conjunction with the CIMEL AOD data is regarded highly beneficial. Contra-intuitively, the AOD to PM2.5 relationship was shown to be insensitive to inclusion of the mixed layer height. The robustness of the relation improves dependent on the time window during the day towards noon. The final relation found for Cabauw is PM2.5=124.5×AOD−0.34 and is valid for fair weather conditions. The relationship found between bias corrected MODIS AOD and PM2.5 at Cabauw is very similar to the analysis based on the much larger dataset from ground based data only. We applied the relationship to a MODIS composite map to assess the PM2.5 distribution over the Netherlands for the first time. The verification of the derived map is difficult because ground level artefact free PM2.5 data are lacking. The validity and utility of our proposed mapping methodology should be further investigated.