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The Aerosols, Clouds and Trace gases Research Infrastructure (ACTRIS) is a distributed infrastructure dedicated to high-quality observation of aerosols, clouds, trace gases and exploration of their interactions. It will deliver precision data, services and procedures regarding the 4D variability of clouds, short-lived atmospheric species and the physical, optical and chemical properties of aerosols to improve the current capacity to analyse, understand and predict past, current and future evolution of the atmospheric environment.

Aerosols play an important role in global meteorology and climate, as well as in air transport and human health, but there are still many unknowns on their effects and importance, in particular for the coarser (giant and ultragiant) aerosol particles. In this study, we aim to exploit the synergy between Raman lidar and Ka-band cloud radar to enlarge the size range in which aerosols can be observed and characterized. To this end, we developed an inversion technique that retrieves the aerosol microphysical properties based on cloud radar reflectivity and linear depolarization ratio. We applied this technique to a 6-year-long dataset, which was created using a recently developed methodology for the identification of giant aerosols in cloud radar measurements, with measurements from Potenza in Italy. Similarly, using collocated and concurrent lidar profiles, a dataset of aerosol microphysical properties using a widely used inversion technique complements the radar-retrieved dataset. Hence, we demonstrate that the combined use of lidar- and radar-derived aerosol properties enables the inclusion of particles with radii up to 12 µm, which is twice the size typically observed using atmospheric lidar alone.

In this study, we present an innovative methodology for the identification of giant aerosols using cloud radar. The methodology makes use of several insects studies in order to separate radar-derived atmospheric plankton signatures into the contributions of insects and giant aerosols. The methodology is then applied to a 6-year-long cloud radar dataset in Potenza, South Italy. Forty giant aerosol events per year were found, which is in good agreement with the site’s climatological record. A sensitivity study on the effects of the giant aerosols on three atmospheric variables and under different atmospheric stability conditions showed that the presence of giant aerosols (a) increased the aerosol optical depth in all the atmospheric stability conditions, (b) decreased the Ångström exponent for the highest and lowest stability conditions and had the opposite effect for the intermediate stability condition, and (c) increased the accumulated precipitation in all the atmospheric conditions, especially in the most unstable ones.

Mineral dust produced by wind erosion of arid and semiarid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socioeconomic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust-forecast-tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities, including in situ, ground-based, and satellite remote sensing observations in northern Africa, the Middle East, and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders.

The CALIPSO Level 3 (CL3) product is the most recent data set produced by the observations of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud–Aerosol Lidar and Pathfinder Satellite Observations (CALIPSO) space platform. The European Aerosol Research Lidar Network (EARLINET), based mainly on multi-wavelength Raman lidar systems, is the most appropriate ground-based reference for CALIPSO calibration/validation studies on a continental scale. In this work, CALIPSO data are compared against EARLINET monthly averaged profiles obtained by measurements performed during CALIPSO overpasses. In order to mitigate uncertainties due to spatial and temporal differences, we reproduce a modified version of CL3 data starting from CALIPSO Level 2 (CL2) data. The spatial resolution is finer and nearly 2°  ×  2° (latitude  ×  longitude) and only simultaneous measurements are used for ease of comparison. The CALIPSO monthly mean profiles following this approach are called CALIPSO Level 3*, CL3*. We find good agreement on the aerosol extinction coefficient, yet in most of the cases a small CALIPSO underestimation is observed with an average bias of 0.02 km−1 up to 4 km and 0.003 km−1 higher above. In contrast to CL3 standard product, the CL3* data set offers the possibility to assess the CALIPSO performance also in terms of the particle backscatter coefficient keeping the same quality assurance criteria applied to extinction profiles. The mean relative difference in the comparison improved from 25 % for extinction to 18 % for backscatter, showing better performances of CALIPSO backscatter retrievals. Additionally, the aerosol typing comparison yielded a robust identification of dust and polluted dust. Moreover, the CALIPSO aerosol-type-dependent lidar ratio selection is assessed by means of EARLINET observations, so as to investigate the performance of the extinction retrievals. The aerosol types of dust, polluted dust, and clean continental showed noticeable discrepancy. Finally, the potential improvements of the lidar ratio assignment have been examined by adjusting it according to EARLINET-derived values.

In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m−2 at surface and 0.007 W m−2 at top of the atmosphere) and dust aerosol layers (0.7 W m−2 at surface and 0.85 W m−2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m−2 at surface and 2.7 W m−2 at top of the atmosphere) and opaque (7.7 W m−2 at surface and 11.8 W m−2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20–150 sr) than for clouds (20–35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

A strategy for European Aerosol Research Lidar Network (EARLINET) correlative measurements for Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has been developed. These EARLINET correlative measurements started in June 2006 and are still in progress. Up to now, more than 4500 correlative files are available in the EARLINET database. Independent extinction and backscatter measurements carried out at high‐performance EARLINET stations have been used for a quantitative comparison with CALIPSO level 1 data. Results demonstrate the good performance of CALIPSO and the absence of evident biases in the CALIPSO raw signals. The agreement is also good for the distribution of the differences for the attenuated backscatter at 532 nm ((CALIPSO‐EARLINET)/EARLINET (%)), calculated in the 1–10 km altitude range, with a mean relative difference of 4.6%, a standard deviation of 50%, and a median value of 0.6%. A major Saharan dust outbreak lasting from 26 to 31 May 2008 has been used as a case study for showing first results in terms of comparison with CALIPSO level 2 data. A statistical analysis of dust properties, in terms of intensive optical properties (lidar ratios, Ångström exponents, and color ratios), has been performed for this observational period. We obtained typical lidar ratios of the dust event of 49 ± 10 sr and 56 ± 7 sr at 355 and 532 nm, respectively. The extinction‐related and backscatter‐related Ångström exponents were on the order of 0.15–0.17, which corresponds to respective color ratios of 0.91–0.95. This dust event has been used to show the methodology used for the investigation of spatial and temporal representativeness of measurements with polar‐orbiting satellites.

The Integrated Carbon Observation System (ICOS) is the reference Research Infrastructure (RI) for the observation of greenhouse gases (GHGs) across Europe, providing standardised, long-term and high-precision measurements of the most relevant species (CO2, CH4, CO, etc.). The ICOS Atmosphere network currently extends throughout the continent, although the density of stations in the Mediterranean area is still low compared to Central and Northern Europe. In this context, the recently implemented class 1 continental station near Potenza in Basilicata, Italy—station code: POT—represents an important step forward in the extension of the ICOS atmosphere domain across the South, reducing the large spatial gaps existing between ICOS sites within the Mediterranean basin. Herein, we provide a description of the new ICOS POT station and the site where it operates, focusing mostly on the technical setup of the sampling system which plays a key role in GHG measurements. With a strong technical connotation, the present paper aims to be beneficial for the ICOS atmosphere community and those stations that intend to join the network in the future, providing an accurate description of the station at the level of single components. Moreover, a brief overview of the peculiarities of the site and the scientific perspectives to be pursued, together with very preliminary data collected at the new ICOS station, are presented. Preliminary data collected during a short campaign are compared with STILT (Stochastic Time-Inverted Lagrangian Transport) model results as a first test of the measurements and to provide a first insight of the specific Potenza situation in terms of GHG concentrations.

Two tailored configurations of the Robust Satellite Technique (RST) multi-temporal approach, for airborne volcanic ash and desert dust detection, have been tested in the framework of the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. The two algorithms, running on Spinning Enhanced Visible Infra-Red Imager (SEVIRI) data, were previously assessed over wide areas by comparison with independent satellite-based aerosol products. In this study, we present results of a first validation analysis of the above mentioned satellite-based ash/dust products using independent, ground-based observations coming from the European Aerosol Research Lidar Network (EARLINET). The aim is to assess the capabilities of RST-based ash/dust products in providing useful information even at local scale and to verify their applicability as a “trigger” to timely activate EARLINET measurements during airborne hazards. The intense Saharan dust event of May 18–23 2008—which affected both the Mediterranean Basin and Continental Europe—and the strong explosive eruptions of Eyjafjallajökull (Iceland) volcano of April–May 2010, were analyzed as test cases. Our results show that both RST-based algorithms were capable of providing reliable information about the investigated phenomena at specific sites of interest, successfully detecting airborne ash/dust in different geographic regions using both nighttime and daytime SEVIRI data. However, the validation analysis also demonstrates that ash/dust layers remain undetected by satellite in the presence of overlying meteorological clouds and when they are tenuous (i.e., with an integrated backscatter coefficient less than ~0.001 sr−1 and with aerosol backscatter coefficient less than ~1 × 10−6 m−1sr−1). This preliminary analysis confirms that the continuity of satellite-based observations can be used to timely “trigger” ground-based LIDAR measurements in case of airborne hazard events. Finally, this work confirms that advanced satellite-based detection schemes may provide a relevant contribution to the monitoring of ash/dust phenomena and that the synergistic use of (satellite-based) large scale, continuous and timely records with (ground-based) accurate and quantitative measurements may represent an added value, especially in operational scenarios.