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Characterizing inter‐instrument variability of sensors is crucial to assessing uncertainties in observational campaigns, networks, and for data assimilation. Here, we co‐locate six high signal‐to‐noise ratio Vaisala CL61 lidar‐ceilometers for a period of 10 days to quantify instrument‐related differences in several observed variables: profiles of attenuated backscatter, its components (parallel‐ and cross‐polarized backscatter) and the volume linear depolarisation ratio (δ $$ \\delta $$ ), as well as derived cloud variables and mixed‐layer height. Analysing intervals between 5 and 60 min, median absolute differences between sensors (AD50 $$ {}_{50} $$ ) and percentiles (e.g., AD75 $$ {}_{75} $$ ) are used to quantify instrument related uncertainties. For backscatter and δ $$ \\delta $$ , we differentiate between conditions with rain, clear sky, and clouds. Here we address instrument precision rather than accuracy, with instrument accuracy assumed. The detected agreement between instruments suggests a distributed measurement network should be capable of providing context for interpretation of spatial differences. If instruments measure accurately, it is possible to resolve spatial differences (e.g., urban–rural) for attenuated backscatter, derived cloud variables and layer heights. However, differences exist and vary with signal‐to‐noise ratio and atmospheric conditions. The AD50 $$ {}_{50} $$inter‐sensor results for 15 min intervals for total cloud‐cover fraction (excluding clear sky and fully overcast conditions) is 1.9%, and for cloud base height 7.3 m. Agreement of all cloud variables is better for boundary layer clouds (when first cloud layer < $$ < $$4 km agl) than for all five cloud layers recorded by the sensor firmware. The 15 min mixed‐layer height AD50 $$ {}_{50} $$is 0 m and the AD75 $$ {}_{75} $$21.5 m. We show that instrument precipitation flags are in good agreement, but do not link closely with ground‐level rainfall observations, hence an alternative algorithm is proposed. We provide quality control recommendations for data processing to improve inter‐instrument agreement of cloud variables and mixed‐layer height. Instrument‐related differences between six co‐located Vaisala CL61 automatic lidar‐ceilometers are quantified for several observed variables: profiles of attenuated backscatter (β) and the linear depolarisation ratio, as well as derived cloud variables and mixed‐layer height. Figure shows variation of relative differences (%) in β (m−1 sr−1) with atmospheric conditions as absolute differences divided by observed values. When instruments are measuring accurately, it is possible to resolve spatial differences (e.g., urban‐rural).

The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth's surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sub-layer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe.

The dilution effect caused by boundary-layer evolution over land has strong influences on air quality. Accurate and continuous measurements of the boundary-layer height over urban areas are therefore needed for complete air-quality assessments. Commercial ceilometers, in combination with reliable and simple methodologies, can be used to retrieve the mixed-layer height, and represent a means of obtaining information on vertical mixing and atmospheric structure above cities. Here, we evaluate various retrieval algorithms based on the gradient method against high-temporal-resolution radiosonde observations. Based on the results, we propose a simple algorithm by using the gradient method, the correction of background noise and the moving averages, with the minimum number of parameters that need to be adjusted to the local properties and the instrument itself. The algorithm is adjusted for Seoul, Korea, and improves the retrieval performance by reducing high-frequency noise. The algorithm is used to investigate the relationship between the evolution of the daytime mixed-layer height and air pollution under a two-layer mixing model where changes in concentration depend only on the urban boundary-layer growth and air entrainment from the free atmosphere. Using 2 months of ceilometer retrievals of mixed-layer height and air-quality data from across the city, we find strong negative correlations for primary emitted pollutants such as NO2, CO, SO2, and particulate matter smaller than 10 µm, and a modest positive correlation for O3. The results provide insight into the significant influence of urban boundary-layer evolution on Seoul’s air quality.

To investigate the spatiotemporal variability of the mixing layer height (MLH) on the North China Plain (NCP), multi-site and long-term observations of the MLH with ceilometers at three inland stations (Beijing, BJ; Shijiazhuang, SJZ; Tianjin, TJ) and one coastal site (Qinhuangdao) were conducted from 16 October 2013 to 15 July 2015. The MLH of the inland stations in the NCP were highest in summer and lowest in winter, while the MLH on the coastal area of Bohai was lowest in summer and highest in spring. As a typical site in southern Hebei, the annual mean of the MLH at SJZ was 464 ± 183 m, which was 15.0 and 21.9 % lower than that at the BJ (594 ± 183 m) and TJ (546 ± 197 m) stations, respectively. Investigation of the shear term and buoyancy term in the NCP revealed that these two parameters in southern Hebei were 2.8 times lower and 1.5 times higher than that in northern NCP within 0–1200 m in winter, respectively, leading to a 1.9-fold higher frequency of the gradient Richardson number > 1 in southern Hebei compared to the northern NCP. Furthermore, combined with aerosol optical depth and PM2.5 observations, we found that the pollutant column concentration contrast (1.2 times) between these two areas was far less than the near-ground PM2.5 concentration contrast (1.5 times). Through analysis of the ventilation coefficient in the NCP, the near-ground heavy pollution in southern Hebei mainly resulted from the lower MLH and wind speed. Therefore, due to the importance of unfavorable weather conditions, heavily polluting enterprises should be relocated and strong emission reduction measures should be introduced to improve the air quality in southern Hebei.

The Jülich Observatory for Cloud Evolution (JOYCE), located at Forschungszentrum Jülich in the most western part of Germany, is a recently established platform for cloud research. The main objective of JOYCE is to provide observations, which improve our understanding of the cloudy boundary layer in a midlatitude environment. Continuous and temporally highly resolved measurements that are specifically suited to characterize the diurnal cycle of water vapor, stability, and turbulence in the lower troposphere are performed with a special focus on atmosphere–surface interaction. In addition, instruments are set up to measure the micro- and macrophysical properties of clouds in detail and how they interact with different boundary layer processes and the large-scale synoptic situation. For this, JOYCE is equipped with an array of state-of-the-art active and passive remote sensing and in situ instruments, which are briefly described in this scientific overview. As an example, a 24-h time series of the evolution of a typical cumulus cloud-topped boundary layer is analyzed with respect to stability, turbulence, and cloud properties. Additionally, we present longer-term statistics, which can be used to elucidate the diurnal cycle of water vapor, drizzle formation through autoconversion, and warm versus cold rain precipitation formation. Both case studies and long-term observations are important for improving the representation of clouds in climate and numerical weather prediction models.

To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

The interest in the use of ceilometers for optical aerosol characterization has increased in the last few years. They operate continuously almost unattended and are also much less expensive than lidars; hence, they can be distributed in dense networks over large areas. However, due to the low signal-to-noise ratio it is not always possible to obtain particle backscatter coefficient profiles, and the vast number of data generated require an automated and unsupervised method that ensures the quality of the profiles inversions. In this work we describe a method that uses aerosol optical depth (AOD) measurements from the AERONET network that it is applied for the calibration and automated quality assurance of inversion of ceilometer profiles. The method is compared with independent inversions obtained by co-located multiwavelength lidar measurements. A difference smaller than 15 % in backscatter is found between both instruments. This method is continuously and automatically applied to the Iberian Ceilometer Network (ICENET) and a case example during an unusually intense dust outbreak affecting the Iberian Peninsula between 20 and 24 February 2016 is shown. Results reveal that it is possible to obtain quantitative optical aerosol properties (particle backscatter coefficient) and discriminate the quality of these retrievals with ceilometers over large areas. This information has a great potential for alert systems and model assimilation and evaluation.

Mountainous regions are often considered pristine environments; however they can be affected by pollutants emitted in more populated and industrialised areas, transported by regional winds. Based on experimental evidence, further supported by modelling tools, here we demonstrate and quantify the impact of air masses transported from the Po Valley, a European atmospheric pollution hotspot, to the northwestern Alps. This is achieved through a detailed investigation of the phenomenology of near-range (a few hundred kilometres), trans-regional transport, exploiting synergies of multi-sensor observations mainly focussed on particulate matter. The explored dataset includes vertically resolved data from atmospheric profiling techniques (automated lidar ceilometers, ALCs), vertically integrated aerosol properties from ground (sun photometer) and space, and in situ measurements (PM10 and PM2.5, relevant chemical analyses, and aerosol size distribution). During the frequent advection episodes from the Po basin, all the physical quantities observed by the instrumental setup are found to significantly increase: the scattering ratio from ALC reaches values >30, aerosol optical depth (AOD) triples, surface PM10 reaches concentrations >100 µg m−3 even in rural areas, and contributions to PM10 by secondary inorganic compounds such as nitrate, ammonium, and sulfate increase up to 28 %, 8 %, and 17 %, respectively. Results also indicate that the aerosol advected from the Po Valley is hygroscopic, smaller in size, and less light-absorbing compared to the aerosol type locally emitted in the northwestern Italian Alps. In this work, the phenomenon is exemplified through detailed analysis and discussion of three case studies, selected for their clarity and relevance within the wider dataset, the latter being fully exploited in a companion paper quantifying the impact of this phenomenology over the long-term (Diémoz et al., 2019). For the three case studies investigated, a high-resolution numerical weather prediction model (COSMO) and a Lagrangian tool (LAGRANTO) are employed to understand the meteorological mechanisms favouring transport and to demonstrate the Po Valley origin of the air masses. In addition, a chemical transport model (FARM) is used to further support the observations and to partition the contributions of local and non-local sources. Results show that the simulations are important to the understanding of the phenomenon under investigation. However, in quantitative terms, modelled PM10 concentrations are 4–5 times lower than the ones retrieved from the ALC and maxima are anticipated in time by 6–7 h. Underestimated concentrations are likely mainly due to deficiencies in the emission inventory and to water uptake of the advected particles not fully reproduced by FARM, while timing mismatches are likely an effect of suboptimal simulation of up-valley and down-valley winds by COSMO. The advected aerosol is shown to remarkably degrade the air quality of the Alpine region, with potential negative effects on human health, climate, and ecosystems, as well as on the touristic development of the investigated area. The findings of the present study could also help design mitigation strategies at the trans-regional scale in the Po basin and suggest an observation-based approach to evaluate the outcome of their implementation.

Over the remote Southern Ocean (SO), cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud-top heights < ∼ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016–March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5∘ S, 158.9∘ E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from the liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ∼ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud-top temperatures > 0 ∘C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of spaceborne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ∼ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ∼ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs.

A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.