Explore the vast range of titles available.

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 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.

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 determination of the depth of daytime and nighttime mixing layers must be known very accurately to relate boundary-layer concentrations of gases or particles to upstream fluxes. The mixing-height is parametrized in numerical weather prediction models, so improving the determination of the mixing height will improve the quality of the estimated gas and particle budgets. Datasets of mixing-height diurnal cycles with high temporal and spatial resolutions are sought by various end users. Lidars and ceilometers provide vertical profiles of backscatter from aerosol particles. As aerosols are predominantly concentrated in the mixing layer, lidar backscatter profiles can be used to trace the depth of the mixing layer. Large numbers of automatic profiling lidars and ceilometers are deployed by meteorological services and other agencies in several European countries providing systems to monitor the mixing height on temporal and spatial scales of unprecedented density. We investigate limitations and capabilities of existing mixing height retrieval algorithms by applying five different retrieval techniques to three different lidars and ceilometers deployed during two 1-month campaigns. We studied three important steps in the mixing height retrieval process, namely the lidar/ceilometer pre-processing to reach sufficient signal-to-noise ratio, gradient detection techniques to find the significant aerosol gradients, and finally quality control and layer attribution to identify the actual mixing height from multiple possible layer detections. We found that layer attribution is by far the most uncertain step. We tested different gradient detection techniques, and found no evidence that the first derivative, wavelet transform, and two-dimensional derivative techniques have different skills to detect one or multiple significant aerosol gradients from lidar and ceilometer attenuated backscatter. However, our study shows that, when mixing height retrievals from a ultraviolet lidar and a near-infrared ceilometer agreed, they were 25–40% more likely to agree with an independent radiosonde mixing height retrieval than when each lidar or ceilometer was used alone. Furthermore, we point to directions that may assist the layer attribution step, for instance using commonly available surface measurements of radiation and temperature to derive surface sensible heat fluxes as a proxy for the intensity of convective mixing. It is a worthwhile effort to pursue such studies so that within a few years automatic profiling lidar and ceilometer networks can be utilized efficiently to monitor mixing heights at the European scale.

A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for the height of the ABL and its sublayers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Here, the advanced algorithm STRATfinder is presented for application to high signal-to-noise ratio (SNR) ALC observations, and results are compared to an automatic algorithm designed for low-SNR measurements (CABAM). The two algorithms are evaluated for application in an operational network setting. Results indicate that the ABL heights derived from low-SNR ALC have increased uncertainty during daytime deep convection, while high-SNR observations can have slightly reduced capabilities in detecting shallow nocturnal layers. Agreement between the ALC-based methods is similar when either is compared to the ABL heights derived from temperature profile data. The two independent methods describe very similar average diurnal and seasonal variations. Hence, high-quality products of ABL heights may soon become possible at national and continental scales.

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 Vaisala ceilometers CT25K and CL31 are eye-safe single lens lidar systems reporting attenuated backscatter profiles; they often operate 24 h a day in fully automated, hands-off operation mode. These profiles can be used for more than just cloud-base height determination. In dry weather situations, there is a fairly good correlation between the ceilometer near-range backscatter and in situ PM10 concentration readings. The comparison of mixing height values based on soundings and on ceilometer backscattering profiles indicates that ceilometers are suitable instruments for determining the convective mixing height. Its enhanced optics and electronics enables the CL31 ceilometer to detect fine boundary-layer structures whose counterparts are seen in temperature profiles.

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and, hence, the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometers, a lidar instrument to measure cloud base height, have been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ), as well as to understand the aerosol transport from sources to the receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM10, PM2.5, NO2), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate.

A new method is described to derive mixing-height time series directly from aerosol-layer height data available from a Vaisala CL51 ceilometer. As complete as possible mixing-height time series are calculated by avoiding outliers, filling data gaps by linear interpolation, and smoothing. In addition, large aerosol-layer heights at night that can be interpreted as residual layers are not assigned as mixing heights. The resulting mixing-height time series, converted to an appropriate data format, can be used as input for dispersion calculations. Two case examples demonstrate in detail how the method works. The mixing heights calculated using ceilometer data are compared with values determined from radiosounding data at Vienna by applying the parcel, Heffter, and Richardson methods. The results of the parcel method, obtained from radiosonde profiles at noon, show the best fit to the ceilometer-derived mixing heights. For midnight radiosoundings, larger deviations between mixing heights from the ceilometer and those deduced from the potential temperature profiles of the soundings are found. We use data from two Vaisala CL51 ceilometers, operating in the Vienna area at an urban and rural site, respectively, during an overlapping period of about 1 year. In addition to the case studies, the calculated mixing-height time series are also statistically evaluated and compared, demonstrating that the ceilometer-based mixing height follows an expected daily and seasonal course.

Elastic backscatter lidar and ceilometer systems provide continuous observations of aerosol and cloud vertical structure, but the interpretation of conventional attenuated backscatter products is often limited by the dominance of signal amplitude, strong event-to-event variability, and the reduced visibility of subtle internal features. In this study, we present a refinement framework designed to extract additional structural information from elastic lidar measurements through multiscale local diagnostics applied directly to the native backscatter field. The methodology combines standardized residual fields, local gradients, variance-based metrics, space–time decorrelation scales and structure functions to highlight atmospheric boundaries, internal layering, mixing zones, and coherent structures that are not always evident in conventional representations. The approach is evaluated through three contrasting atmospheric case studies observed in 2024. Two spring events are associated with mineral dust intrusions characterized by different vertical coupling with the planetary boundary layer, while a summer case represents a non-dust regime dominated by diurnal boundary-layer evolution. The refined diagnostics consistently reveal features hidden or only weakly visible in the raw backscatter field, including sharp interfaces, embedded stratification, wave-like perturbations and transitions between decoupled and mixed atmospheric states. Results show that the proposed metrics enable a more objective description of aerosol-layer dynamics and boundary–layer interactions without requiring complex inversion procedures or auxiliary measurements. Because the method relies only on standard elastic lidar observations, it is in principle applicable to ceilometer and lidar monitoring networks. However, the present evaluation is based on three contrasting case studies and should therefore be regarded as a proof-of-concept demonstration. The framework offers a candidate pathway for enhanced atmospheric feature detection and improved interpretation of routine profiling observations, with automated regime classification as a longer-term goal requiring validation on larger and more diverse datasets.