Ceilometer Data Research Articles

Intercomparison of Machine Learning Models to Determine the Planetary Boundary Layer Height Over Central Amazonia

AbstractThe planetary boundary layer height (zi) is a key parameter in meteorology and climatology, influencing weather prediction, cloud formation, and the vertical transport of scalars and energy near Earth's surface. This study compares multiple machine learning (ML) models that predict zi from surface measurements at two sites in Central Amazonia—the Amazon Tall Tower Observatory (ATTO) and the Manacapuru site of the GoAmazon experiment (T3). Models were trained on ceilometer data with radiosonde measurements used for validation. We evaluated model performance by withholding approximately 10% of the data (as complete months) for testing, comparing predictions against ERA‐5 reanalysis data using RMSE, nRMSE, and R2 metrics. Our results show that gradient boosted ensemble models using all available features perform best. A modified recursive feature elimination algorithm identified minimal sets of 5–7 surface measurements sufficient for accurate zi prediction, demonstrating potential for wider spatial monitoring using cost‐effective sensors. The study revealed previously unrecognized variables influential in determining zi, such as deep soil temperature measurements (40 cm), suggesting new avenues for investigating land‐atmosphere interactions. This study demonstrates the applicability of ML models to model zi.

Calibration of backscattering coefficients with coherent heterodyne lidar utilizing molecular scattering.

Coherent heterodyne lidars are typically used for windspeed and attenuated backscattering measurements. The lack of molecular backscattering detection capability has limited the calibrated backscattering measurements until recent advances in coherent lidar technology. In this work, the simultaneous detection of aerosol and molecular backscattering is demonstrated with coherent heterodyne lidar, and the results are compared with a state-of-the-art Raman lidar PollyXT as a reference in a long-range for the first time. The molecular scattering is measured up to 3 km altitude and the extinction of laser power is calculated based on the attenuation. The essential corrections for the molecular scattering data are described and discussed. The backscattering coefficients are calculated with simple high spectral resolution lidar algorithms and the low altitude overlap is calibrated with on-site ceilometer data. A successful high spectral resolution measurement within coherent heterodyne lidar enables the self-calibration of the measured data and removes guessing of the backscatter to extinction ratio and initial values for the typical inversion algorithms. The self-calibration also enables basic aerosol classification based on the measured backscatter to extinction ratios.

Comparison of horizontal visibility observed by present weather sensor and human eye-based method and evaluated from ceilometer’s vertical backscattering profile over two Hungarian meteorological stations

This research aims to evaluate a quantitative comparison of the visibilities detected by three different methods: (i) human eye-based observations; (ii) calculated from the measured values of forward scatter visibility sensor; and (iii) evaluated from ceilometer’s vertical backscattering profiles. Visibility data observed at two meteorological stations, PestszentlĘrinc and Szeged (Hungary) were analyzed. The paper focuses on the fog events observed during the periods of October-December in 2019 and November 21- 24 in 2020. The results reveal that the visibility observed by the three methods can be significantly different. Comparison of the values of visibility detected by the three different techniques shows, that visibility evaluated from the ceilometer data is the largest, and the human eye-based observation detects the smallest values. Analysis of the data about fog detection (yes or no) reveals that the ceilometer detects significantly shorter duration of the fog than the other two methods.

Unveiling the Correlation Between Lyapunov Coefficients and Deep Learning Performance Using Ceilometer Data

Unveiling the Correlation Between Lyapunov Coefficients and Deep Learning Performance Using Ceilometer Data

CAECENET: An automatic system processing photometer and ceilometer data from different networks to provide columnar and vertically-resolved aerosol properties.

This work introduces CAECENET, a new system capable of automatically retrieving columnar and vertically-resolved aerosol properties running the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm using sun-sky photometer (aerosol optical depth, AOD; and sky radiance measurements) and ceilometer (range corrected signal; RCS) data as input. This method, so called GRASPpac, is implemented in CAECENET, which assimilates sun-sky photometers data from CÆLIS database and ceilometer data from ICENET database (Iberian Ceilometer Network). CAECENET allows for continuous and near-real-time monitoring of both vertical and columnar aerosol properties. The main characteristics and workflow of CAECENET are explained in detail. This work also explores the potential of CAECENET to monitor and analyze the evolution of transported aerosol events on a regional scale by means of the distribution of CAECENET stations across the Iberian Peninsula. As an example, this paper analyzes, using the CAECENET products, the case of a Saharan dust outbreak that occurred between the 3rd and 5th of October 2022. This was an intense event, with AOD at 440 nm values around 0.5 in Madrid and Valladolid, and reaching 1.55 in Granada. Transport from the Canadian wildfires at the end of June 2023 is also studied. Despite the long-range transport of the smoke particles in this event, measured volume concentrations reached and surpassed 80 μm3/cm3 in some stations. The results obtained point to the utility of this CAECENET tool for analyzing changes in the height and speed of the event propagation, in the aerosol concentration, and how this affects the optical properties.

Ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data

Abstract. Ceilometers are used routinely at aerodromes worldwide to derive the height and sky coverage fraction of cloud layers. This information, possibly combined with direct observations by human observers, contributes to the production of meteorological aerodrome reports (METARs). Here, we present ampycloud, a new algorithm, and its associated Python package for automatic processing of ceilometer data with the aim of determining the sky coverage fraction and base height of cloud layers above aerodromes. The ampycloud algorithm was developed at the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) as part of the AMAROC (AutoMETAR/AutoReport rOund the Clock) program to help in the fully automatic production of METARs at Swiss civil aerodromes. ampycloud is designed to work with no direct human supervision. The algorithm consists of three distinct, sequential steps that rely on agglomerative clustering methods and Gaussian mixture models to identify distinct cloud layers from individual cloud base hits reported by ceilometers. The robustness of the ampycloud algorithm stems from the first processing step, which is simple and reliable. It constrains the two subsequent processing steps that are more sensitive but also better suited to handling complex cloud distributions. The software implementation of the ampycloud algorithm takes the form of an eponymous, pip-installable Python package developed on GitHub and made publicly accessible.

Refining Planetary Boundary Layer Height Retrievals From Micropulse‐Lidar at Multiple ARM Sites Around the World

AbstractKnowledge of the planetary boundary layer height (PBLH) is crucial for various applications in atmospheric and environmental sciences. Lidar measurements are frequently used to monitor the evolution of the PBLH, providing more frequent observations than traditional radiosonde‐based methods. However, lidar‐derived PBLH estimates have substantial uncertainties, contingent upon the retrieval algorithm used. In addressing this, we applied the Different Thermo‐Dynamic Stabilities (DTDS) algorithm to establish a PBLH data set at five separate Department of Energy's Atmospheric Radiation Measurement sites across the globe. Both the PBLH methodology and the products are subject to rigorous assessments in terms of their uncertainties and constraints, juxtaposing them with other products. The DTDS‐derived product consistently aligns with radiosonde PBLH estimates, with correlation coefficients exceeding 0.77 across all sites. This study delves into a detailed examination of the strengths and limitations of PBLH data sets with respect to both radiosonde‐derived and other lidar‐based estimates of the PBLH by exploring their respective errors and uncertainties. It is found that varying techniques and definitions can lead to diverse PBLH retrievals due to the inherent intricacy and variability of the boundary layer. Our DTDS‐derived PBLH data set outperforms existing products derived from ceilometer data, offering a more precise representation of the PBLH. This extensive data set paves the way for advanced studies and an improved understanding of boundary‐layer dynamics, with valuable applications in weather forecasting, climate modeling, and environmental studies.

Impact of Urbanization on Cloud Characteristics over Sofia, Bulgaria

Urban artificial surfaces and structures induce modifications in land–atmosphere interactions, affecting the exchange of energy, momentum, and substances. These modifications stimulate urban climate formation by altering the values and dynamics of atmospheric parameters, including cloud-related features. This study evaluates the presence and quantifies the extent of such changes over Sofia, Bulgaria. The findings reveal that estimations of low-level cloud base height (CBH) derived from lifting condensation level (LCL) calculations may produce unexpected outcomes due to microclimate influence. Ceilometer data indicate that the CBH of low-level clouds over urban areas exceeds that of surrounding regions by approximately 200 m during warm months and afternoon hours. Moreover, urban clouds exhibit reduced persistence relative to rural counterparts, particularly pronounced in May, June, and July afternoons. Reanalysis-derived low-level cloud cover (LCC) shows no significant disparities between urban and rural areas, although increased LCC is observed above the western and northern city boundaries. Satellite-derived cloud products reveal that the optically thinnest low-level clouds over urban areas exhibit slightly higher cloud tops, but the optically thickest clouds are more prevalent during warm months. These findings suggest an influence of urbanization on cloudiness, albeit nuanced and potentially influenced by the city size and surrounding physical and geographical features.

CALOTRITON: a convective boundary layer height estimation algorithm from ultra-high-frequency (UHF) wind profiler data

Abstract. Long time series of observations of atmospheric dynamics and composition are collected at the French Pyrenean Platform for Observation of the Atmosphere (P2OA). Planetary boundary layer depth is a key variable of the climate system, but it remains difficult to estimate and analyse statistically. In order to obtain reliable estimates of the convective boundary layer height (Zi) and to allow long-term series analyses, a new restitution algorithm, named CALOTRITON, has been developed. It is based on the observations of an ultra-high-frequency (UHF) radar wind profiler (RWP) from P2OA with the help of other instruments for evaluation. Estimates of Zi are based on the principle that the top of the convective boundary layer is associated with both a marked inversion and a decrease in turbulence. Those two criteria are respectively manifested by larger RWP reflectivity and smaller vertical-velocity Doppler spectral width. With this in mind, we introduce a new UHF-deduced dimensionless parameter which weighs the air refractive index structure coefficient with the inverse of vertical velocity standard deviation to the power of x. We then search for the most appropriate local maxima of this parameter for Zi estimates with defined criteria and constraints such as temporal continuity. Given that Zi should correspond to fair-weather cloud base height, we use ceilometer data to optimize our choice of the power x and find that x=3 provides the best comparisons. The estimates of Zi by CALOTRITON are evaluated using different Zi estimates deduced from radiosounding according to different definitions. The comparison shows excellent results with a regression coefficient of up to 0.96 and a root-mean-square error of 71 m, which is close to the vertical resolution of the UHF RWP of 75 m, when conditions are optimal. In more complex situations, that is when the atmospheric vertical structure is itself particularly ambiguous, secondary retrievals allow us to identify potential thermal internal boundary layers or residual layers and help to qualify the Zi estimations. Frequent estimate errors are observed nevertheless; for example, when Zi is below the UHF RWP first reliable gate or when the boundary layer begins its transition to a stable nocturnal boundary layer.

Comparison of PBL Heights from Ceilometer Measurements and Greenhouse Gases Concentrations in São Paulo

This paper presents a study conducted in São Paulo, Brazil, where the planetary boundary layer height (PBLH) was determined using ceilometer data and the wavelet covariance transform method. The retrieved PBLH values were subsequently compared with the concentrations of CO2 and CH4 measured at three distinct experimental sites in the city. The period of study was July 2021. This study also included a comparison between ceilometer data and lidar data, which demonstrated the favorable applicability of the ceilometer data for PBLH estimation. An examination of the correlation between changes in average CO2 concentrations and PBLH values revealed stronger correlations for the IAG and UNICID stations, with correlation coefficients (ρ) of approximately −0.86 and −0.85, respectively, in contrast to the Pico do Jaraguá station, which exhibited a lower correlation coefficient of −0.42. When assessing changes in CH4 concentrations against variations in PBL height, the retrieved correlation coefficients were approximately −0.78 for IAG, −0.66 for UNICID, and −0.38 for Pico do Jaraguá. The results indicated that CO2/CH4 concentrations are negatively correlated with PBL heights, with CO2 concentrations showing more significant correlation than CH4. Additionally, among the three measurement stations, IAG measurements displayed the most substantial correlation. The results from this study contribute to the understanding of the relationship between PBLH and greenhouse gas concentrations, emphasizing the potential of remote sensing systems like ceilometers in monitoring and studying atmospheric processes.

Long-Range Transport Analysis Based on Eastern Atmospheric Circulation and Its Impact on the Dust Event over Moldavia, Romania in August 2022

During the second half of August 2022, a dust intrusion event occurred when dust that originated in the dry regions of the Kalmyk steppe (located in Russia, northeast of the Black Sea, north of Georgia, and northwest of the Caspian Sea) and the Precaspian plain was transported over the eastern region of Romania. The arid soil found in these areas can be attributed to an extended period of intense drought, with notable instances occurring in 2002, 2003, 2015, and 2018. This situation was further intensified by heatwaves experienced in May and June of 2022. The dust event was captured in MODIS images. In addition, smoke trains originating from fires in the north of the Azov were detected, but these did not reach Romania. Optical parameters from AERONET were used to confirm the dust event. To determine the trajectory of the particles, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used in this paper. The ensemble median model was used to highlight the presence and concentration of dust in the eastern part of Romania. Aerosols were detected between 0 and 4 km, according to radar and ceilometer data from the REXDAN cloud remote sensing facility in Galați, Romania. This dust intrusion event was the result of the dominant easterly circulation caused by the extension of the East European High to the northeast of the continent, which transported the dust towards the eastern part of Romania for more than 2 days. Moreover, the torrential rains between 22 and 24 August did not clear the atmosphere of dust, since the intense easterly circulation kept carrying the dust into the Moldavian area.

Systematic analysis of virga and its impact on surface particulate matter observations

Abstract. Studies focusing on virga are rare, even though it is a commonly occurring phenomenon. In this study, we investigated aerosol backscatter profiles from a ceilometer located on The University of Texas at El Paso (UTEP) campus from 2015–2021 to identify virga events. Ceilometer data effectively captured virga events from regular precipitation based on the backscattering intensities. To characterize the virga phenomena, a systematic method was developed using ceilometer profiles, soundings, surface rain gauges, and radar data from the nearest National Weather Service (NWS) site. A total of 50 virga events were identified during the study period. These events appeared only during a specific time of the year, revealing a seasonal occurrence pattern. We identified and classified these virga events and investigated their impact on the surface measurements recorded by the on-campus Continuous Ambient Air Monitoring Station (CAMS). Virga events were classified as columnar and non-columnar events based on their aerosol profiles. We observed that during some of the columnar virga events, surface particulate matter (PM) levels displayed a sudden upward trend indicating aerosol loading in the surface layer after precipitation evaporation. In total, 20 of the virga events showed a columnar structure out of the 50 identified in this study. More detailed analysis of selected events shows that virga affects regional air quality. A significant result of this study is that analysis of sudden changes in local air quality needs to consider the possible effects of virga on the surface layer.

Boundary Layer Height Characteristics in Mexico City from Two Remote Sensing Techniques

The Atmospheric Boundary Layer (ABL) height is a key parameter in air quality research as well as for numerical simulations and forecasts. The identification of thermally stable layers, often with radiosondes, has been a common approach for estimating ABL height, though with limited temporal coverage. Remote sensing techniques offer essentially continuous measurements. Nevertheless, ABL height retrievals from different methods can vary greatly when compared, which is particularly notable for topographically complex terrains, such as that surrounding Mexico City. This study, employing one year of data in Mexico City, reveals that the daytime convective boundary layer height (retrieved from Doppler lidar data) is typically lower than the aerosol layer height (retrieved from ceilometer data). Although both estimated heights evolved diurnally, the more elevated aerosol layer decays more slowly, suggesting that the mechanisms that elevate aerosols are not limited to convective motions. Additionally, both diurnal and seasonal variability are investigated, comparing remotely sensed-retrieved heights with thermally stable layers estimated from radiosonde data. Multiple stable layers often develop, those at higher levels have similar values to the ceilometer-retrieved heights, while stable layers at lower heights are similar to Doppler lidar height retrievals. The present research constitutes the first detailed analysis of ceilometer backscatter and Doppler lidar thresholding methods for estimating ABL height over Mexico City, and our results illustrate the complexity of mixing mechanisms on the ABL in this region of complex orography.

Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data

Abstract. Ceilometer measurements of aerosol backscatter profiles have been widely used to provide continuous planetary boundary layer height (PBLHT) estimations. To investigate the robustness of ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) ceilometer and balloon-borne sounding data at ARM fixed-location atmospheric observatories and from ARM mobile facilities deployed around the world for various field campaigns. These observatories cover from the tropics to the polar regions and over both ocean and land surfaces. Statistical comparisons of ceilometer-estimated PBLHTs from the Vaisala CL31 ceilometer data with radiosonde-estimated PBLHTs from the ARM PBLHT-SONDE Value-added Product (VAP) are performed under different atmospheric conditions including stable and unstable atmospheric boundary layer, low-level cloud-free conditions, and cloudy conditions at these ARM observatories. Under unstable conditions, good comparisons are found between ceilometer- and radiosonde-estimated PBLHTs at ARM low- and mid-latitude land observatories. However, it is still challenging to obtain reliable PBLHT estimations over ocean surfaces even using radiosonde data. Under stable conditions, ceilometer- and radiosonde-estimated PBLHTs have weak correlations. We compare different PBLHT estimations utilizing the Heffter, the Liu–Liang, and the bulk Richardson number methods applied to radiosonde data with ceilometer-estimated PBLHT. We find that ceilometer-estimated PBLHT compares better with the Liu–Liang method under unstable conditions and compares better with the bulk Richardson number method under stable conditions.

Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals

Abstract. Cloud and aerosol lidars measuring backscatter and depolarization ratio are the most suitable lidars to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water containing clouds (SLCC) based on ceilometers measuring only co-polarization backscatter. We utilize observations collected at Davis, Antarctica, where low-level, mixed-phase clouds, including supercooled liquid water (SLW) droplets and ice crystals, remain poorly understood due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018 to February 2019, with a variety of instruments including a depolarization lidar and a W-band cloud radar which were used to build a two-dimensional cloud phase mask distinguishing SLW and mixed-phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarization ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar–lidar approach with an accuracy as high as 0.91. This study provides a new SLCC retrieval approach based on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. Finally, the two algorithms were applied to a full year of ceilometer observations to retrieve cloud phase and frequency of occurrences of SLCC: SLCC was present 29 ± 6 % of the time for T19 and 24 ± 5 % of the time for G22-Davis over that annual cycle.

Boundary Layer via Multifractal Mass Conductivity through Remote Sensing Data in Atmospheric Dynamics

In this manuscript, multifractal theories of motion based on scale relativity theory are considered in the description of atmospheric dynamics. It is shown that these theories have the potential to highlight nondimensional mass conduction laws that describe the propagation of atmospheric entities. Then, using special operational procedures and harmonic mappings, these equations can be rewritten and simplified for their plotting and analysis to be performed. The inhomogeneity of these conduction phenomena is analyzed, and it is found that it can fluctuate and increase at certain fractal dimensions, leading to the conclusion that certain atmospheric structures and phenomena of either atmospheric transmission or stability can be explained by atmospheric fractal dimension inversions. Finally, this hypothesis is verified using ceilometer data throughout the atmospheric profiles.

Study of Planetary Boundary Layer, Air Pollution, Air Quality Models and Aerosol Transport Using Ceilometers in New South Wales (NSW), Australia

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.

Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques

Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques

Impact of assimilating lidar water vapour and temperature profiles with a hybrid ensemble transform Kalman filter: Three‐dimensional variational analysis on the convection‐permitting scale

AbstractWe discuss the analysis impact of the ensemble‐based assimilation of differential absorption lidar observed water vapour and Raman lidar observed temperature profiles into the Weather Research and Forecasting model at convection‐permitting scale. The impact of flow‐dependent background error covariance in the data assimilation (DA) system that uses the hybrid three‐dimensional variational (3DVAR) ensemble transform Kalman filter (ETKF) was compared to 3DVAR DA. The 3DVAR‐ETKF experiment resulted in a 50% lower temperature and water vapour RMSE than the 3DVAR experiment when taking the assimilated lidar data as reference and 26% (38%) lower water vapour (temperature) RMSE when comparing against independent radiosonde observations collocated with the lidar site. The planetary boundary‐layer height of the analyses compared to independent ceilometer data provided additional evidence of improvement. The 3DVAR analysis RMSE showed 140 m, whereas 3DVAR‐ETKF showed 60 m. Although limited to a single case study, we attribute these improvements to the flow‐dependent background error covariance matrix in the 3DVAR‐ETKF approach. The vertical profile measured from a single stationary lidar system established a spatial impact with a 100 km radius. This seems to indicate future assimilation of water vapour and temperature data from an operational lidar network. The assimilation impact persisted 7 hr into the forecast time compared with the ceilometer data and 4 hr with GPS observations.

Characteristics of Desert Precipitation in the UAE Derived from a Ceilometer Dataset

Understanding rainfall in arid and water-scarce regions is central to the efficient use of water resources in agriculture, irrigation, and domestic food security. This work presents a new dataset with which to study precipitation processes in arid regions, utilising two years (2018–2020) of ceilometer observations made at Al Ain International Airport in the desert region of Al Ain, United Arab Emirates (UAE), where the annual rainfall is 76 mm. Ceilometer data provide a novel method by which to study both the evolution of water droplets from the cloud base down to the surface and the local circumstances required for rain to successfully reach the surface. In this work, we explore how successful precipitation depends on the initial size of the droplets and the thermodynamic profile below the cloud. For 64 of the 105 rain events, the droplet diameters ranged from 0.60 to 3.75 mm, with a mean of 1.84 mm. We find that smaller droplets, higher cloud bases, reduced cloud depths, and colder cloud bases all act to prevent successful precipitation, instead yielding virga (28 out of the 105 rain generating events). We identify how these multiple regional factors combine—specifically, we identify clouds deeper than 2.9 km, droplet diameters greater than 2 mm, and a midpoint below-cloud RH profile greater than 50%—to give successful rainfall, which may ultimately lead to more efficient rainfall enhancing measures, such as cloud seeding.