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The planetary boundary layer height is a very important parameter in the atmosphere because it determines the range where the most effective dispersion processes take place, and it serves as a medium for the vertical transport of heat, moisture, and pollutants. The accurate estimation of boundary layer height (BLH) is vital for air pollution prediction. In this paper, the BLH estimated by AD-Net was compared with that from the ECMWFs over East Asia from September 2015 to August 2018. A continuous 24 h BLH estimation from AD-Net generally matched with the aerosol vertical structures. Diurnal and seasonal variation and spatial variation of BLH can also be shown, suggesting the good performance of AD-Net BLH. The comparison of seasonal mean BLH between AD-Net and ECMWFs was conducted at 20 lidar sites. On average, there was an underestimation of the ECMWFs, mostly in summer and winter. A significant disagreement between AD-Net and the ECMWFs was noted, especially over coastal areas and mountain areas. In order to investigate the difference between them, two BLHs were compared under different land cover types and climate conditions. In general, the BLH of the ECMWFs was less than that of AD-Net over most of the land cover types in summer and winter. The smallest differences (0.26 km) existed over water surfaces in winter compared with AD-Net, and the largest underestimation (1.42 km) occurred over grassland surfaces in summer. Similarly, all the BLHs of the ECMWFs were lesser than those of AD-Net under different climatological conditions in summer and winter. The mean difference between AD-Net BLH and ECMWFs BLH was 1.05, 0.71, and 0.48 km for arid regions, semi-arid and semi-wet regions, and wet regions, respectively. The largest underestimation occurred over arid regions in winter, with a value of 1.42 km. The smallest underestimation occurred over wet regions, with a value of 0.27 km. The present research provides better insight into the BLH performance in the ECMWFs reanalysis data. The new continuous PBL dataset can be used to improve the model parameterization of PBL and our understanding of the atmospheric transport of pollutants which affect air quality and human health. [ABSTRACT FROM AUTHOR]

The planetary boundary layer height is a very important parameter in the atmosphere because it determines the range where the most effective dispersion processes take place, and it serves as a medium for the vertical transport of heat, moisture, and pollutants. The accurate estimation of boundary layer height (BLH) is vital for air pollution prediction. In this paper, the BLH estimated by AD-Net was compared with that from the ECMWFs over East Asia from September 2015 to August 2018. A continuous 24 h BLH estimation from AD-Net generally matched with the aerosol vertical structures. Diurnal and seasonal variation and spatial variation of BLH can also be shown, suggesting the good performance of AD-Net BLH. The comparison of seasonal mean BLH between AD-Net and ECMWFs was conducted at 20 lidar sites. On average, there was an underestimation of the ECMWFs, mostly in summer and winter. A significant disagreement between AD-Net and the ECMWFs was noted, especially over coastal areas and mountain areas. In order to investigate the difference between them, two BLHs were compared under different land cover types and climate conditions. In general, the BLH of the ECMWFs was less than that of AD-Net over most of the land cover types in summer and winter. The smallest differences (0.26 km) existed over water surfaces in winter compared with AD-Net, and the largest underestimation (1.42 km) occurred over grassland surfaces in summer. Similarly, all the BLHs of the ECMWFs were lesser than those of AD-Net under different climatological conditions in summer and winter. The mean difference between AD-Net BLH and ECMWFs BLH was 1.05, 0.71, and 0.48 km for arid regions, semi-arid and semi-wet regions, and wet regions, respectively. The largest underestimation occurred over arid regions in winter, with a value of 1.42 km. The smallest underestimation occurred over wet regions, with a value of 0.27 km. The present research provides better insight into the BLH performance in the ECMWFs reanalysis data. The new continuous PBL dataset can be used to improve the model parameterization of PBL and our understanding of the atmospheric transport of pollutants which affect air quality and human health.

The top of the boundary layer, referred to as the planetary boundary layer height (BLH), is an important physical parameter in atmospheric numerical models, which has a critical role in atmospheric simulation, air pollution prevention, and climate prediction. The traditional methods for determining BLHs using Doppler lidar vertical velocity variance (σw2) can be classified into the variance and peak methods, which depend on atmospheric conditions due to their use of a single threshold, hence limiting their ability to estimate diurnal BLHs. Edge detection (ED) was later introduced in BLH estimation due to its ability to identify the 2D gradient of an image. A key step in ED is automatically identifying the edge of BLHs based on the peaks of the profile, hence avoiding the influence of extreme atmospheric conditions. Two cases in the diurnal cycle on 4 March 2019 and 8 July 2019 reveal that ED outperforms both the variance and peak methods in nighttime and extreme atmospheric conditions. The retrieved BLHs from 2018 to 2020 were compared with radiosonde (RS) measurements for the same time at the neutral, stable, and convective boundary layers. The correlation coefficient (R: 0.4 vs. 0.05, 0.14; 0.26 vs. −0.10, −0.16; 0.35 vs. 0.01, 0.16) and root mean square error (RMSE (km): 0.58 vs. 0.82, 0.90; 0.37 vs. 1.01, 0.50; 0.66 vs. 0.98, 0.82) obtained by the ED method were higher and lower than those obtained by the variance and peak methods, respectively. The mean absolute error (MAE) of the ED method under the NBL, SBL, and CBL conditions are lower than the variance and peak methods (MAE (km): 0.44, 0.14, 0.50 vs. 0.62, 0.34, 0.64; 0.59, 0.75, 0.74), respectively. The mean relative error (MRE) of the ED method is lower than the variance and peak methods under the NBL condition (MRE: −8.88% vs. −18.39%, 13.91%). Under the SBL, the MRE of the ED method is lower than the variance method and higher than the peak method (−38.64%, vs. −152.23%; 14.02%). Under the CBL, the MRE of the ED method is lower than the variance method and higher than the peak method (−15.07% vs. 2.24%; 5.64%). In addition, the comparison between ED and wavelet covariance transform (WCT) method and RS measurements showed that the ED method has a similar performance with the WCT method and is even better. In the long-term analysis, the hourly and monthly BLHs in the diurnal and annual cycles, respectively, as obtained by ED, were highly consistent with the RS measurements and obtained the lowest standard error. In the annual cycle, the retrieved BLHs in summer and autumn were higher than those retrieved in spring and winter.

High-precision and -resolution atmospheric boundary layer height (BLH) has received increasing attention in air pollution research in recent years. The low time resolution of sounding data is the main challenge to validate BLH retrieval from lidar observations. To resolve this issue, we conducted simultaneous tethered balloon sounding and lidar observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) during winter 2019–2020. The BLHs derived from the tethered balloon sounding data were 170, 210, 393, 676, 423, and 190 m at 02:00, 08:00, 11:00, 14:00, 17:00, and 20:00 (Beijing time), respectively. The diurnal evolution of BLH was reasonably captured by lidar observation-based wavelet covariance transform and ideal profile fitting methods, which exhibited correlation coefficients of 0.91 and 0.89, respectively, with the BLHs determined from tethered balloon sounding data. The lidar results slightly overestimated the BLHs, though all results were acceptable when considering both the absolute and relative errors with respect to BLHs from the tethered balloon data. Our results revealed high-precision and -resolution diurnal variations in BLH at SACOL in Northwest China and suggest the importance of validating lidar-based BLHs using simultaneous sounding data.

The planetary boundary layer height (PBLH) is the atmospheric region closest to the earth’s surface and has important implications on weather forecasting, air quality, and climate research. However, lidar-based methods traditionally used to determine PBLH—such as the ideal profile fitting method (IPF), maximum gradient method, and wavelet covariance transform—are not only heavily influenced by cloud layers, but also rely heavily on a low signal-to-noise ratio (SNR). Therefore, a random sample fitting (RANSAF) method was proposed for PBLH detection based on combining the random sampling consensus and IPF methods. According to radiosonde measurements, the testing of simulated and satellite-based signals shows that the proposed RANSAF method can reduce the effects of the cloud layer and significantly fluctuating noise on lidar-based PBLH detection better than traditional algorithms. The low PBLH bias derived by the RANSAF method indicates that the improved algorithm has a superior performance in measuring PBLH under a low SNR or when a cloud layer exists where the traditional methods are mostly ineffective. The RANSAF method has the potential to determine regional PBLH on the basis of satellite-based lidar backscatter profiles.

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.

Atmospheric boundary layer height is one of the most important parameters in atmospheric dispersion modelling because it has a large effect on predicted air quality. Comparisons between Atmospheric Dispersion Modelling System, version 4 (ADMS 4) and lidar data were carried out on boundary layer height data from central London. The comparison showed that the boundary layer height predicted by the ADMS 4 was, on average, lower than lidar for the subset of data taken. ADMS 4 has a very simple surface scheme which is not representative of complex urban environments and the results from this research imply that there is not sufficient surface roughness within the model to produce a large enough boundary layer height. The aim of this study is to create an improved model to better forecast the growth of the daytime urban boundary layer and predict boundary layer height, h , in an air quality dispersion model using lidar measurements. The combined model was developed by using a surface model and an atmospheric boundary layer height model. Measurements of atmospheric boundary layer height by lidar used vertical velocity variance and the overall conclusion was that the combined model improved the performance of ADMS in urban areas. [ABSTRACT FROM AUTHOR]

We detected a severe haze process in Guangzhou area with lidar and microwave radiometer, performed an inversion to get boundary layer height by wavelet covariance transform, and analyzed the correlation between meteorological factors of boundary layer and visibility from the perspective of dynamical and thermodynamic structures. Our results indicate that the boundary layer height shows significant daily changes, consistent with ground visibility variation. During the cleaning process, the boundary layer height exceeded 1 km; during severe haze, the height was only 500 m. Temperature gradient of 50-100 m, which was 30 h lag, was remarkably correlated with visibility, with the correlation coefficient of 0.77. High layer visibility (255 m) and low layer stability were significantly anticorrelation, and the maximum anticorrelation coefficient was up to −0.76 in cleaning days and −0.49 in haze days. In the related boundary layer meteorological factors, surface ventilation coefficient was linearly correlated with ground visibility, with the greatest correlation coefficient of 0.88. The correlation coefficients of boundary layer height, ground wind velocity, relative humidity and ground visibility were 0.76, 0.67, and −0.77, respectively. There was a strong correlation between different meteorological factors. The dominant meteorological factor during this haze process was surface ventilation coefficient. In the area without boundary layer height sounding, ground visibility and wind velocity could be used to estimate boundary layer height. [ABSTRACT FROM AUTHOR]

The top of the boundary layer, referred to as the planetary boundary layer height (BLH), is an important physical parameter in atmospheric numerical models, which has a critical role in atmospheric simulation, air pollution prevention, and climate prediction. The traditional methods for determining BLHs using Doppler lidar vertical velocity variance ( σ w 2 ) can be classified into the variance and peak methods, which depend on atmospheric conditions due to their use of a single threshold, hence limiting their ability to estimate diurnal BLHs. Edge detection (ED) was later introduced in BLH estimation due to its ability to identify the 2D gradient of an image. A key step in ED is automatically identifying the edge of BLHs based on the peaks of the profile, hence avoiding the influence of extreme atmospheric conditions. Two cases in the diurnal cycle on 4 March 2019 and 8 July 2019 reveal that ED outperforms both the variance and peak methods in nighttime and extreme atmospheric conditions. The retrieved BLHs from 2018 to 2020 were compared with radiosonde (RS) measurements for the same time at the neutral, stable, and convective boundary layers. The correlation coefficient (R: 0.4 vs. 0.05, 0.14; 0.26 vs. −0.10, −0.16; 0.35 vs. 0.01, 0.16) and root mean square error (RMSE (km): 0.58 vs. 0.82, 0.90; 0.37 vs. 1.01, 0.50; 0.66 vs. 0.98, 0.82) obtained by the ED method were higher and lower than those obtained by the variance and peak methods, respectively. The mean absolute error (MAE) of the ED method under the NBL, SBL, and CBL conditions are lower than the variance and peak methods (MAE (km): 0.44, 0.14, 0.50 vs. 0.62, 0.34, 0.64; 0.59, 0.75, 0.74), respectively. The mean relative error (MRE) of the ED method is lower than the variance and peak methods under the NBL condition (MRE: −8.88% vs. −18.39%, 13.91%). Under the SBL, the MRE of the ED method is lower than the variance method and higher than the peak method (−38.64%, vs. −152.23%; 14.02%). Under the CBL, the MRE of the ED method is lower than the variance method and higher than the peak method (−15.07% vs. 2.24%; 5.64%). In addition, the comparison between ED and wavelet covariance transform (WCT) method and RS measurements showed that the ED method has a similar performance with the WCT method and is even better. In the long-term analysis, the hourly and monthly BLHs in the diurnal and annual cycles, respectively, as obtained by ED, were highly consistent with the RS measurements and obtained the lowest standard error. In the annual cycle, the retrieved BLHs in summer and autumn were higher than those retrieved in spring and winter. [ABSTRACT FROM AUTHOR]

High-precision and -resolution atmospheric boundary layer height (BLH) has received increasing attention in air pollution research in recent years. The low time resolution of sounding data is the main challenge to validate BLH retrieval from lidar observations. To resolve this issue, we conducted simultaneous tethered balloon sounding and lidar observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) during winter 2019–2020. The BLHs derived from the tethered balloon sounding data were 170, 210, 393, 676, 423, and 190 m at 02:00, 08:00, 11:00, 14:00, 17:00, and 20:00 (Beijing time), respectively. The diurnal evolution of BLH was reasonably captured by lidar observation-based wavelet covariance transform and ideal profile fitting methods, which exhibited correlation coefficients of 0.91 and 0.89, respectively, with the BLHs determined from tethered balloon sounding data. The lidar results slightly overestimated the BLHs, though all results were acceptable when considering both the absolute and relative errors with respect to BLHs from the tethered balloon data. Our results revealed high-precision and -resolution diurnal variations in BLH at SACOL in Northwest China and suggest the importance of validating lidar-based BLHs using simultaneous sounding data. [ABSTRACT FROM AUTHOR]

The planetary boundary layer height (PBLH) is the atmospheric region closest to the earth's surface and has important implications on weather forecasting, air quality, and climate research. However, lidar-based methods traditionally used to determine PBLH—such as the ideal profile fitting method (IPF), maximum gradient method, and wavelet covariance transform—are not only heavily influenced by cloud layers, but also rely heavily on a low signal-to-noise ratio (SNR). Therefore, a random sample fitting (RANSAF) method was proposed for PBLH detection based on combining the random sampling consensus and IPF methods. According to radiosonde measurements, the testing of simulated and satellite-based signals shows that the proposed RANSAF method can reduce the effects of the cloud layer and significantly fluctuating noise on lidar-based PBLH detection better than traditional algorithms. The low PBLH bias derived by the RANSAF method indicates that the improved algorithm has a superior performance in measuring PBLH under a low SNR or when a cloud layer exists where the traditional methods are mostly ineffective. The RANSAF method has the potential to determine regional PBLH on the basis of satellite-based lidar backscatter profiles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]