A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes

Ying-chao, Zhang; Chen, Su; Chen, Siying; Chen, He; Guo, Pan

doi:10.5194/amt-13-6675-2020

Cited by 10 publications

(5 citation statements)

References 69 publications

(83 reference statements)

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“…In the present study, the reasons for variations in the SBL and ABL top, as seen in Figure 7, might be due to unstable conditions along with interlayer mixing due to complex dynamics of terrain and the corresponding wind pattern. The presence of low cloud, a frequent occurrence at the present site, may induce error in the extraction of boundary layer heights [153][154][155].…”

Section: Atmospheric Boundary Layermentioning

confidence: 92%

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Profiling of Aerosols and Clouds over High Altitude Urban Atmosphere in Eastern Himalaya: A Ground-Based Observation Using Raman LIDAR

et al. 2023

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Profiles of aerosols and cloud layers have been investigated over a high-altitude urban atmosphere in the eastern Himalayas in India, for the first time, using a Raman LIDAR. The study was conducted post-monsoon season over Darjeeling (latitude 27°01′ N longitude 88°36′ E, 2200 masl), a tourist destination in north-eastern India. In addition to the aerosols and cloud characterization and atmospheric boundary layer detection, the profile of the water vapor mixing ratio has also been analyzed. Effects of atmospheric dynamics have been studied using the vertical profiles of the normalized standard deviation of RCS along with the water vapor mixing ratio. The aerosol optical characteristics below and above the Atmospheric Boundary Layer (ABL) region were studied separately, along with the interrelation of their optical and microphysical properties with synoptic meteorological parameters. The backscatter coefficient and the extinction coefficient were found in the range from 7.15×10−10 m−1 sr−1 to 3.01×10−5 m−1 sr−1 and from 1.02×10−5 m−1 to 2.28×10−3 m−1, respectively. The LIDAR ratio varies between 3.9 to 78.39 sr over all altitudes. The variation of the linear depolarization ratio from 0.19 to 0.32 indicates the dominance, of non-spherical particles. The periodicity observed in different parameters may be indicative of atmospheric wave phenomena. Cloud parameters, such as scattering coefficients, top and bottom height, and optical depth for different cloud phases, have been evaluated. A co-located Micro Rain Radar has been used with LIDAR for cloud life cycle study.

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Section: Atmospheric Boundary Layermentioning

confidence: 92%

“…Each dimension corresponds to a condition (or a variable) for grouping the objects. The choice of these dimension or variables would depend on the system under consideration [65,153].…”

Section: Appendix a Interactions Of Optical Radiation With Atmospheri...mentioning

confidence: 99%

Profiling of Aerosols and Clouds over High Altitude Urban Atmosphere in Eastern Himalaya: A Ground-Based Observation Using Raman LIDAR

et al. 2023

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“…Gradient-based algorithms (Yang et al, 2017;Flamant et al, 1997;Summa et al, 2013;He et al, 2006) and wavelet transform algorithms (Davis et al, 2000;Baars et al, 2008;Brooks, 2003) are two main categories of lidar algorithms. The combination of different algorithms (Zhang et al, 2020;Sawyer and Li, 2013;Zhang et al, 2022), the introduction of image processing (Vivone et al, 2021;Lewis et al, 2013), the extended-Kalman filtering technique (Kokkalis et al, 2020), and the consideration of stability (Su et al, 2020) are all new directions in the evolution of lidar algorithms. Radar wind profilers are another active remote sensor to obtain PBLHs.…”

Section: Introductionmentioning

confidence: 99%

An ensemble method for improving the estimation of planetary boundary layer height from radiosonde data

Chen,

Yang,

Wang

et al. 2023

Atmos. Meas. Tech.

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Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for weather, climate, and air quality models. Radiosonde is one of the most commonly used instruments for PBLH determination and is generally accepted as a standard for other methods. However, mainstream approaches for the estimation of PBLH from radiosonde present some uncertainties and even show disadvantages under some circumstances, and the results need to be visually verified, especially during the transition period of different PBL regimes. To avoid the limitations of individual methods and provide a benchmark estimation of PBLH, we propose an ensemble method based on high-resolution radiosonde data collected in Beijing in 2017. Seven existing methods including four gradient-based methods are combined along with statistical modification. The ensemble method is verified at 08:00, 14:00, and 20:00 Beijing time (BJT = UTC+8), respectively. The overestimation of PBLH can be effectively eliminated by setting thresholds for gradient-based methods, and the inconsistency between individual methods can be reduced by clustering. Based on the statistics of a 1-year observational analysis, the effectiveness (E) of the ensemble method reaches up to 62.6 %, an increase of 6.5 %–53.0 % compared to the existing methods. Nevertheless, the ensemble method suffers to some extent from uncertainties caused by the consistent overestimation of PBLH, the profiles with a multi-layer structure, and the intermittent turbulence in the stable boundary layer (SBL). Finally, this method has been applied to characterize the diurnal and seasonal variations of different PBL regimes. Particularly, the average convective boundary layer (CBL) height is found to be the highest in summer, and the SBL is lowest in summer with about 200 m. The average PBLH at the transition stage lies around 1100 m except in winter. These findings imply that the ensemble method is reliable and effective.

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“…It evolves throughout the day and is season influenced by solar radiation and other factors. Therefore, the atmospheric boundary layer height (ABLH) is a crucial parameter in numerical weather prediction models, climate change studies and air quality assessments [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning

Chang

Liu

et al. 2022

Remote Sensing

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Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions.

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A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes

Cited by 10 publications

References 69 publications

Profiling of Aerosols and Clouds over High Altitude Urban Atmosphere in Eastern Himalaya: A Ground-Based Observation Using Raman LIDAR

Profiling of Aerosols and Clouds over High Altitude Urban Atmosphere in Eastern Himalaya: A Ground-Based Observation Using Raman LIDAR

An ensemble method for improving the estimation of planetary boundary layer height from radiosonde data

Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning

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