Abstract
Although coherent Doppler wind lidar (CDWL) is promising in detecting boundary layer height (BLH), differences between BLH results are observed when different CDWL measurements are used as tracers. Here, a robust solution for BLH detections with CDWL is proposed and demonstrated: mixed layer height (MLH) is retrieved best from turbulent kinetic energy dissipation rate (TKEDR), while stable boundary layer height (SBLH) and residual layer height (RLH) can be retrieved from carrier-to-noise ratio (CNR). To study the cause of the BLH differences, an intercomparison experiment is designed with two identical CDWLs, where only one is equipped with a stability control subsystem. During the experiment, it is found that the CNR could be distorted by instrument instability because the coupling efficiency from free-space to the polarization-maintaining fiber of the telescope is sensitive to the surrounding environment. In the ML, a bias up to 2.13 km of the MLH from CNR is found, which is caused by the CNR deviation. In contrast, the MLH from TKEDR is robust as long as the accuracy of wind is guaranteed. In the SBL (RL), the CNR is found capable to retrieve SBLH and RLH simultaneously and robustly. This solution is tested during an observation period over one month. Statistical analysis shows that the root-mean-square errors (RMSE) in the MLH, SBLH, and RLH are 0.28 km, 0.23 km, and 0.24 km, respectively.
摘 要
尽管相干多普勒测风激光雷达在边界层高度探测方面有很好的应用前景, 但当采用不同的观测数据时, 边界层高度的探测结果存在差异. 本文提出了一种基于相干多普勒测风激光雷达探测边界层高度的稳健方法: 用湍流动能耗散率确定混合层高度, 用载噪比确定稳定边界层高度和残留层高度. 为了研究边界层探测高度差异的成因, 本文采用了两台相同机制的雷达作对比, 并为其中一台配备了温控装置. 实验发现, 由于自由空间到望远镜保偏光纤的耦合效率对环境敏感, 仪器不稳定性将导致载噪比误差, 进而导致基于载噪比确定的混合层高度出现高达 2.13km 的偏移. 相比之下, 只要风场观测数据是准确的, 基于湍流动能耗散率就可以稳健地确定混合层高度. 实验还发现载噪比可以稳健地确定稳定边界层高度和残留层高度. 为了验证该方法的有效性, 本文进行了为期一个月的实验, 统计结果表明: 混合层高度, 稳定边界层高度和残留层高度的均方根误差分别为 0.28km, 0.23km 和 0.24km.
Similar content being viewed by others
References
Banakh, V. A., A. Brewer, E. L. Pichugina, and I. N. Smalikho, 2010: Measurements of wind velocity and direction with coherent Doppler lidar in conditions of a weak echo signal. Atmospheric and Oceanic Optics, 23, 381–388, https://doi.org/10.1134/s1024856010050076.
Banakh, V. A., I. N. Smalikho, and A. V. Falits, 2017: Estimation of the turbulence energy dissipation rate in the atmospheric boundary layer from measurements of the radial wind velocity by micropulse coherent Doppler lidar. Optics Express, 25, 22 679–22 692, https://doi.org/10.1364/oe.25.022679.
Banakh, V. A., I. N. Smalikho, and A. V. Falits, 2020: Estimation of the height of turbulent mixing layer from data of Doppler lidar measurements using conical scanning by a probe beam. Atmospheric Measurement Techniques Discussions, https://doi.org/10.5194/amt-2020-259.
Brooks, I. M., 2003: Finding boundary layer top: Application of a wavelet covariance transform to lidar backscatter profiles. J. Atmos. Oceanic Technol., 20, 1092–1105, https://doi.org/10.1175/1520-0426(2003)020<1092:fbltao>2.0.co;2.
Chambers, D. M., 1997: Modeling heterodyne efficiency for coherent laser radar in the presence of aberrations. Optics Express, 1, 60–67, https://doi.org/10.1364/oe.E000060.
Coen, M. C., C. Praz, A. Haefele, D. Ruffieux, P. Kaufmann, and B. Calpini, 2014: Determination and climatology of the planetary boundary layer height above the Swiss plateau by in situ and remote sensing measurements as well as by the COSMO-2 model. Atmospheric Chemistry and Physics, 14, 13 205–13 221, https://doi.org/10.5194/acp-14-13205-2014.
Cohn, S. A., and W. M. Angevine, 2000: Boundary layer height and entrainment zone thickness measured by lidars and wind-profiling radars. J. Appl. Meteorol. Climatol., 39, 1233–1247, https://doi.org/10.1175/1520-0450(2000)039<1233:blhaez>2.0.co;2.
Emeis, S., C. Jahn, C. Münkel, C. Münsterer, and K. Schäfer, 2007: Multiple atmospheric layering and mixing-layer height in the Inn valley observed by remote sensing. Meteor. Z., 16, 415–424, https://doi.org/10.1127/0941-2948/2007/0203.
Emeis, S., K. Schäfer, and C. Münkel, 2008: Surface-based remote sensing of the mixing-layer height-A review. Meteor. Z., 17, 621–630, https://doi.org/10.1127/0941-2948/2008/0312.
Flamant, C., J. Pelon, P. H. Flamant, and P. Durand, 1997: Lidar determination of the entrainment zone thickness at the top of the unstable marine atmospheric boundary layer. Bound. — Layer Meteorol., 83, 247–284, https://doi.org/10.1023/a:1000258318944.
Guo, J. P., and Coauthors, 2016: The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data. Atmospheric Chemistry and Physics, 16, 13 309–13 319, https://doi.org/10.5194/acp-16-13309-2016.
Hooper, W. P., and E. W. Eloranta, 1986: Lidar measurements of wind in the planetary boundary layer: The method, accuracy and results from joint measurements with radiosonde and kytoon. J. Appl. Meteorol. Climatol., 25, 990–1001, https://doi.org/10.1175/1520-0450(1986)025<0990:lmowit>2.0.co;2.
Huang, M., and Coauthors, 2017: Estimate of boundary-layer depth over Beijing, China, using Doppler lidar data during SURF-2015. Bound. -Layer Meteorol., 162, 503–522, https://doi.org/10.1007/s10546-016-0205-2.
Kaimal, J. C., and J. J. Finnigan, 1994: Atmospheric Boundary Layer Flows: Their Structure and Measurement. Oxford University Press, 7–9.
Leung, M. Y. T., W. Zhou, C. M. Shun, and P. W. Chan, 2018: Large-scale circulation control of the occurrence of low-level turbulence at Hong Kong international airport. Adv. Atmos. Sci., 35, 435–444, https://doi.org/10.1007/s00376-017-7118-y.
Lewis, J. R., E. J. Welton, A. M. Molod, and E. Joseph, 2013: Improved boundary layer depth retrievals from MPLNET. J. Geophys. Res., 118, 9870–9879, https://doi.org/10.1002/jgrd.50570.
Li, H., Y. Yang, X. M. Hu, Z. W. Huang, G. Y. Wang, B. D. Zhang, and T. J. Zhang, 2017: Evaluation of retrieval methods of daytime convective boundary layer height based on lidar data. J. Geophys. Res., 122, 4578–4593, https://doi.org/10.1002/2016jd025620.
Li, Z. Q., and Coauthors, 2016: Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements. Remote Sensing of Environment, 173, 59–68, https://doi.org/10.1016/j.rse.2015.11.019.
Luo, T., Z. E. Wang, D. M. Zhang, and B. Chen, 2016: Marine boundary layer structure as observed by A-train satellites. Atmospheric Chemistry and Physics, 16, 5891–5903, https://doi.org/10.5194/acp-16-5891-2016.
Manninen, A., T. Marke, M. Tuononen, and E. J. O’Connor, 2018: Atmospheric boundary layer classification with Doppler lidar. J. Geophys. Res., 123, 8172–8189, https://doi.org/10.1029/2017jd028169.
Melfi, S. H., J. D. Spinhirne, S. H. Chou, and S. P. Palm, 1985: Lidar observations of vertically organized convection in the planetary boundary layer over the ocean. J. Appl. Meteorol. Climatol., 24, 806–821, https://doi.org/10.1175/1520-0450(1985)024<0806:loovoc>2.0.co;2.
O’Connor, E. J., A. J. Illingworth, I. M. Brooks, C. D. Westbrook, R. J. Hogan, F. Davies, and B. J. Brooks, 2010: A method for estimating the turbulent kinetic energy dissipation rate from a vertically pointing Doppler lidar, and independent evaluation from balloon-borne in situ measurements. J. Atmos. Oceanic Technol., 27, 1652–1664, https://doi.org/10.1175/2010jtecha1455.1.
Peña, A., S. E. Gryning, and A. N. Hahmann, 2013: Observations of the atmospheric boundary layer height under marine upstream flow conditions at a coastal site. J. Geophys. Res., 118, 1924–1940, https://doi.org/10.1002/jgrd.50175.
Sathe, A., and J. Mann, 2013: A review of turbulence measurements using ground-based wind lidars. Atmospheric Measurement Techniques, 6, 3147–3167, https://doi.org/10.5194/amt-6-3147-2013.
Schween, J. H., A. Hirsikko, U. Löhnert, and S. Crewell, 2014: Mixing-layer height retrieval with ceilometer and Doppler lidar: From case studies to long-term assessment. Atmospheric Measurement Techniques, 7, 3685–3704, https://doi.org/10.5194/amt-7-3685-2014.
Seibert, P., F. Beyrich, S.-E. Gryning, S. Joffre, A. Rasmussen, and P. Tercier, 2000: Review and intercomparison of operational methods for the determination of the mixing height. Atmos. Environ., 34, 1001–1027, https://doi.org/10.1016/s1352-2310(99)00349-0.
Seidel, D. J., C. O. Ao, and K. Li, 2010: Estimating climatological planetary boundary layer heights from radiosonde observations: Comparison of methods and uncertainty analysis. J. Geophys. Res., 115, D16113, https://doi.org/10.1029/2009jd013680.
Smalikho, I. N., and V. A. Banakh, 2017: Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer. Atmospheric Measurement Techniques, 10, 4191–4208, https://doi.org/10.5194/amt-10-4191-2017.
Steyn, D. G., M. Baldi, and R. M. Hoff, 1999: The detection of mixed layer depth and entrainment zone thickness from lidar backscatter profiles. J. Atmos. Oceanic Technol., 16, 953–959, https://doi.org/10.1175/1520-0426(1999)016<0953:tdomld>2.0.co;2.
Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, 9–16.
Su, T. N., Z. Q. Li, and R. Kahn, 2020: A new method to retrieve the diurnal variability of planetary boundary layer height from lidar under different thermodynamic stability conditions. Remote Sens. Environ., 237, 111519, https://doi.org/10.1016/j.rse.2019.111519.
Vakkari, V., E. J. O’Connor, A. Nisantzi, R. E. Mamouri, and D. G. Hadjimitsis, 2015: Low-level mixing height detection in coastal locations with a scanning Doppler lidar. Atmospheric Measurement Techniques, 8, 1875–1885, https://doi.org/10.5194/amt-8-1875-2015.
Wang, C., and Coauthors, 2017: 1.5 µm polarization coherent lidar incorporating time-division multiplexing. Optics Express, 25, 20 663–20 674, https://doi.org/10.1364/oe.25.020663.
Wang, C., H. Y. Xia, Y. P. Liu, S. F. Lin, and X. K. Dou, 2018: Spatial resolution enhancement of coherent Doppler wind lidar using joint time-frequency analysis. Optics Communications, 424, 48–53, https://doi.org/10.1016/j.optcom.2018.04.042.
Wang, C., and Coauthors, 2019: Relationship analysis of PM2.5 and boundary layer height using an aerosol and turbulence detection lidar. Atmospheric Measurement Techniques, 12, 3303–3315, https://doi.org/10.5194/amt-12-3303-2019.
Wei, T. W., and Coauthors, 2019: Simultaneous wind and rainfall detection by power spectrum analysis using a VAD scanning coherent Doppler lidar. Optics Express, 27, 31 235–31 245, https://doi.org/10.1364/oe.27.031235.
Yang, Y. J., and Coauthors, 2020: Diurnal evolution of the wintertime boundary layer in urban Beijing, China: Insights from doppler lidar and a 325-m meteorological tower. Remote Sensing, 12, 3935, https://doi.org/10.3390/rs12233935.
Yuan, J. L., H. Y. Xia, T. W. Wei, L. Wang, B. Yue, and Y. B. Wu, 2020: Identifying cloud, precipitation, windshear, and turbulence by deep analysis of the power spectrum of coherent Doppler wind lidar. Optics Express, 28, 37 406–37 418, https://doi.org/10.1364/oe.412809.
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights
• The CNR could be distorted by instrument instability, which would result in a deviation.
• Although there is a CNR deviation, the TKEDR measured by CDWL is accurate as long as the accuracy of wind is guaranteed.
• The RMSE of the MLH from CNR is 1.08 km, and it is reduced to 0.28 km when TKEDR is used.
• The principal BLH features, including MLH, SBLH, and RLH, are robustly detected by CDWL during this observation.
Rights and permissions
About this article
Cite this article
Wang, L., Qiang, W., Xia, H. et al. Robust Solution for Boundary Layer Height Detections with Coherent Doppler Wind Lidar. Adv. Atmos. Sci. 38, 1920–1928 (2021). https://doi.org/10.1007/s00376-021-1068-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-021-1068-0
Key words
- boundary layer height
- coherent Doppler wind lidar
- carrier-to-noise ratio
- turbulent kinetic energy dissipation rate