Preprints
https://doi.org/10.5194/amt-2021-288
https://doi.org/10.5194/amt-2021-288
18 Nov 2021
 | 18 Nov 2021
Status: this preprint was under review for the journal AMT but the revision was not accepted.

Turbulence Detection in the Atmospheric Boundary Layer using Coherent Doppler Wind Lidar and Microwave Radiometer

Pu Jiang, Jinlong Yuan, Kenan Wu, Lu Wang, and Haiyun Xia

Abstract. The refractive index structure constant (Cn2) is a key parameter in describing the influence of turbulence on laser transmission in the atmosphere. A new method for continuous Cn2 profiling with both high temporal and spatial resolution is proposed and demonstrated. Under the assumption of the Kolmogorov “2/3 law”, the Cn2 profile can be calculated by using the wind field and turbulent kinetic energy dissipation rate (TKEDR) measured by coherent Doppler wind lidar (CDWL) and other meteorological parameters derived from microwave radiometer (MWR). In the horizontal experiment, a comparison between the results from our new method and measurements made by a large aperture scintillometer (LAS) is conducted. Except for the period of stratification stabilizing, the correlation coefficient between them in the six-day observation is 0.8389, the mean error and standard deviation is 1.09 × 10−15 m−2/3 and 2.14 × 10−15 m−2/3, respectively. In the vertical direction, the continuous observation results of Cn2 and other turbulence parameter profiles in the atmospheric boundary layer (ABL) are retrieved. More details of the atmospheric turbulence can be found in the ABL owe to the high temporal and spatial resolution of MWR and CDWL (spatial resolution of 26 m, temporal resolution of 147 s).

Pu Jiang et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-288', Anonymous Referee #1, 12 Dec 2021
    • AC1: 'Reply on RC1', Haiyun Xia, 30 Jan 2022
  • RC2: 'Comment on amt-2021-288', Anonymous Referee #2, 13 Dec 2021
    • AC2: 'Reply on RC2', Haiyun Xia, 30 Jan 2022