24 Mar 2023
 | 24 Mar 2023

Suppression of precipitation bias on wind velocity from continuous-wave Doppler lidars

Liqin Jin, Jakob Mann, Nikolas Angelou, and Mikael Sjöholm

Abstract. In moderate to heavy precipitation, rain droplets may deteriorate Doppler lidars' accuracy for measuring the line-of-sight wind velocity because their projected velocity on the beam direction differs greatly from that of air. Therefore, we propose a method of effectively filtering away the adverse effects of rain on velocity estimation by sampling the Doppler spectra faster than the rain drops' beam transit time. By using a special averaging procedure, we can suppress the rain signal by sampling the spectrum at 3 kHz. On a moderately rainy day with a maximum rain intensity of 4 mm/h, three ground-based continuous-wave Doppler lidars were used to conduct a field measurement campaign at the Risø campus of the Technical University of Denmark. We demonstrate that the rain bias can effectively be removed by normalizing the noise-flattened Doppler spectra with their peak values before they are averaged down to 50 Hz prior to the determination of the speed. In comparison to the sonic anemometer measurements acquired at the same location, the wind velocity bias at 50 Hz is reduced from up to −1.58 m/s of the conventional lidar data to −0.18 m/s of the normalized lidar data. This significant reduction of the bias occurs at the minute with the highest amount of rain when the measurement distance of the lidar is 103.9 m with a corresponding probe length being 9.8 m. With the smallest probe length, 1.2 m, the rain-induced bias was only present at the period with the highest rain intensity and was also effectively eliminated with the procedure. Thus the proposed method for reducing the impact of rain on continuous-wave Doppler lidar measurements of air velocity is promising, without requiring much computational effort.

Liqin Jin et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-464', Anonymous Referee #1, 13 May 2023
    • AC1: 'Reply on RC1', Liqin Jin, 08 Jul 2023
  • RC2: 'Comment on egusphere-2023-464', Anonymous Referee #2, 29 May 2023
    • AC2: 'Reply on RC2', Liqin Jin, 08 Jul 2023
    • AC3: 'Reply on RC2', Liqin Jin, 08 Jul 2023
  • RC3: 'Comment on egusphere-2023-464', Anonymous Referee #3, 14 Jun 2023
    • AC4: 'Reply on RC3', Liqin Jin, 08 Jul 2023
  • RC4: 'Comment on egusphere-2023-464', Anonymous Referee #4, 14 Jun 2023
    • AC5: 'Reply on RC4', Liqin Jin, 09 Jul 2023

Liqin Jin et al.

Liqin Jin et al.


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Short summary
By sampling the spectra of a Doppler lidar faster than the raindrop's beam transit time, the rain signal can be filtered away and the bias on the wind velocity estimation can be reduced. In the method we propose, 3 kHz spectra are normalized with their peak values before retrieving the radial wind velocity. In three hours period, we have observed a significant reduction of the bias of the lidar data relative to the sonic. The tendency is that the more it rains, the more the bias is reduced.