Preprints
https://doi.org/10.5194/amt-2023-39
https://doi.org/10.5194/amt-2023-39
09 Mar 2023
 | 09 Mar 2023
Status: this preprint has been withdrawn by the authors.

Coupling physics-informed neuronal networks with 3D scanning pulsed Doppler lidar

Christian Schiefer, Sebastian Kauczok, Albert Töws, and Andre Weipert

Abstract. Physics-Informed neuronal networks (PINN) is a research field where a neuronal network is trained to solve an incorporated partial differential equation that describes some physical phenomenon. This work describes the coupling of the Navier Stokes (NS) equation with data from a 3D scanning pulsed Doppler lidar to reconstruct blanked sectors with radial velocities in a plan position indicator (PPI) scan. For the reconstruction, only the adjacent line of sight (LOS) measurements were used as input data for the neuronal network. Almost one year of collected lidar data were used to analyze the wind field sector reconstruction algorithm. The results show that the reconstruction of 35° azimuth sectors feature mean square errors of less than 1 m2/s2 and absolute errors of less than 2 m/s in 99 % and 98 %, respectively, of all cases. The runtime is about 0.1 minutes on average with commercial off-the-shelve CPU hardware. The reconstructed wind field of radial velocities can be used either to fill in sectors where the lidar is blocked e.g. by an obstacle or to extend the maximum operational range by measuring only a few lines-of-sight with increased pulse accumulation time. An example of a range extension PPI provided here demonstrates that the range can be extended to 25 km while maintaining the total recording time of 30 s as for the reference PPI scan featuring only a maximum range of approximately 12 km.

This preprint has been withdrawn.

Christian Schiefer et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-39', Jincheng Zhang, 17 Mar 2023
    • AC1: 'Reply on RC1', Christian Schiefer, 23 Mar 2023
  • RC2: 'Comment on amt-2023-39', Anonymous Referee #2, 20 Mar 2023
    • AC2: 'Reply on RC2', Christian Schiefer, 25 Mar 2023
      • EC1: 'Reply on AC2: Considering comparing with a simple interpolation.', Jorge Luis Chau, 30 Mar 2023
        • AC3: 'Reply on EC1', Christian Schiefer, 24 Apr 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-39', Jincheng Zhang, 17 Mar 2023
    • AC1: 'Reply on RC1', Christian Schiefer, 23 Mar 2023
  • RC2: 'Comment on amt-2023-39', Anonymous Referee #2, 20 Mar 2023
    • AC2: 'Reply on RC2', Christian Schiefer, 25 Mar 2023
      • EC1: 'Reply on AC2: Considering comparing with a simple interpolation.', Jorge Luis Chau, 30 Mar 2023
        • AC3: 'Reply on EC1', Christian Schiefer, 24 Apr 2023

Christian Schiefer et al.

Christian Schiefer et al.

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This preprint has been withdrawn.

Short summary
This work demonstrates the physics-informed neuronal algorithm capability to reconstruct wind fields of radial velocities based on 3D scanning Doppler lidar data. Empty PPI sectors can be reconstructed and a dedicated range extension scan is demonstrated with ranges up to 25 km.