TR2019-152

Improving LiDAR performance on complex terrain using CFD-based correction and direct-adjoint-loop optimization


    •  Nabi, S., Nishio, N., Grover, P., Matai, R., Kajiyama, Y., Kotake, N., Kameyama, S., Yoshiki, W., Iida, M., "Improving LiDAR performance on complex terrain using CFD-based correction and direct-adjoint-loop optimization", Journal of physics, DOI: 10.1088/1742-6596/1452/1/012082, Vol. 1452, November 2019.
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      • @article{Nabi2019nov,
      • author = {Nabi, S. and Nishio, N. and Grover, P. and Matai, R. and Kajiyama, Y. and Kotake, N. and Kameyama, S. and Yoshiki, W. and Iida, M.},
      • title = {Improving LiDAR performance on complex terrain using CFD-based correction and direct-adjoint-loop optimization},
      • journal = {Journal of physics},
      • year = 2019,
      • volume = 1452,
      • month = nov,
      • doi = {10.1088/1742-6596/1452/1/012082},
      • url = {https://www.merl.com/publications/TR2019-152}
      • }
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  • Research Area:

    Dynamical Systems


Naive estimation of horizontal wind velocity over complex terrain using measurements from a single wind-LiDAR introduces a bias due to the assumption of uniform velocity in any horizontal plane. While Computational Fluid Dynamics (CFD)-based methods have been proposed for bias removal, there are several issues exist in the implantation. For instance, the upstream atmospheric boundary layer thickness or direction are unknown. Conventional CFD-based corrections use trial and error to estimate the bias. Such approaches not only become numerically intractable for complicated flows, e.g. when the number of unknowns is large, but they also suffer from the fact that there is no guarantee for optimality of the obtained results. We propose a direct-adjoint-loop (DAL) optimization based framework to estimate such unknown parameters in a systematic way. For the validation of the method, we performed an experimental study using DIABREZZA LiDAR on a complex terrain for two wind directions of northwesterly (NW) and southeasterly (SE). The slope error associated with linear regression improved from -0.09 to -0.02 for SE and from -0.09 to +0.04 for NW.