Posters

Abstract

Lidars are increasingly being used for wind resource assessment in complex terrain. Complex flows are inhomogeneous, generally violating the historical homogeneity requirement for unbiased lidar wind field reconstruction. To compensate for these biases, computational fluid dynamics models (CFD) are used to estimate the flow curvature above the lidar. These estimated wind vectors are used in the wind field reconstruction instead of the homogeneous assumption. Typically, these CFD conversion factors use Reynolds Average Navier Stokes (RANS) models with fixed parameters, such as atmospheric stability, roughness length, and wind speed. One of the key parameters is the stability of the flow, its resistance to vertical motion. Stability determines how strongly topographical and vegetation variations can be converted into mechanical turbulence, affecting the turbulence and the velocity of the flow. Many CFD models use neutral stability, typically found at dawn and dusk, during the transition between daytime unstable conditions and nighttime stable conditions. But most of the time, the atmosphere is stable or unstable, not neutral. In this research, we show that parametrizing the CFD models used for lidar corrections with different stability values improves the performance of the corrections when comparing CFD-corrected lidar data with collocated met masts and CFD-corrected doppler lidar.