Doppler Lidar-based Characterization of Wake Flows and their Impact on Turbulence in the Atmospheric Boundary Layer

Alternate forms of generating electricity must be implemented worldwide to reduce the warming of our atmosphere and oceans due to climate change. Currently, electricity generation worldwide is predominately created by burning fossil fuels, which releases greenhouse gases into the atmosphere, accelerating global warming. Global air temperature has risen by 0.20°C in the past three decades. Alternate sources of electricity independent of fossil fuels have been achieved via harnessing renewable energy sources (e.g., solar, hydro, and wind. Kinetic energy is extracted from the ambient flow using a wind turbine, which creates and transfers energy to our electrical grids worldwide. We often observe a substantial decrease in the ambient flow and an increase in turbulence downstream of a wind turbine due to the extraction of the kinetic energy.For wind farms, the wake phenomenon can lead to financial losses for an individual wind farm upwards of $750,000 annually and an increase in structural loading that can cause a wind farm to have a shortened lifespan and reduced wind energy yield.

Currently, we recognize that wakes meander and propagate further downstream during stable atmospheric conditions due to the presence of laminar flow. However, we do not understand how wakes impact the kinematic transport of heat, moisture, and momentum with the lower Atmospheric Boundary Layer (ABL). Heat, moisture, and momentum transport in the lower ABL are critical parameters in our numerical weather prediction (NWP) models to simulate weather events (e.g., thunderstorms). High-resolution measurements of moisture, temperature, and the 3-D velocity components are necessary to capture the effect of the wake on the transport of heat, momentum, and moisture in the lower ABL. Wakes can significantly impact the 3-D wind components, moisture, temperature, and pressure, leading to errors in our ability to forecast the weather in the region around wind farms using our high-resolution numerical weather models (e.g., HRRR).

For the analysis of the wake characteristics, a Doppler lidar was deployed to capture the radial velocity deficits at 1Hz resolution of the wake through different scanning strategies (PPI, RHI, Vertical Stare, and VAD-6) downstream of a 200-m meteorological tower and a small, scaled wind farm in Lubbock, Texas. To perform the kinematic heat, moisture, and momentum analysis, meteorological data of the 3-D wind components, moisture, and temperature were measured at 50 Hz resolution at ten different levels (e.g., 0.9 – 200 m) spaced logarithmically with height. Additionally, vertical velocity variance was derived from the Doppler lidar and 200-m tower measurements to understand better how a wake alters vertical transport within thermals. HRRR output will be utilized to verify NWP forecasts around wind farms over the central plains. Combining the observations and model outputs, we aim to identify errors in our parameterization schemes to improve our NWP forecasts around wind farms.