Sensitivity Study of Different Planetary Boundary Layer Schemes of WRF for a Case of Low-Level Jet in the NY Bight

Offshore wind energy will be a critical contributor to the US to transition into clean energy. The US government plans to install 30 GW offshore wind farms by 2030. Offshore wind is increasingly becoming a popular energy source as its levelized cost decreases yearly despite the higher material costs due to its abundance in the US coastal regions and close proximity to large population centers. Offshore wind speed is persistently faster than onshore due to the smaller surface roughness over the water. Stable stratification and small ambient turbulence also occur more often in the offshore environment. However, the efficiency of the wind turbines largely depends on our ability to assess and characterize wind resources at the rotor-swept area correctly.

Low-level jets (LLJ) are a phenomenon involving the occurrence of wind speed maxima in the atmospheric boundary layer. The wind speed increases above the surface and reaches a maximum, then slows above the wind speed maximum. LLJs are common in stably stratified boundary layers. The inertial oscillation of wind displaced by diurnal convective mixing and low-level baroclinicity due to some sources, including horizontal temperature gradient, are two known causes of LLJ formation. LLJs usually form due to local effects with weak synoptic pressure gradients, but in some cases, LLJs form due to strong pressure gradients in the meso or synoptic scale. These events usually have stronger winds in the jet maximum than the locally generated LLJs. Offshore LLJs usually form due to baroclinicity instead of solely inertial oscillations. Though LLJs can typically form up to 2 km ABL, the jet maxima often occur at a height relevant to wind energy.

The wind energy industry is interested in LLJs as they have a twofold impact on the wind power generation from a wind farm. On one hand, LLJs enhance the performance of wind turbines due to increased wind speed in the rotor-swept area. On the other hand, the stress on the turbine structure increases during an LLJ event due to increased wind shear and veer. Depending on the vertical position of the LLJs, turbine blades go in and out of varying wind speeds and directions, which imparts additional stress on the blades. Therefore, accurate representation and prediction of LLJs are essential in the wind energy industry.

Here, we conduct a sensitivity study of different planetary boundary layer (PBL) schemes of the Weather Research and Forecast (WRF) model by simulating a case of strong LLJ, which persisted for 9 hours on May 15, 2020, in the NY Bight (Chatterjee et al 2022). This LLJ has a developed jet nose at a height of around 100 m on May 15th at 16 UTC, dissipating in the early hours of May 16th. We test local, non-local, and scale-elimination PBL schemes, testing the hypothesis that the local PBL scheme MYNN will perform better compared to others as MYNN has proved to be effective in the simulation of winds in stably stratified atmosphere.

To assess model performance, we compare simulations to data from the offshore wind measurement campaign of the New York State Energy Research and Development Authority (NYSERDA), with two floating LIDARs within the simulation domain.
We use the five performance metrics described in Pronk et al., 2022: bias, the centered component of root-mean-square error (cRMSE), Pearson’s correlation coefficient, earth mover’s distance (EMD), and standard deviation. As the next step, we check the sensitivity of the boundary layer forcing dataset. Initially, we have run the simulations with the GFS dataset. We will assess the performance of NARR, MERRA2, and ERA5 forced from the above-mentioned comparisons with the best-performing PBL scheme.

References

Chatterjee, T., Li, J., Yellapantula, S., Jayaraman, B., & Quon, E. (2022). Wind farm response to mesoscale-driven coastal low level jets: A multiscale large eddy simulation study. In Journal of Physics: Conference Series (Vol. 2265). Institute of Physics. https://doi.org/10.1088/1742-6596/2265/2/022004

Pronk, V., Bodini, N., Optis, M., Lundquist, J. K., Moriarty, P., Draxl, C., Purkayastha, A., & Young, E. (2022). Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain? Wind Energy Science, 7(2), 487–504. https://doi.org/10.5194/WES-7-487-2022