scholarly journals A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets

2021 ◽  
Vol 14 (6) ◽  
pp. 3141-3158
Author(s):  
Xiaoli G. Larsén ◽  
Jana Fischereit
Keyword(s):  
Wind Farm ◽  
Wrf Model ◽  
Wind Farms ◽  
Weather Research And Forecasting ◽  
Low Level ◽  
Low Level Jets ◽  
Simulation Results ◽  
Weather Research

Abstract. While the wind farm parameterization by Fitch et al. (2012) in the Weather Research and Forecasting (WRF) model has been used and evaluated frequently, the explicit wake parameterization (EWP) by Volker et al. (2015) is less well explored. The openly available high-frequency flight measurements from Bärfuss et al. (2019a) provide an opportunity to directly compare the simulation results from the EWP and Fitch scheme with in situ measurements. In doing so, this study aims to complement the recent study by Siedersleben et al. (2020) by (1) comparing the EWP and Fitch schemes in terms of turbulent kinetic energy (TKE) and velocity deficit, together with FINO 1 measurements and synthetic aperture radar (SAR) data, and (2) exploring the interactions of the wind farm with low-level jets (LLJs). This is done using a bug-fixed WRF version that includes the correct TKE advection, following Archer et al. (2020). Both the Fitch and the EWP schemes can capture the mean wind field in the presence of the wind farm consistently and well. TKE in the EWP scheme is significantly underestimated, suggesting that an explicit turbine-induced TKE source should be included in addition to the implicit source from shear. The value of the correction factor for turbine-induced TKE generation in the Fitch scheme has a significant impact on the simulation results. The position of the LLJ nose and the shear beneath the jet nose are modified by the presence of wind farms.

scholarly journals A case study of wind farm effects using two wake parameterizations in WRF (V3.7) in the presence of low level jets

2020 ◽  
Author(s):  
Xiaoli G. Larsén ◽  
Jana Fischereit
Keyword(s):  
Wind Farm ◽  
Wrf Model ◽  
Wind Farms ◽  
Weather Research And Forecasting ◽  
Flow Acceleration ◽  
Low Level ◽  
Low Level Jets ◽  
Flight Measurements

Abstract. While the wind farm parameterization by Fitch et al. (2012) in Weather Research and Forecasting (WRF) model has been used and evaluated frequently, the Explicit Wake Parameterization (EWP) by Volker et al. (2015) is less well explored. The openly available high frequency flight measurements from Bärfuss et al. (2019) provide an opportunity to directly compare the simulation results from the EWP and Fitch scheme with in situ measurements. In doing so, this study aims to compliment the recent study by Siedersleben et al. (2020) by (1) comparing the EWP and Fitch schemes in terms of turbulent kinetic energy (TKE) and velocity deficit, together with FINO 1 measurements and Synthetic Aperture Radar (SAR) data and (2) exploring the interactions of the wind farm with Low Level Jets. Both the Fitch and the EWP schemes can capture the mean wind field in the presence of the wind farm consistently and well. However, their skill is limited in capturing the flow acceleration along the farm edge. TKE in the EWP scheme is significantly underestimated, suggesting that an explicit turbine-induced TKE source should be included in addition to the implicit source from shear. The position of the LLJ nose and the shear beneath the jet nose are modified by the presence of wind farms.

The 4–5 December 2001 IMPROVE-2 Event: Observed Microphysics and Comparisons with the Weather Research and Forecasting Model

2009 ◽  
Vol 137 (4) ◽  
pp. 1372-1392 ◽  
Author(s):  
Yanluan Lin ◽  
Brian A. Colle
Keyword(s):  
Water Conservation ◽  
Wrf Model ◽  
Spherical Particles ◽  
Weather Research And Forecasting ◽  
Coast Range ◽  
Surface Precipitation ◽  
Orographic Rainfall ◽  
Convective Cells ◽  
Weather Research

Abstract This paper highlights the observed and simulated microphysical evolution of a moderate orographic rainfall event over the central Oregon Cascade Range during 4–5 December 2001 of the Second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2). Airborne in situ measurements illustrate the spatial variations in ice crystal distributions and amounts over the windward Cascades and within some convective cells. The in situ microphysical observations, ground radars, and surface observations are compared with four bulk microphysical parameterizations (BMPs) within the Weather Research and Forecasting (WRF) model. Those WRF BMP schemes that overpredict surface precipitation along the Cascade windward slopes are shown to have too rapid graupel (rimed snow) fallout. Most BMP schemes overpredict snow in the maximum snow depositional growth region aloft, which results in excessive precipitation spillover into the immediate lee of the Cascades. Meanwhile, there is underprediction to the east of the Cascades in all BMP schemes. Those BMPs that produce more graupel than snow generate nearly twice as much precipitation over the Oregon Coast Range as the other BMPs given the cellular convection in this region. Sensitivity runs suggest that the graupel accretion of snow generates too much graupel within select WRF BMPs. Those BMPs that generate more graupel than snow have shorter cloud residence times and larger removal of available water vapor. Snow depositional growth may be overestimated by 2 times within the BMPs when a capacitance for spherical particles is used rather than for snow aggregates. Snow mass–diameter relationships also have a large impact on the snow and cloud liquid water generation. The positive definite advection scheme for moisture and hydrometeors in the WRF reduces the surface precipitation by 20%–30% over the Coast Range and improves water conservation, especially where there are convective cells.

scholarly journals Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

2017 ◽  
Vol 10 (11) ◽  
pp. 4229-4244 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Statistical Significance ◽  
Wrf Model ◽  
Grid Cell ◽  
Power Production ◽  
Weather Research And Forecasting ◽  
Model Version ◽  
The Impact ◽  
Weather Research

Abstract. Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

scholarly journals Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

2017 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Power Production ◽  
Weather Research And Forecasting ◽  
Model Version ◽  
The Impact ◽  
Weather Research

Abstract. Forecasts of wind power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate that a vertical grid with nominally 12-m vertical resolution is necessary for reproducing the observed power production, with statistical significance. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed and low turbulence conditions. We also find the WFP performance is independent of atmospheric stability, the number of wind turbines per model grid cell, and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

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