Abstract
The Weather Research and Forecasting (WRF) model is used to investigate choice of land surface model (LSM) on the near surface wind profile, including heights reached by multi-megawatt (MW) wind turbines. Simulations of wind profiles and surface energy fluxes were made using five LSMs of varying degrees of sophistication in dealing with soil–plant–atmosphere feedbacks for the Department of Energy (DOE) Southern Great Plains (SGP) Atmospheric Radiation Measurement Program (ARM) Central Facility in Oklahoma, USA. Surface flux and wind profile measurements were available for validation. WRF was run for three, two-week periods covering varying canopy and meteorological conditions. The LSMs predicted a wide range of energy flux and wind shear magnitudes even during the cool autumn period when we expected less variability. Simulations of energy fluxes varied in accuracy by model sophistication, whereby LSMs with very simple or no soil–plant–atmosphere feedbacks were the least accurate; however, the most complex models did not consistently produce more accurate results. Errors in wind shear were also sensitive to LSM choice and were partially related to energy flux accuracy. The variability of LSM performance was relatively high suggesting that LSM representation of energy fluxes in WRF remains a large source of model uncertainty for simulating wind turbine inflow conditions.
Highlights
Atmospheric models are not perfect predictors of incoming wind conditions or “inflow” at heights spanned by industrial-scale wind turbines (~40 to 200 m above ground level, a.g.l.)
We hypothesize that wind speeds simulated at heights equivalent to a turbine rotor disk are sensitive to land surface model (LSM) choice due to variations in the sophistication of each model’s characterization or parameterization of the soil–vegetation–atmosphere continuum
The rapid changes in crop cover, albedo, and surface meteorology are evident in the soil moisture and energy flux measurements, the latter shown by the midday Bowen ratio (Figure 4)
Summary
Atmospheric models are not perfect predictors of incoming wind conditions or “inflow” at heights spanned by industrial-scale wind turbines (~40 to 200 m above ground level, a.g.l.). One way to optimize model accuracy is to identify areas in numerical models that may lead to a wind forecasting improvement. This is a warranted endeavor, as a wind forecasting improvement of as little as. We hypothesize that wind speeds simulated at heights equivalent to a turbine rotor disk are sensitive to LSM choice due to variations in the sophistication of each model’s characterization or parameterization of the soil–vegetation–atmosphere continuum. In this work, this theory is tested for northern Oklahoma using the Weather Research and Forecasting (WRF) model [2]
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