Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development

Abstract

The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.

Quality of wind characteristics in recent wind atlases over the North Sea

“…Hence our current results are a substantial corroboration of this earlier result. Apparently, stable conditions are in general still challenging (Holtslag et al ., ; Sandu et al ., ; Steeneveld, ; Tsiringakis et al ., ), despite recent efforts to improve the turbulent mixing formulation (Sandu et al ., ; Bengtsson et al ., ; Olson et al ., ; ). Furthermore, the results in Figure b support the hypothesis that the slow speed bias results from a smoothing effect, as this would manifest itself most clearly for distinct wind speed maxima.…”

A Verification Approach Used in Developing the Rapid Refresh and Other Numerical Weather Prediction Models

“…However, each of these apps and database instances-from a high-level point of viewis essentially the same and thus we will use the surface meteorology app to illustrate how the back and front ends of the verification system work. GSD's model verification system is used daily for monitoring the accuracy of the current operational models, evaluating improvements in model physics (e.g., Benjamin et al 2016;Olson et al 2019), and evaluating the impact of different datasets in the data assimilation system (e.g., James and Benjamin 2017).…”

“…We already have one app that separates the surface meteorological statistics as a function of land-surface type; this app has proven very useful in adjusting the HRRR's performance over different surface roughness lengths (for example). Furthermore, funding from the second Wind Forecast Improvement Project (WFIP-2; Olson et al 2019;Wilczak et al 2019) has allowed us to develop a prototype app that includes a wide range of discriminators that can be used when generating the verification statistics.…”

The Hydrometeorology Testbed–West Legacy Observing Network: Supporting Research to Applications for Atmospheric Rivers and Beyond

“…These observations support studies of model verification and validation, the impact of observations on model performance, and parameterization, which can ultimately lead to improvements in models and services. Some examples of current research in these areas include studies of the impact of instrumentation or data assimilation on model skill, as in the impact of observations on the skill of numerical weather prediction models at forecasting wind events and wind speeds [63,64]; model verification/validation, including parameterization and algorithms [65][66][67][68]; how model improvements impact skill of wind forecasts [41,69]; and evaluation of microphysical algorithms in the Advanced Research Weather and Research Forecast Model [70]. Observations have also been used in assessing model skill in forecasting ARs during CalWater-2015 [51], and in wind speeds for wind turbines [64].…”

“…The WFIP2 field campaign used the AROs in Washington and Oregon, as well as 915-MHz radars and other instrumentation, to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications [13,41,65,69]. A large suite of instrumentation was deployed in the Pacific Northwest [14], including eight 915-MHz WP radars and radio acoustic sounding systems (RASS) deployed in complex terrain with many wind farms and large wind power production, and three existing coastal ARO 449-MHz radars.…”