1B.3 Improving 0 – 12h wind energy prediction by assimilating surface, met-tower, turbine nacelle anemometer, wind profiler and lidar wind observations using the NCAR WRF-RTFDDA model

Monday, 2 August 2010: 4:00 PM
Torrey's Peak III & IV (Keystone Resort)
Yubao Liu, NCAR, Boulder, CO

In collaboration with Xcel Energy and Vasaila Inc., the National Center for Atmospheric Research (NCAR) conducts modeling study to evaluate the existing and the enhanced intensive observation systems for wind power nowcasting and short-range forecasting at a northern Colorado wind farm. The NCAR WRF (Weather Research and Forecasting model) based Real-Time Four-Dimensional Data Assimilation (RTFDDA) and forecasting system, which has been employed to support Xcel Energy operational wind forecast, was used in this study. The observational data include the data collected from ten meteorological towers (Met-tower), one 915Hz wind profiler, one Windcube Doppler lidar, as well as wind speed and power generation reports from more than 300 wind turbines. The WRF-RTFDDA 4D data assimilation algorithm allows spreading and propagating observational information in the WRF model space (x, y, z and time) with weighting functions, which are built upon observation location and time. The WRF-RTFDDA was set up to run with four nested domains with grid increments of 30, 10, 3.333 and 1.111km, respectively. In this study, we investigated a) spread of surface observations in PBL according to PBL depth and regimes, b) optimization of horizontal influence radii and steep-terrain adjustment, c) different contributions of various observation platforms, d) PBL changes in response to the data assimilation. It is found that PBL mixing and thermodynamic structures are greatly influenced by the PBL parameterization formulation. The range of the data assimilation effect on forecasts relies on both weather and PBL regimes. In most cases, assimilation of both in-farm and near-farm observations can improve up to 12-hour wind power prediction, while assimilation of in-farm data can significantly improve 0–6 hour forecasts.
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