Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications

Publication: Research - peer-reviewJournal article – Annual report year: 2011


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This paper describes an advanced multi-scale weather modeling system, WRF–RTFDDA–LES, designed to simulate synoptic scale (~2000 km) to small- and micro-scale (~100 m) circulations of real weather in wind farms on simultaneous nested grids. This modeling system is built upon the National Center for Atmospheric Research (NCAR) community Weather Research and Forecasting (WRF) model. WRF has been enhanced with the NCAR Real-Time Four-Dimensional Data Assimilation (RTFDDA) capability. FDDA is an effective data assimilation algorithm, which is capable of assimilating diverse weather measurements on model grids and seamlessly providing realistic mesoscale weather forcing to drive a large eddy simulation (LES) model within the WRF framework. The WRF based RTFDDA LES modeling capability is referred to as WRF–RTFDDA–LES. In this study, WRF–RTFDDA–LES is employed to simulate real weather in a major wind farm located in northern Colorado with six nested domains. The grid sizes of the nested domains are 30, 10, 3.3, 1.1, 0.370 and 0.123 km, respectively. The model results are compared with wind–farm anemometer measurements and are found to capture many intra-farm wind features and microscale flows. Additional experiments are conducted to investigate the impacts of subgrid scale (SGS) mixing parameters and nesting approaches. This study demonstrates that the WRF–RTFDDA–LES system is a valuable tool for simulating real world microscale weather flows and for development of future real-time forecasting system, although further LES modeling refinements, such as adaptive SGS mixing parameterization and wall-effect modeling, are highly desired.
Original languageEnglish
JournalJournal of Wind Engineering & Industrial Aerodynamics
Issue number4
Pages (from-to)308-319
StatePublished - 2011
CitationsWeb of Science® Times Cited: 29


  • LES, Multiscale Modeling, wind energy, WRF, 4D data assimilation, NWP
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