J14.3
Ensemble data assimilation for wind energy applications

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Wednesday, 26 January 2011: 2:00 PM
Ensemble data assimilation for wind energy applications
2A (Washington State Convention Center)
Luca Delle Monache, NCAR, Boulder, CO; and G. Descombes, B. Kosovic, M. D. Simpson, V. Bulaevskaya, M. Alai, and L. G. Glascoe

Ensemble data assimilation (DA) is explored for wind energy applications. The proposed ensemble Kalman Filter analysis and short-term wind prediction system is based on the Weather Research and Forecasting (WRF) model, coupled with the Data Assimilation Research Testbed (DART). WRF is a community mesoscale numerical weather prediction system designed to serve the needs of operational forecasting and atmospheric research. It is a collaborative effort among several institutions including the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration, the Air Force Weather Agency, the Naval Research Laboratory, the University of Oklahoma, and the Federal Aviation Administration. DART is a community facility for ensemble DA developed and maintained by the Data Assimilation Research Section (DAReS) at NCAR. DART provides state-of-the-science DA tools that are easy to implement and use for a variety of DA applications.

The prognostic quantity of interest in wind energy applications is the wind in the lower part of the atmospheric boundary layer, and in particular winds at hub height, i.e., approximately 80-100 m above ground level. Quantities assimilated in the DA experiments include conventional surface and upper-air observations and a high-resolution sea surface temperature satellite data set. Additionally, wind and temperature observations from meteorological towers in the proximity of the wind farm are assimilated. The system is tested for wind predictions over an off-shore wind farm, and the WRF model is run with two nested domains with 36 and 12 horizontal grid increments. An analysis of WRF-DART performance with the different localization and inflation algorithms available in DART as well as with ensembles of different sizes (from 30 to 80 members) will be presented.