Evaluation of wind forecasts and observation impacts from variational and ensemble data assimilation for wind energy applications

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The U.S. Department of Energy Wind Forecast Improvement Project (WFIP) has recently been completed with the aim of 1) understanding the performance of different mesoscale data assimilation systems for lower-atmospheric wind prediction and 2) determining the observation impacts on wind forecasts within the different assimilation systems. Here an ensemble Kalman filter (EnKF) was tested against a three-dimensional variational data assimilation (3DVAR) technique. Forecasts lasting 24 hours were produced for a month-long period to determine the day-to-day performance of each system, as well as over 10 individual wind ramp cases. The observation impacts from surface mesonet and profiler/sodar wind observations aloft were also tested in each system for both the month-long run and the ramp forecasts. It was found that EnKF forecasts verified over a domain including Texas and Oklahoma were better than those of 3DVAR for the month-long experiment throughout the forecast window, presumably from the use of flow-dependent covariances in the EnKF. The assimilation of mesonet data improved both EnKF and 3DVAR early forecasts, but sodar/profiler data showed a degradation (EnKF) or had no effect (3DVAR), with the degradation apparently resulting from a lower-atmospheric wind bias. For the wind ramp forecasts, ensemble averaging appears to overwhelm any improvements flow-dependent assimilation may have on ramp forecasts, leading to better 3DVAR ramp prediction. This suggests that best member techniques within the EnKF may be necessary for improved performance over 3DVAR for forecasts of sharp features such as wind ramps. Observation impacts from mesonet and profiler/sodar observations generally improved EnKF ramp forecasts, but either had little effect on or degraded 3DVAR forecasts.

Original languageEnglish
Pages (from-to)3230-3245
Number of pages16
JournalMonthly Weather Review
Issue number8
StatePublished - 2015


  • Data assimilation
  • Ensembles
  • Kalman filters
  • Mesoscale forecasting
  • Variational analysis


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