• Open Access

Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

Authors

  • John L. Williams III,

    Corresponding author
    1. Hydrologic Science and Engineering Program, Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado, USA
    2. Meteorological Institute, University of Bonn, Bonn, Germany
    Current affiliation:
    1. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
    • Corresponding author: J. L. Williams, Meteorological Institute, University of Bonn, Meckenheimer Allee 176, DE-53115 Bonn, Germany. (jwilliam@uni-bonn.de)

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  • Reed M. Maxwell,

    1. Hydrologic Science and Engineering Program, Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado, USA
    2. Integrated Groundwater Modeling Center, Colorado School of Mines, Golden, Colorado, USA
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  • Luca Delle Monache

    1. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
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Abstract

[1] Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.

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