Comparing observation impact between variational and ensemble data assimilation schemes on short-term, low-level wind forecasting



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A variety of studies have been performed to determine the effectiveness of different data assimilation schemes within numerical weather prediction. For sequential schemes, previous research using mesoscale models at the horizontal grid spacing of tens of kilometers has shown that the ensemble Kalman filter (EnKF) has outperformed a 3DVAR system in producing both analyses and subsequent forecasts. However, deciding if the same holds true for smaller grid spacing has yet to be determined. Also, the type of observations has been shown to make an impact on the resulting assimilation. This study focuses on investigating the relative performance of an EnKF and 3DVAR data assimilation scheme in producing low-level, short-term (0-24 hr) wind forecasts over the western two-thirds of the United States as well as specifically over Texas. This work uses a nested 12km/3km WRF-ARW modeling configuration and compares the Data Assimilation Research Testbed (DART) EnKF and the 3DVAR Gridpoint Statistical Interpolation (GSI) system over both domains for both routine wind forecasting and wind ramp event forecasting. In addition, a data denial experiment is set up with GSI to show the impact of adding a network of profilers and sodars on both domains for routine wind forecasting.



Data assimilation, Variational, Ensemble kalman filter, Wind forecasting, Observation impact