Title: Comparing observation impact on low-level wind forecasts between an ensemble Kalman filter and a 3DVAR data assimilation scheme

Authors: Erin Kashawlic (Texas Tech University)
Brian Ancell (Texas Tech University)

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 horizontal resolutions 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, the relative performance of these systems at very fine grid spacing is unclear. This study focuses on investigating the relative performance of a high-resolution EnKF and 3DVAR data assimilation scheme in producing low-level, 0-24hr wind forecasts over northwest Texas. This work employs 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.

Initial assimilation experiments using a variety of deployed sodar, radiosonde, and surface observations beyond the routine observational network are performed and results are presented here. The forecast quality of the EnKF and the GSI systems are compared, and the observational impacts within both assimilation systems are investigated to understand whether the most important observations vary among the two schemes. The ultimate goal of this study is to discover the best way to assimilate the most important observations in producing low-level wind and subsequent wind power forecasts. Future plans for additional assimilation experiments are discussed.

GMAO Head: Michele Rienecker Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: May 27 2011