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line that is generated by boundary layer separation resulting from strong downslope winds and lifted aloft by the lee wave circulation ( Doyle et al. 2009 ). Some of the major challenges for T-REX are to observe these subrotor vortices, to estimate the strength of the horizontal vorticity, and to investigate their characteristics. In this study, high-resolution scanning Doppler lidar observations taken during T-REX are analyzed, and a method for deriving tangential velocity V Ï• and vorticity
line that is generated by boundary layer separation resulting from strong downslope winds and lifted aloft by the lee wave circulation ( Doyle et al. 2009 ). Some of the major challenges for T-REX are to observe these subrotor vortices, to estimate the strength of the horizontal vorticity, and to investigate their characteristics. In this study, high-resolution scanning Doppler lidar observations taken during T-REX are analyzed, and a method for deriving tangential velocity V Ï• and vorticity
coherent Doppler lidars were operated by the Arizona State University (ASU) and by the Institute of Atmospheric Physics of the German Aerospace Center (DLR), Oberpfaffenhofen, respectively. A proven algorithm for the 3D wind retrieval from multiple Doppler radars was applied to the dual-lidar observations. We chose the Multiple Doppler Synthesis and Continuity Adjustment Technique (MUSCAT) since it provides stable solutions and can be used over complex terrain. A brief description of MUSCAT will be
coherent Doppler lidars were operated by the Arizona State University (ASU) and by the Institute of Atmospheric Physics of the German Aerospace Center (DLR), Oberpfaffenhofen, respectively. A proven algorithm for the 3D wind retrieval from multiple Doppler radars was applied to the dual-lidar observations. We chose the Multiple Doppler Synthesis and Continuity Adjustment Technique (MUSCAT) since it provides stable solutions and can be used over complex terrain. A brief description of MUSCAT will be
towers, airborne sensors, and ground-based remote sensors (radar, lidar, sodar, and wind profiler; e.g., Balsley et al. 1988 ; Van Zandt 2000 ; Contini et al. 2004 ; Shupe et al. 2008 ). Each of these instruments and techniques has its own strengths and limitations, and they all are expensive. The tower and airborne sensors are limited in vertical coverage, and the VV obtained from wind profilers is susceptible to several types of biases ( Rao et al. 2008 ). The intent of this paper is to explore
towers, airborne sensors, and ground-based remote sensors (radar, lidar, sodar, and wind profiler; e.g., Balsley et al. 1988 ; Van Zandt 2000 ; Contini et al. 2004 ; Shupe et al. 2008 ). Each of these instruments and techniques has its own strengths and limitations, and they all are expensive. The tower and airborne sensors are limited in vertical coverage, and the VV obtained from wind profilers is susceptible to several types of biases ( Rao et al. 2008 ). The intent of this paper is to explore
