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  • Author or Editor: Michael I. Biggerstaff x
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Daniel P. Betten, Michael I. Biggerstaff, and Louis J. Wicker


A visualization technique that allows simultaneous spatial analysis of complex flow behavior from thousands of Lagrangian trajectories is presented and tested using a high temporal and spatial resolution cloud model. The utility of the trajectory mapping technique is illustrated by showing that the source height of the air trajectories is a good proxy to the model-derived equivalent potential temperature. Moreover, the history of the forcing of vertical momentum is related to instantaneous vertical motion patterns shown to be elucidated in the trajectory mapping framework. The robustness of the trajectory mapping method was evaluated by integrating tendency terms and comparing Lagrangian-derived quantities to instantaneous values in the model. The original trajectory maps were also compared to those where the original fields have been filtered and/or the available data frequency are limited to the spatial and temporal scales typical of research radar datasets. The trajectory mapping method was applied to a supercell observed on 29 May 2004 to demonstrate that trajectory behavior for the observed case compares well to those from the higher-resolution numerical model output.

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Bradley Isom, Robert Palmer, Redmond Kelley, John Meier, David Bodine, Mark Yeary, Boon-Leng Cheong, Yan Zhang, Tian-You Yu, and Michael I. Biggerstaff


Mobile weather radars often utilize rapid-scan strategies when collecting observations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multibeam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field of view (FOV) of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) is engaged in the design, construction, and testing of a mobile imaging weather radar system called the atmospheric imaging radar (AIR). Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamforming techniques. Adaptive algorithms allow for improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during two severe weather events in Oklahoma. Several digital beamforming methods were tested and analyzed, producing unique, simultaneous multibeam measurements using the AIR.

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