Evaluation of Distributed Collaborative Adaptive Sensing for Detection of Low-Level Circulations and Implications for Severe Weather Warning Operations

Abstract

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is a multiyear engineering research center established by the National Science Foundation for the development of small, inexpensive, low-power radars designed to improve the scanning of the lowest levels (,3 km AGL) of the atmosphere. Instead of sensing autonomously, CASA radars are designed to operate as a network, collectively adapting to the changing needs of end users and the environment; this network approach to scanning is known as distributed collaborative adaptive sensing (DCAS). DCAS optimizes the low-level volume coverage scanning and maximizes the utility of each scanning cycle. A test bed of four prototype CASA radars was deployed in southwestern Oklahoma in 2006 and operated continuously while in DCAS mode from March through June of 2007.This paper analyzes three convective events observed during April-May 2007, during CASA's intense operation period (IOP), with a special focus on evaluating the benefits and weaknesses of CASA radar system deployment and DCAS scanning strategy of detecting and tracking low-level circulations. Data collected from nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) and CASA radars are compared for mesoscyclones, misocyclones, and low-level vortices. Initial results indicate that the dense, overlapping coverage at low levels provided by the CASA radars and the high temporal (60 s) resolution provided by DCAS give forecasters more detailed feature continuity and tracking. Moreover, the CASA system is able to resolve a whole class of circulations-misocyclones-far better than the WSR-88Ds. In fact, many of these are probably missed completely by the WSR-88D. The impacts of this increased detail on severe weather warnings are under investigation. Ongoing efforts include enhancing the DCAS data quality and scanning strategy, improving the DCAS data visualization, and developing a robust infrastructure to better support forecast and warning operations.

The Analysis and Prediction of the 8–9 May 2007 Oklahoma Tornadic Mesoscale Convective System by Assimilating WSR-88D and CASA Radar Data Using 3DVAR

“…Even though the CASA radars have a relatively broad beamwidth (1.88 vs the 0.898 of WSR-88D), their range gate spacing of 100 m (vs 250 m for WSR-88D), adaptive sampling capabilities, and the short baseline of the CASA radars allow for much higher spatial and temporal resolution than typically available with WSR-88D radars. Such finescale resolution is paramount for observing small-scale features such as tornadoes (Brotzge et al 2010).…”

Genesis of the Chickasha, Oklahoma, tornado on 24 May 2011 as observed by CASA radar and Oklahoma Mesonet

“…CASA prototype (and similarly designed Xband) radars can provide real-time adaptive scanning at high temporal (60 s) and spatial (~100 m) sampling , and networks of these radars can be used to fill gaps in low-level coverage. [For a detailed list of CASA radar specifications, see Junyent et al (2010) The CASA radars collected data in an automated, adaptive fashion (Brotzge et al 2010). The radars started a new scan cycle every 60 s. During that time, the radars either completed three 360° scans at lowlevels (1, 2, and 3° elevation) or finished one 360° scan at the 2° elevation followed by sector scans of varying width.…”

Enhanced air operations using JView for an air-ground fused situation awareness udop

“…With DDDAS, real-time weather information from ground stations [50] can be used for emergency management [51]. Overlays (e.g., display fusion) of weather from aircraft routes can provide SAW (L2), increase safety from impending weather threats (L3) and support monitoring for users (L5) [52].…”