Development and Validation of A Real-time Hail System Using High-Resolution Polarimetric Radar Network Observations

Hail is one of the most common natural hazards threating human life and property. For example, a hailstorm event in North Texas on March 26, 2017 caused damages running into hundreds of millions of dollars. Numerous studies in the past have shown that the microphysical information from dual-polarization radars can be used to identify different hydrometeor phases, but it has been very hard to demonstrate this in practice, due to lots of practical engineering problems. Another difficulty in hail detection using radars is the lack of in-situ observations for radar-based product verification. In addition, current operational weather radar network (i.e., NEXRAD) is unable to produce high spatiotemporal resolution observations for real-time hail warning operations.

In order to overcome the resolution and coverage limitations of traditional weather radar network, the center for Collaborative Adaptive Sensing of the Atmosphere (CASA) has deployed a dense network of short range high resolution X band radars in Dallas-Fort Worth (DFW) for urban weather disaster monitoring and mitigation (Chen and Chandrasekar 2015; Chandrasekar et al. 2017). Hail detection and hail path nowcast are among the most important operational products produced by the DFW network. In this paper, we implement the hydrometeor classification methodology proposed by Bechini and Chandrasekar (2015) for the DFW X-band radars and a local National Weather Service (NWS) radar at S-band. Compared to conventional fuzzy-logic approach, this region-based method is very appealing in terms of operational application and easy interpretation (Bechini and Chandrasekar 2015). In real time, the hydrometeor types identified by individual DFW radar nodes are merged together using clustering analysis, to produce a clean, networked level operational hail product. This paper details the hail detection scheme and clustering analysis of the DFW hail system. In addition, the in-situ observations and NWS ground hail reports are used for validation purposes.

References:

Bechini, R. and V. Chandrasekar, 2015: A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications. J. Atmos. Oceanic Technol., 32, 22-47.

Chandrasekar, V., Haonan Chen, and Brenda Philips, 2017: Principles of High-Resolution Radar Network for Hazard Mitigation and Disaster Management in an Urban Environment. Journal of the Meteorological Society of Japan. (to be published)

Chen, Haonan, and V. Chandrasekar, 2015: The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network. J. Hydrol., 531, 259-271.