Principles of High-Resolution Radar Network for Hazard Mitigation and Disaster Management in an Urban Environment

The center for the Collaborative Adaptive Sensing of the Atmosphere (CASA) Dallas–Fort Worth (DFW) Urban Demonstration Network consists of a high-resolution X-band radar network and a National Weather Service S-band radar system (i.e., KFWS radar). On the basis of these radars, CASA has developed an end-to-end warning system that includes sensors, software architecture, products, data dissemination and visualization, and user decision-making modules. This paper presents a technical summary of the DFW radar network for urban weather disaster detection and mitigation from the perspective of the tracking and warning of hails, tornadoes, and floods. Particularly, an overview of the design trade-offs of the X-band radar network is presented. The architecture and associated algorithms for various product systems are described, including the real-time hail detection system, the multiple Doppler vector wind retrieval system, and the high-resolution quantitative precipitation estimation system. Sample products in the presence of high wind, tornado, hail, and flash flood are provided, and the system performance is demonstrated by cross-validation with ground observations and weather reports.

[1]  L. J. Miller,et al.  A dual doppler radar method for the determination of wind velocities within precipitating weather systems , 1974 .

[2]  R. E. Carlson,et al.  Monotone Piecewise Cubic Interpolation , 1980 .

[3]  V. Chandrasekar,et al.  Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Sean X. Zhang CASA real-time multi-Doppler retrieval system , 2011 .

[5]  V. Chandrasekar,et al.  A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications , 2015 .

[6]  V. N. Bringi,et al.  Rain-Rate Estimation in the Presence of Hail Using S-Band Specific Differential Phase and Other Radar Parameters , 1995 .

[7]  V. Chandrasekar,et al.  Short wavelength technology and the potential for distributed networks of small radar systems , 2009, 2009 IEEE Radar Conference.

[8]  V. Chandrasekar,et al.  Polarimetric Doppler Weather Radar: Principles and Applications , 2001 .

[9]  Francesc Junyent,et al.  Theory and Characterization of Weather Radar Networks , 2009 .

[10]  Conrad L. Ziegler,et al.  Single- and Multiple-Doppler Radar Observations of Tornadic Storms , 1980 .

[11]  V. Chandrasekar,et al.  The CASA quantitative precipitation estimation system: a five year validation study , 2012 .

[12]  V. Chandrasekar,et al.  A Parametric Time Domain Method for Spectral Moment Estimation and Clutter Mitigation for Weather Radars , 2008 .

[13]  V. Chandrasekar,et al.  The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network , 2015 .

[14]  Eiichi Yoshikawa,et al.  Probabilistic Attenuation Correction in a Networked Radar Environment , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Nitin Bharadwaj,et al.  The CASA Integrated Project 1 Networked Radar System , 2010 .

[16]  Jeff W. Brogden,et al.  Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities , 2016 .

[17]  Lawrence D. Carey,et al.  A multiparameter radar case study of the microphysical and kinematic evolution of a lightning producing storm , 1996 .

[18]  V. Chandrasekar,et al.  Time-varying ice crystal orientation in thunderstorms observed with multiparameter radar , 1996, IEEE Trans. Geosci. Remote. Sens..

[19]  V. Chandrasekar,et al.  Error Structure of Multiparameter Radar and Surface Measurements of Rainfall. Part III : Specific Differential Phase , 1990 .

[20]  V. Chandrasekar,et al.  A Robust Attenuation Correction System for Reflectivity and Differential Reflectivity in Weather Radars , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Nitin Bharadwaj,et al.  Signal Processing System for the CASA Integrated Project I Radars , 2010 .

[23]  Michael Zink,et al.  Closed-loop architecture for distributed collaborative adaptive sensing of the atmosphere: meteorological command and control , 2010, Int. J. Sens. Networks.

[24]  V. Chandrasekar,et al.  Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification , 2000 .

[25]  Dmitri Moisseev,et al.  Recent advances in classification of observations from dual polarization weather radars , 2013 .

[26]  G. P. Cressman AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEM , 1959 .

[27]  J. Todd Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[28]  V. Chandrasekar,et al.  An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign , 2017 .

[29]  Eugenio Gorgucci,et al.  Evaluation of Attenuation Correction Methodology for Dual-Polarization Radars: Application to X-Band Systems , 2005 .