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Yanting Wang and V. Chandrasekar

Abstract

This paper presents the sensing aspects and performance evaluation of the quantitative precipitation estimation (QPE) system in an X-band dual-polarization radar network developed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center. CASA’s technology enables precipitation observation close to the ground and QPE is one of the important applications. With expanding urbanization all over the world, vulnerability to floods has increased from intense rainfall such as urban flash floods. The QPE products that are derived at high spatiotemporal resolution, which is enabled by the deployment of a dense radar network, have the potential to improve the prediction of flash-flooding threats when coupled with hydrological models. Derivation of QPE from radar observations is a challenging process, in which the use of dual-polarization radar variables is advantageous. At X band, the specific differential propagation phase (K dp) between the orthogonal linear polarization states is particularly appealing. The K dp field is robustly acquired using an adaptive estimation method, and a simple R(K dp) relation is used to perform precipitation estimation in this X-band radar network. Radar observations and QPE from multiyear field experiments are used to demonstrate the performance of rainfall estimation from the single-parameter K dp-based rainfall product. The operational feasibility of radar QPE using an X-band radar network is critically assessed.

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Yanting Wang and V. Chandrasekar

Abstract

The specific differential phase K dp is one of the important parameters measured by dual-polarization radar that is being considered for the upgrade of the current Next Generation Weather Radar (NEXRAD) system. Estimation of the specific differential phase requires computing the derivative of range profiles of the differential propagation phase. The existence of possible phase wrapping, noise, and associated fluctuation in the differential propagation phase makes the evaluation of derivatives an unstable numerical process. In this paper, a robust algorithm is presented to estimate the specific differential phase, which is able to work on wrapped phases and keep up with the spatial gradients of rainfall, to provide a high-resolution specific differential phase.

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Yanting Wang, V. Chandrasekar, and V. N. Bringi

Abstract

Transmitting an arbitrary state of polarization while receiving horizontal–vertical polarization states is termed the hybrid polarization mode of operation. A theoretical model is developed for hybrid mode dual-polarization measurements in terms of the covariance matrix under linear horizontal–vertical polarization basis. The cross polarization encountered introduces biases in the copolar parameters estimated in the hybrid mode. Such biases are investigated for different precipitation types and propagation effects resulting from hydrometeor orientation and antenna properties. Polarimetric data measured by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar transmitting horizontal–vertical polarization states is alternately used to demonstrate the measurement accuracy that would be expected in different storm scenarios observed in the hybrid mode.

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Evan Ruzanski, V. Chandrasekar, and Yanting Wang

Abstract

Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories and is of great practical importance. Nowcasting using weather radar reflectivity data has been shown to be particularly useful and the Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network provides high-resolution (0.5-km spatial and 1-min temporal resolution) reflectivity data that are amenable to producing valuable nowcasts. This paper describes the theory and implementation of a nowcasting system operating in the CASA Distributed Collaborative Adaptive Sensing network and shows that nowcasting can be reliably performed in such a distributed environment. In this context, nowcasting is used in a traditional sense to produce predictions of radar reflectivity fields up to 10 min into the future to support emergency manager decision making, and in a novel manner to support researchers and operational forecasters where 1–5-min nowcasts are used to steer the radar nodes to better observe moving precipitation systems. The high-resolution nature of CASA data and distributed system architecture necessitate the use of a fast nowcasting algorithm. A method is described that uses linear least squares estimation implemented in the Fourier domain for motion estimation with advection performed via a kernel-based method formulated in the spatial domain. Results of a performance evaluation during the CASA 2009 Integrative Project 1 experiment are presented that show that the nowcasting system significantly outperformed persistence forecasts of radar reflectivity in terms of critical success index and mean absolute error for lead times up to 10 min. Feedback from end users regarding the use of nowcasting for adaptive scanning was also unanimously positive.

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David McLaughlin, David Pepyne, V. Chandrasekar, Brenda Philips, James Kurose, Michael Zink, Kelvin Droegemeier, Sandra Cruz-Pol, Francesc Junyent, Jerald Brotzge, David Westbrook, Nitin Bharadwaj, Yanting Wang, Eric Lyons, Kurt Hondl, Yuxiang Liu, Eric Knapp, Ming Xue, Anthony Hopf, Kevin Kloesel, Alfred DeFonzo, Pavlos Kollias, Keith Brewster, Robert Contreras, Brenda Dolan, Theodore Djaferis, Edin Insanic, Stephen Frasier, and Frederick Carr

Dense networks of short-range radars capable of mapping storms and detecting atmospheric hazards are described. Composed of small X-band (9.4 GHz) radars spaced tens of kilometers apart, these networks defeat the Earth curvature blockage that limits today s long-range weather radars and enables observing capabilities fundamentally beyond the operational state-of-the-art radars. These capabilities include multiple Doppler observations for mapping horizontal wind vectors, subkilometer spatial resolution, and rapid-update (tens of seconds) observations extending from the boundary layer up to the tops of storms. The small physical size and low-power design of these radars permits the consideration of commercial electronic manufacturing approaches and radar installation on rooftops, communications towers, and other infrastructure elements, leading to cost-effective network deployments. The networks can be architected in such a way that the sampling strategy dynamically responds to changing weather to simultaneously accommodate the data needs of multiple types of end users. Such networks have the potential to supplement, or replace, the physically large long-range civil infrastructure radars in use today.

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