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Sebastián M. Torres and Christopher D. Curtis

1. Introduction Weather radars typically sample the received signals at a rate given by the inverse of the pulse width. Range-oversampling processing operates on time series samples that are acquired at an L -times-as-fast rate, where L is the range-oversampling factor. A generalized model for this type of processing involves two stages: transformation and estimation ( Torres and Curtis 2012 ). In the transformation stage, an L -by- M matrix of time series samples V is transformed as X

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Christopher D. Curtis

1. Introduction Time series simulation plays an important role in developing new weather radar signal processing algorithms. Having simulated data that accurately represent the weather signal characteristics is essential. In this paper, a few modifications to conventional simulators will be introduced to improve both accuracy and performance. The focus will be on simulating single-polarization weather radar data using a Gaussian spectral model for the weather (or ground clutter) signal

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John Y. N. Cho

study: Investigation of interference into 5 GHz weather radars from unlicensed national information infrastructure devices, Part II. Department of Commerce NTIA Rep. TR-11-479, 28 pp. [Available online at https://www.its.bldrdoc.gov/publications/download/11-479.pdf .] Cho , J. Y. N. , 2010 : Signal processing algorithms for the Terminal Doppler Weather Radar: Build 2. MIT Lincoln Laboratory Project Rep. ATC-363, 79 pp. [Available online at http

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John Y. N. Cho

1. Introduction The Terminal Doppler Weather Radar (TDWR) radar data acquisition (RDA) subsystem is being replaced as part of a broader Federal Aviation Administration (FAA) program to improve the supportability of the system. An engineering prototype RDA has been developed with a scalable, open-systems hardware platform ( Cho et al. 2004 ). With the dramatically increased computing power and more flexible transmitter control, modern signal processing algorithms can be implemented to improve

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Sebastián M. Torres and Christopher D. Curtis

to study the effects of range-time signal processing on the RWF for weather radars. Although the presentation thus far has been general, focusing on time series data that are oversampled in range is useful for illustrating the effects of advanced range-time processing techniques such as range oversampling with pseudowhitening ( Torres et al. 2004 ). A few cases are presented next that are typical when processing range-oversampled time series data. In this processing scenario, a digital matched

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John Y. N. Cho and Edward S. Chornoboy

) system signal processing. Preprints, 20th Int. Conf. on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology , Seattle, WA, Amer. Meteor. Soc., 5.5 . Sachidananda, M. , and Zrnić D. S. , 1999 : Systematic phase codes for resolving range overlaid signals in a Doppler weather radar. J. Atmos. Oceanic Technol. , 16 , 1351 – 1363 . 10.1175/1520-0426(1999)016<1351:SPCFRR>2.0.CO;2 Sachidananda, M. , and Zrnić D. S. , 2000 : Clutter filtering and

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Valery Melnikov and Dusan S. Zrnić

thermals ( section 2 ). Radar-derived properties of thermals are discussed in section 3 . Bragg scatter at S band is typically weak. Special processing procedures to increase the detectability of weak echoes are discussed in the next section. 2. Processing procedures to increase radar detectability We have conducted experiments with the S-band research KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma. To increase the detectability of weak echoes, a number of signal

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Francesc Junyent, V. Chandrasekar, and P. Kennedy

vertical at both wavelengths) radar operation, as well as high-polarization purity single-wavelength operation at S or X band [see Junyent et al. (2015) for a list of the main radar characteristics]. Dual-wavelength weather radar systems have historically been used to estimate precipitation properties through the use of the dual-wavelength ratio (DWR), defined as the ratio between reflectivity at the longer wavelength and reflectivity at the shorter wavelength. The DWR signal captures the difference

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Basivi Radhakrishna, Frédéric Fabry, and Alamelu Kilambi

to study birds’ migration and their behavior in different seasons. Though weather radars are vital tools for ornithologists to study birds’ migration at various spatial scales, the challenges in processing the enormous quantity of data that radar captures have constrained its application to a handful of studies ( Gauthreaux et al. 2003 ; Buler and Diehl 2009 ; Buler et al. 2012 ; Buler and Dawson 2014 ). Most of the algorithms for detecting bird echoes use radial velocity standard deviations

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Feng Nai, Sebastián Torres, and Christopher Curtis

number of samples per dwell, and the data windowing used in signal processing ( Torres and Curtis 2007 ). The relative location of the resolution volume in azimuth is determined by the azimuthal sampling interval (i.e., the spacing between consecutive radials or rays) and the location of the circulation center with respect to the nearest point on the sampling grid. Radar-observed velocities from resolution volumes that are physically smaller in the azimuthal dimension are more likely to faithfully

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