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  • Author or Editor: Sebastián M. Torres x
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Sebastián M. Torres
,
Christopher D. Curtis
, and
David Schvartzman

Abstract

With more weather radars relying on low-power solid-state transmitters, pulse compression has become a necessary tool for achieving the sensitivity and range resolution that are typically required for weather observations. While pulse compression is well understood in the context of point-target radar applications, the design of pulse compression waveforms for weather radars is challenging because requirements for these types of systems traditionally assume the use of high-power transmitters and short conventional pulses. In this work, Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern requirements are used to illustrate how suitable requirements can be formulated for the radar range weighting function (RWF), which is determined by the transmitted waveform and any range-time signal processing. These new requirements set bounds on the RWF range sidelobes, which are unavoidable with pulse compression waveforms. Whereas nonlinear frequency modulation schemes are effective at reducing RWF sidelobes, they usually require a larger transmission bandwidth, which is a precious commodity. An optimization framework is proposed to obtain minimum-bandwidth pulse compression waveforms that meet the new RWF requirements while taking into account the effects of any range-time signal processing. Whereas pulse compression is used to meet sensitivity and range-resolution requirements, range-time signal processing may be needed to meet data-quality and/or update-time requirements. The optimization framework is tailored for three processing scenarios and corresponding pulse compression waveforms are produced for each. Simulations of weather data are used to illustrate the performance of these waveforms.

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Igor R. Ivić
,
Christopher Curtis
, and
Sebastián M. Torres

Abstract

A radar antenna intercepts thermal radiation from various sources including the ground, the sun, the sky, precipitation, and man-made radiators. In the radar receiver, this external radiation produces noise that constructively adds to the receiver internal noise and results in the overall system noise. Consequently, the system noise power is dependent on the antenna position and needs to be estimated accurately. Inaccurate noise power measurements may lead to reduction of coverage if the noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated. Moreover, when an erroneous noise power is used at low-to-moderate signal-to-noise ratios, estimators can produce biased meteorological variables. Therefore, to obtain the best quality of radar products, it is desirable to compute meteorological variables using the noise power measured at each antenna position. In this paper, an effective method is proposed to estimate the noise power in real time from measured powers at each radial. The technique uses a set of criteria to detect radar range resolution volumes that do not contain weather signals and uses those to estimate the noise power. The algorithm is evaluated using both simulated and real time series data; results show that the proposed technique accurately produces estimates of the system noise power. An operational implementation of this technique is expected to significantly improve the quality of weather radar products with a relatively small computational burden.

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Sebastián M. Torres
,
Yannick F. Dubel
, and
Dusan S. Zrnić

Abstract

This paper describes the implementation of the staggered pulse repetition time (PRT) technique on NOAA's research and development WSR-88D in Norman, Oklahoma. The prototype algorithm incorporates a novel rule for the correct assignment of Doppler mean velocity that is needed to accommodate arbitrary stagger ratios. Description of the rule, consideration of errors, and choice of appropriate stagger ratios are presented. The staggered PRT algorithm is integrated with the standard processing on the WSR-88D, some details of which are included in the paper. A simple ground clutter canceller removes the pure complex time series mean (DC) component from autocovariance estimates; censoring of overlaid echoes and thresholding are equivalent to those used on the WSR-88D. Further, a cursory verification of statistical errors indicates good agreement with theoretical expectations. Although the staggered PRT algorithm operates in real time, it was advantageous to collect several events of staggered PRT time series data for further scrutiny. Results presented from one of the events demonstrate the potency of the staggered PRT to mitigate range and velocity ambiguities.

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Igor R. Ivić
,
Dušan S. Zrnić
, and
Sebastián M. Torres

Abstract

Demonstration of a method for improved Doppler spectral moment estimation is made on NOAA's research and development Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma. Time series data have been recorded using a commercial processor and digital receiver whereby the sampling frequency is several times larger than the reciprocal of the transmitted pulse width. The in-phase and quadrature-phase components of oversampled weather signals are used to estimate the first three spectral moments by suitably combining weighted averages in range with usual processing at fixed range locations. The weights are chosen in such a manner that the resulting signals become uncorrelated. Consequently, the variance of estimates decreases significantly as is verified by this experiment.

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William P. Kustas
,
Martha C. Anderson
,
Joseph G. Alfieri
,
Kyle Knipper
,
Alfonso Torres-Rua
,
Christopher K. Parry
,
Hector Nieto
,
Nurit Agam
,
William A. White
,
Feng Gao
,
Lynn McKee
,
John H. Prueger
,
Lawrence E. Hipps
,
Sebastian Los
,
Maria Mar Alsina
,
Luis Sanchez
,
Brent Sams
,
Nick Dokoozlian
,
Mac McKee
,
Scott Jones
,
Yun Yang
,
Tiffany G. Wilson
,
Fangni Lei
,
Andrew McElrone
,
Josh L. Heitman
,
Adam M. Howard
,
Kirk Post
,
Forrest Melton
, and
Christopher Hain

Abstract

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment.

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