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This paper introduces the Spectrum-Time Estimation and Processing (STEP) algorithm developed in the Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU). The STEP processing framework integrates three novel algorithms recently developed in ARRC: spectrum clutter identification, bi-Gaussian clutter filtering, and multi-lag moment estimation. The three modules of STEP algorithm fulfill three functions: clutter identification, clutter filtering and noise reduction, respectively. The performance of STEP has been evaluated using simulated data as well as real data collected by the C-band polarimetric research radar OU-Polarimetric Radar for Innovations in Meteorology and Engineering. Results show that STEP algorithm can effectively improve quality of polarimetric weather data in the presence of ground clutter and noise. [ABSTRACT FROM AUTHOR]

When the National Weather Radar Testbed (NWRT) was installed in 2004, a single-channel digital receiver was implemented so that the radar could mimic typical Weather Surveillance Radar (WSR) version 1988 Doppler (WSR-88D) capability. This, however, left unused eight other channels, built into the antenna. This paper describes the hardware instrumentation of a recently completed project that digitizes the radar signals produced by these channels. The NWRT is the nation's first phased array devoted to weather observations, and this testbed serves as an evaluation platform to test new hardware and signal processing concepts. The multichannel digital data will foster a new generation of adaptive/fast scanning techniques and space-antenna/interferometry measurements, which will then be used for improved weather forecasting via data assimilation. The multichannel receiver collects signals from the sum, azimuth-difference, elevation-difference, and five broad-beamed auxiliary channels. One of the major advantages of the NWRT is the capability to adaptively scan weather phenomena at a higher temporal resolution than is possible with the WSR-88D. Access to the auxiliary channels will enable clutter mitigation and advanced array processing for higher data quality with shorter dwell times. Potential benefits of higher quality and higher resolution data include: better understanding of storm dynamics and convective initiation; better detection of small-scale phenomena, including tornadoes and microbursts; and crossbeam wind, shear, and turbulence estimates. These items have the distinct possibility to ultimately render increased lead time for warnings and improved weather prediction. Finally, samples of recently collected data are presented in the results section of this paper. [ABSTRACT FROM PUBLISHER]

This study presents a new identification and mitigation scheme of second trip contamination for pulsed Doppler polarimetric weather radars with the ability of random phase coding. This scheme can be easily implemented in a magnetron radar without any hardware changes. For relatively weak contamination, identification and mitigation are based on a multilag processing method, which uses multiple lags of both the auto- and cross-correlation functions to estimate radar moments. For relatively strong contamination, instantaneous phase variations of horizontal and vertical polarization channels are combined into a simple fuzzy-logic scheme to complete the identification. Data from the C-band OU-PRIME radar are used to demonstrate the effectiveness of the proposed scheme for identification and mitigation of second-trip echoes. [ABSTRACT FROM AUTHOR]