Spectrum-time estimation and processing (STEP) algorithm for improving weather radar data quality

The quality of moment data is essential to various radar functions that are used for weather detection, estimation, and forecasting. Obviously, improving data quality is essential for enhancing the performance of these functions. The Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU) and Enterprise Electronics Corporation (EEC) have recently developed a new Spectrum-Time Estimation and Processing (STEP) algorithm for the purpose of improving polarimetric radar moment estimation. The STEP algorithm consists of two major modules. The first module identifies and filters ground clutter in the spectral domain. The clutter identification algorithm applies a fuzzy-logic scheme associated with several characteristic parameters, including spectral phase variation and spectral power ratio; clutter filtering is based on a bi-Gaussian model (with one Gaussian spectrum for weather and another for clutter) and non-linear regression. The second module, in the time domain, mitigates the noise effect and estimates the radar moments using the recently developed multi-lag processing estimator. This paper presents the scheme of the STEP algorithm and data processing, and its implementation and testing on OU-PRIME (Polarimetric Radar for Innovations in Meteorology and Engineering), a C-band weather radar with high angular resolution. Results show that the combined spectrum-time domain processing has great potential in improving the moment data quality of weather radars.