Signal Processing and Radar Characteristics (SPARC) Simulator: A Flexible Dual-Polarization Weather-Radar Signal Simulation Framework Based on Preexisting Radar-Variable Data

This paper presents a novel, system-level, weather-radar time-series simulator able to ingest archived dual-polarization data and produce time-series data with the desired system and scanning parameters (e.g., antenna patterns, pulse repetition times, spatial sampling, waveform type). Time-series simulations are an important tool for testing signal processing techniques and can also be used to test the changes in system characteristics. The SPARC simulator ingests archived radar-variable data and produces dual-polarization time series with the desired system characteristics. First, the archived data are conditioned to fill in for missing or censored data. Then, based on the six meteorological variables, scattering centers are generated in a grid that matches the desired spatial sampling. For each scattering center, a spectrum shaping technique is used to create time-series data with the desired acquisition parameters. The effects of phase coding, pulse compression, range folding, waveform selection, and antenna patterns are incorporated in the data. In addition to conventionally sampled data, the simulator can produce range-oversampled data with the desired range correlation for range-time processing techniques. The results of applying diverse signal processing techniques and system designs on the simulated data show that the simulator can be used to qualitatively analyze the collective impact of a variety of those techniques on radar observables for any archived weather scenario.