A Realistic Dual-Polarization Radar Time-Series Simulator Based on Archived Data

As new technology becomes available, weather-radar researchers devise systems with more capabilities that allow for new, sophisticated algorithms and techniques. The implementation of these techniques and the collection of good weather data can be time-consuming and expensive. A realistic simulator that ingests existing weather data could be used to explore the effects of several techniques in a wide variety of weather scenarios. This work presents a versatile, two-dimensional weather-radar time-series scenario simulator able to ingest archived dual-polarization data and produce Level-I data with the desired scanning parameters (e.g., pulse-repetition times, spatial resolution, waveform type). First, the archived data are conditioned, and missing or censored data are produced. 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 the antenna pattern 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 (e.g., adaptive pseudowhitening). Furthermore, it adjusts the noise floor level to set the sensitivity based on the selected waveform power. The results of applying diverse signal processing techniques on the simulated data shows that the simulator can be used to qualitatively analyze the impact of a variety of those techniques on radar observables for any archived weather scenario.