Efficient Algorithm to Calculate a Time-Domain Echo Signal from Moving Targets Based on Physical Optics and the Application to an Autonomous Driving Simulation

An automotive radar simulator is proposed that can consider a dynamic driving scenario. The impulse response is computed based on the distance between the radar and the mesh position and the radar equation. The first-order physical optics technique is used to calculate the backscattering by the meshes, which can efficiently consider the shape of the target; however, because the radar operating frequency is very high, the required amount of mesh for discretization is large. Hence, the calculation of the time-domain echo signal requires considerable computational time. To reduce this numerical complexity, a new scheme is proposed to accurately approximate the time-domain baseband signal generated by the large number of meshes. The radar adopts the frequency modulated continuous waveform. Range-Doppler processing is used to estimate the range and relative velocity of the targets based on which simulation results are numerically verified for a driving scenario.

Adapting the COSP Radar Simulator to Compare GCM Output and GPM Precipitation Radar Observations

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolution. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the sub-grid scale. This work adapts Quickbeam to the dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate-to-heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land/ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP sub-grid algorithm by applying a more realistic, non-deterministic approach to assigning GCM grid box convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated sub-grid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud cover assumption.