Dealiasing Dual-Pol-QC’ed Radar Velocity in Real-time Based on HRRR 3D Winds

Doppler weather radar has been pivotal in advancing convective-scale Numerical Weather Prediction (NWP). However, the effective use of radar data is highly dependent on data quality. Key sources of contamination include velocity ambiguities, ground clutter from normal and anomalous propagation, and range ambiguities. Velocity ambiguities arise when the true velocity exceeds the Nyquist velocity, necessitating the correction of aliased radial velocities before they can be used quantitatively.

The dealiasing problem involves finding the integer multiplier required to resolve Nyquist aliasing ambiguities. Over the years, various dealiasing algorithms have been developed, ranging from traditional methods to recent AI/ML-based approaches. This study introduces a three-step reference check algorithm, using High-Resolution Rapid Refresh (HRRR) 3D wind forecasts as a reference. The process is as follows: First, calculate the difference between observed and model-produced radial velocities at the radar site over the entire domain to estimate the multiplier. Second, derive Velocity-Azimuth Display (VAD) profiles to further refine the velocity correction. Last, perform a local tuning by calculating the average radial velocity within a specified window and adjusting the multiplier accordingly.

This algorithm has been applied to NOAA National Severe Storms Laboratory (NSSL) Dual-Polarization Quality Controlled (DPQCed) radar velocity products. Its performance was compared with the region-based algorithm and unwrap algorithm available in the Python ARM Radar Toolkit (Py-ART) and a dealiasing technique used in NCEP BUFR data. The results are promising, and a real-time product with corrected radial velocity data is now available. The corrected velocities will be assimilated into NOAA’s Rapid Refresh Forecast System (RRFS) and the Real-Time Mesoscale Analysis (RTMA) system for further evaluation.