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Radial velocity estimates provided by Doppler weather radar are criticalmeasurements used by operational forecasters for the detection and monitoringof life-impacting storms. The sampling methods used to produce thesemeasurements are inherently susceptible to aliasing, which produces ambiguousvelocity values in regions with high winds, and needs to be corrected using avelocity dealiasing algorithm (VDA). In the US, the Weather SurveillanceRadar-1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is aprocessing environment that provides a world-class VDA; however, this algorithmis complex and can be difficult to port to other radar systems outside of theWSR-88D network. In this work, a Deep Neural Network (DNN) is used to emulatethe 2-dimensional WSR-88D ORPG dealiasing algorithm. It is shown that a DNN,specifically a customized U-Net, is highly effective for building VDAs that areaccurate, fast, and portable to multiple radar types. To train the DNN model, alarge dataset is generated containing aligned samples of folded and dealiasedvelocity pairs. This dataset contains samples collected from WSR-88D Level-IIand Level-III archives, and uses the ORPG dealiasing algorithm output as asource of truth. Using this dataset, a U-Net is trained to produce the numberof folds at each point of a velocity image. Several performance metrics arepresented using WSR-88D data. The algorithm is also applied to othernon-WSR-88D radar systems to demonstrate portability to other hardware/softwareinterfaces. A discussion of the broad applicability of this method ispresented, including how other Level-III algorithms may benefit from thisapproach.

A Deep Learning-based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator