Robust Velocity Dealiasing for Weather Radar Based on Convolutional Neural Networks

Abstract

Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. A large aliasing range (ra) is important for surveillance but a high aliasing velocity (va) is also important to obtain storm dynamics unambiguously. However, the ra and va are inversely related to pulse repetition time. This “Doppler dilemma” is more challenging at shorter wavelengths. The proposed algorithm employs a CNN (convolutional neural network), which is widely used in image classification, to tackle the velocity dealiasing issue. Velocity aliasing can be converted to a classification problem. The velocity field and aliased count can be regarded as the input image and the label, respectively. Through a fit-and-adjust process, the best weights and the biases of the model are determined to minimize a cost function. The proposed method is compared against the traditional region-based method. Both methods show similar performance on mostly filled precipitation. For sparsely filled precipitation; however, the CNN demonstrated better performance since the CNN processes the entire scan at once while the region-based method processes only the limited adjacent area.