Robust Velocity Dealiasing for Weather Radar Based on Convolutional Neural Networks

Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. A large aliasing range ($r_{\mathrm{a}}$) is important for surveillance but a high aliasing velocity ($v_{\mathrm{a}}$) is also important to obtain storm dynamics unambiguously. However, the $r_{\mathrm{a}}$ and $v_{\mathrm{a}}$ 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.