Novel Tornado Detection Using an Adaptive Neuro-Fuzzy System with S-Band Polarimetric Weather Radar

Tornado debris signatures (TDS) exhibited in polarimetric measurements have the potential to facilitate tornado detection. The upgrade of the network of S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) to dual polarization was completed recently. Therefore, it is timely to develop a tornado detection algorithm that capitalizes on TDS and integrates with other existing signatures observed in the velocity (shear signature) and Doppler spectrum (spectral signature) fields. In this work, the analysis indicates that TDS are not always present with shear and spectral signatures. A neuro-fuzzy tornado detection algorithm (NFTDA) using the Sugeno fuzzy inference system is developed to consider the strength of different tornado signatures that are characterized by operationally available data of differential reflectivity, cross-correlation coefficient, velocity difference, and spectrum width with the goal of reliable and robust detection. The performance is further optimized using a training procedure based on a neural network. The performance of NFTDA is evaluated using polarimetric WSR-88D data from 17 tornadoes with enhanced Fujita (EF) scale ratings ranging from EF-0 to EF-4 and distance from 16 to 133 km to the radar. NFTDA performs well with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 86%, 11%, and 78%, respectively. Moreover, a computationally efficient method is introduced to analyze the sensitivity of the tornado signatures. It is demonstrated that even though TDS play a less important role than the other two signatures, TDS can help improve the detection, especially during the later stage of a tornado, when the shear and spectral signatures become weaker.