Fusion of High-Resolution Reflectivity for a New Array Weather Radar

Abstract

Array Weather Radar (AWR) is a novel type weather radar equipped with a distributed phased array technology. As a new instrument with new technology, the AWR offers very high spatiotemporal resolution that enables detection of the fine-scale flow field and reflectivity of severe convective storms. This new AWR provides coordinated observations of a target from three subarrays of transmitter-receiver antenna units. This paper introduces a resolution enhancement concept that the very high range resolution of one subarray can be used to compensate lower azimuth and elevation resolutions of the other subarrays of the AWR. The resolution enhancement effect is estimated using data point density. A data fusion method is then presented to obtain a unified high-resolution reflectivity from the networked and coordinated AWR subarray observations. First, based on the reflectivity data from the AWR subarray volume scans, numbers of the data-point filling in both the azimuth and elevation directions are calculated. Then, the fusion of three subarray reflectivity data is achieved through the vertical and horizontal filling and merging in a common coordinate system. The final product of the fused high-resolution reflectivity is verified using both subjective and objective evaluations. The verification experiments included radar echoes of two simulated weather scenarios, a small-scale heavy precipitation and a tornado, along with a real precipitation event. The real precipitation event was observed from the AWR system that is installed and operational at the Changsha Huanghua International Airport. The performance of the proposed high-resolution reflectivity fusion method yields a 35% smaller root mean square error and an 11% increase in the correlation coefficient to the maximum extent. The real event result shows that the final fused high-resolution reflectivity depicted a more detailed and complete echo structure compared to the China New Generation Weather Radar network observation.