The establishment of optimal ground-based radar datasets by comparison and correlation analyses with space-borne radar data

Abstract

Calibration error, due to ground-based radar (GR) hardware parameters, introduces biases to GR reflectivity factor data. As a result of this, GR observations from the China new-generation weather radar network have sometimes been found to be inconsistent, even when observing precipitation in the same area. In this paper a method, called the Available Best Comparable Data set (ABCD) method, is proposed. The method evaluates the radar reflectivity bias in GR datasets by using TRMM Standard Product (TSP) 2A25 data from Tropical Rainfall Measuring Mission (TRMM) satellite-borne precipitation radar (PR). The analysis is supported by simultaneous measurement data from two precipitation events that occurred in the middle and lower reaches of the Yangtze River between June and July 2010; data were collected during these events by three radars in Nanjing, Changzhou and Nantong (Jiangsu province, China). The ABCD method could correct deviations in the GR reflectivity factors; the GR-corrected values were consistent with those observed by automatic weather stations. The method can be applied to improve the quality of precipitation data products from the GR network and their quantitative application for the southern region in China.