A test pattern identification algorithm and its application to CINRAD/SA(B) data

• Published in: Advances in atmospheric sciences Vol. 31; no. 2; pp. 331 - 343
• Main Authors: Jiang, Yuan, Liu, Liping
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2014; Springer Nature B.V; National Meteorological Center, Beijing 100081%State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081; Nanjing University of Information Science and Technology, Nanjing 210044; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
• Subjects: Algorithms; Atmospheric Sciences; Data analysis; Data collection; Earth and Environmental Science; Earth Sciences; Echoes; Fluctuation; Fuzzy logic; Geophysics/Geodesy; Meteorology; Precipitation; Radar; Radar data; Radar echoes; Reflectivity; China, People's Rep; test pattern; radar data; quality control; fuzzy logic
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/s00376-013-2315-9

A variety of faulty radar echoes may cause serious problems with radar data applications, especially radar data assimilation and quantitative precipitation estimates. In this study, “test pattern” caused by test signal or radar hardware failures in CINRAD (China New Generation Weather Radar) SA and SB radar operational observations are investigated. In order to distinguish the test pattern from other types of radar echoes, such as precipitation, clear air and other non-meteorological echoes, five feature parameters including the effective reflectivity data percentage ( R Z ), velocity RF (range folding) data percentage ( R RF ), missing velocity data percentage ( R M ), averaged along-azimuth reflectivity fluctuation and averaged along-beam reflectivity fluctuation are proposed. Based on the fuzzy logic method, a test pattern identification algorithm is developed, and the statistical results from all the different kinds of radar echoes indicate the performance of the algorithm. Analysis of two typical cases with heavy precipitation echoes located inside the test pattern are performed. The statistical results show that the test pattern identification algorithm performs well, since the test pattern is recognized in most cases. Besides, the algorithm can effectively remove the test pattern signal and retain strong precipitation echoes in heavy rainfall events.