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Advances in Meteorology
Volume 2016, Article ID 9716535, 20 pages
http://dx.doi.org/10.1155/2016/9716535
Research Article

Attenuation Correction Effects in Rainfall Estimation at X-Band Dual-Polarization Radar: Evaluation with a Dense Rain Gauge Network

1Department of Astronomy and Atmospheric Sciences, Research and Training Team for Future Creative Astrophysicists and Cosmologists, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea
2Radar Analysis Division, Weather Radar Center, Korea Meteorological Administration, 61 16-Gil Yeouidaebangro, Dongjakgu, Seoul 07062, Republic of Korea
3Center for Atmospheric REmote Sensing (CARE), Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea

Received 25 December 2015; Accepted 24 April 2016

Academic Editor: Hiroyuki Hashiguchi

Copyright © 2016 Young-A Oh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The effects of attenuation correction in rainfall estimation with X-band dual-polarization radar were investigated with a dense rain gauge network. The calibration bias in reflectivity () was corrected using a self-consistency principle. The attenuation correction of and the differential reflectivity () were performed by a path integration method. After attenuation correction, and were significantly improved, and their scatter plots matched well with the theoretical relationship between and . The comparisons between the radar rainfall estimation and the rain gauge rainfall were investigated using the bulk statistics with different temporal accumulations and spatial averages. The bias significantly improves from 70% to 0% with . However, the improvement with was relatively small, from 3% to 1%. This indicated that rainfall estimation using a polarimetric variable was more robust at attenuation than was a single polarimetric variable method. The bias did not show improvement in comparisons between the temporal accumulations or the spatial averages in either rainfall estimation method. However, the random error improved from 68% to 25% with different temporal accumulations or spatial averages. This result indicates that temporal accumulation or spatial average (aggregation) is important to reduce random error.