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

被引:3
|
作者
Oh, Young-A [1 ]
Lee, DaeHyung [1 ]
Jung, Sung-Hwa [2 ]
Nam, Kyung-Yeub [2 ]
Lee, GyuWon [1 ,3 ]
机构
[1] Kyungpook Natl Univ, Res & Training Team Future Creat Astrophysicists, Dept Astron & Atmospher Sci, 80 Daehakro, Bukgu 41566, Daegu, South Korea
[2] Korea Meteorol Adm, Weather Radar Ctr, Radar Anal Div, 61 16 Gil Yeouidaebangro, Seoul 07062, South Korea
[3] Kyungpook Natl Univ, Ctr Atmospher REmote Sensing CARE, 80 Daehakro, Bukgu 41566, Daegu, South Korea
关键词
DIFFERENTIAL REFLECTIVITY; CAMPAIGN; ERRORS;
D O I
10.1155/2016/9716535
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
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 (Z(H)) was corrected using a self-consistency principle. The attenuation correction of Z(H) and the differential reflectivity (Z(DR)) were performed by a path integration method. After attenuation correction, Z(H) and Z(DR) were significantly improved, and their scatter plots matched well with the theoretical relationship between Z(H) and Z(DR). 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 R(Z(H)). However, the improvement with R(Z(H,) Z(DR)) 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.
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页数:20
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