Bias correction of radar rainfall estimates based on a geostatistical technique
Bias correction of radar rainfall estimates based on a geostatistical technique
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基于地统计技术的雷达降雨估计偏差校正
DOI：
10.2306/scienceasia1513-1874.2012.38.373
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发表时间：
2012
期刊：
影响因子：
1.2
通讯作者：
S. Chumchean
中科院分区：
文献类型：
--
作者：
R. Hanchoowong;U. Weesakul;S. Chumchean
不同类型的降雨特征具有不同的雨滴谱分布。由雷达测量的DSD对Z-R关系的参数有根本性的影响;使用气候学的Z-R关系来估计雷达降雨量可能导致雷达降雨量估计的偏差。本文试图消除偏差的雷达降水估计的来源，由于不确定的Z-R关系，通过应用当地的偏差调整因子，具有相同的气候降雨特征的区域。记录的历史日降雨量数据，从188个均匀分布的雨量计位于雷达伞下及其附近，用于描述的气候空间格局的降雨量在研究区域的基础上克里格方法。结果发现，一个简单的克里金技术与各向同性Bessel-J半变异函数模型是最好的方法来分类的气候模式的降雨特征的研究区域，因此，它已被用于识别局部偏差校正区的建议每小时的局部偏差（HLB）校正方法。从随机选择的500个经校准和交叉验证的已验证数据集的量规中评估了具有不同复杂程度的不同偏倚校正方法的性能。这些方法包括平均场偏差校正（MFB），每小时平均场偏差校正（HMFB），每小时范围相关平均场偏差校正（HRMFB）和HLB校正。四十四个降雨事件记录在2003 - 2005年从位于泰国Nakhon-Ratchasima省的S波段Pimai雷达，和50个自动雨量计在这项研究中使用。研究结果显示，平均而言，HLB方法可以提高16.7%，14.3%，2.8%，0.4%的雷达降水估计的准确性校准的仪器和11.8%，10.2%，9.4%，4.1%的交叉验证的仪器相比，无偏校正，MFB，HMFB和HRMFB方法，分别为。
Various types of rainfall characteristic have different rainfall drop size distributions (DSDs). DSDs that are measured by radar have a fundamental influence on parameters of the Z-R relationship; using a climatological Z-R relationship to estimate radar rainfall can lead to bias in radar rainfall estimates. This paper attempts to remove the source of bias in radar rainfall estimates due to an uncertain Z-R relationship by applying a local bias adjustment factor to a region that has the same climatological rainfall characteristic. Recorded historical daily rainfall data from 188 uniformly distributed rain gauges located under the radar umbrella and its vicinity were used for describing the climatological spatial pattern of rainfall in the study area based on kriging approaches. It was found that a simple kriging technique with the isotropic Bessel-J semivariogram model was the best method to classify climatological patterns of rainfall characteristic of the study area and therefore it has been used for identifying local bias correction areas of the proposed hourly local bias (HLB) correction method. Performances of different bias correction methods with various levels of complexity were evaluated from 500 of the calibrated and cross-validated gauges of the validated data set, selected randomly. These methods include mean field bias correction (MFB), hourly mean field bias correction (HMFB), hourly range dependent mean field bias correction (HRMFB), and HLB correction. Forty-four rainfall events recorded during 2003-2005 from the S-band Pimai radar located in Nakhon-Ratchasima Province, Thailand, and 50 automatic rain gauges were used in this study. The results of this study showed that, on average, the proposed HLB method could improve accuracy of radar rainfall estimates by 16.7%, 14.3%, 2.8%, 0.4% for the calibrated gauges, and by 11.8%, 10.2%, 9.4%, 4.1% for the cross-validated gauges when compared to non-bias corrected, MFB, HMFB, and HRMFB methods, respectively.