Characterizing the Uncertainty and Assessing the Value of Gap-Filled Daily Rainfall Data in Hawaii
Characterizing the Uncertainty and Assessing the Value of Gap-Filled Daily Rainfall Data in Hawaii
复制标题
描述夏威夷每日降雨量数据的不确定性并评估其价值
DOI：
10.1175/jamc-d-20-0007.1
复制
发表时间：
2020
影响因子：
3
通讯作者：
Mathew P. Lucas
中科院分区：
文献类型：
--
作者：
Ryan J. Longman;A. Newman;T. Giambelluca;Mathew P. Lucas
几乎所有的日降雨量时间序列都包含仪器记录中的空白。可以使用各种方法来使用相邻站点（预测站点）的观测值填充缺失数据。在这项研究中，五个计算简单的间隙填充方法-正常比（NR），线性回归（LR），反距离加权（ID），分位数映射（QM），和单一最佳估计（BE）-进行评估，以1）确定间隙填充夏威夷日降雨量的最佳方法，2）量化与填充不同大小的间隙相关的误差，以及3）在空间内插之前确定间隙填充的值。结果表明，在决定降水预报质量方面，目标站和预报站之间的相关性比站间的邻近性更重要。此外，包括雨/无雨校正的基础上，无论是站之间的相关性或站之间的接近显着减少了虚假的降雨量添加到一个充满数据集。对于较大的差距，相对中位误差范围为12.5%至16.5%，两种方法之间未发现统计学差异。对于次月间隙，当考虑填充和观察到的月总量之间的差异时，NR方法始终产生1天（2.1%）、15天（16.6%）和30天（27.4%）间隙的最低平均误差。结果表明，空间插值之前的间隙填充提高了网格化估计的整体质量，因为当20%的日常数据集被填充时，发现了更高的相关性和更低的性能误差，而不是在空间插值之前将这些数据留空。
Almost all daily rainfall time series contain gaps in the instrumental record. Various methods can be used to fill in missing data using observations at neighboring sites (predictor stations). In this study, five computationally simple gap-filling approaches—normal ratio (NR), linear regression (LR), inverse distance weighting (ID), quantile mapping (QM), and single best estimator (BE)—are evaluated to 1) determine the optimal method for gap filling daily rainfall in Hawaii, 2) quantify the error associated with filling gaps of various size, and 3) determine the value of gap filling prior to spatial interpolation. Results show that the correlation between a target station and a predictor station is more important than proximity of the stations in determining the quality of a rainfall prediction. In addition, the inclusion of rain/no-rain correction on the basis of either correlation between stations or proximity between stations significantly reduces the amount of spurious rainfall added to a filled dataset. For large gaps, relative median errors ranged from 12.5% to 16.5% and no statistical differences were identified between methods. For submonthly gaps, the NR method consistently produced the lowest mean error for 1- (2.1%), 15- (16.6%), and 30-day (27.4%) gaps when the difference between filled and observed monthly totals was considered. Results indicate that gap filling prior to spatial interpolation improves the overall quality of the gridded estimates, because higher correlations and lower performance errors were found when 20% of the daily dataset is filled as opposed to leaving these data unfilled prior to spatial interpolation.