A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data
A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data
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DOI：
10.1175/jhm-d-12-027.1
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发表时间：
2013-06-01
影响因子：
3.8
通讯作者：
Lundquist, Jessica D.
中科院分区：
文献类型：
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作者：
Henn, Brian;Raleigh, Mark S.;Lundquist, Jessica D.
近地表气温观测经常有缺失数据的周期，许多使用这些数据集的应用程序需要填充所有缺失的周期。有多种方法可以填补缺失的数据，但这些方法的相对准确性尚未得到评估。在这项对比研究中，使用了五种技术来填充缺失的温度数据：经验正交函数(EOFs)形式的时空相关性、时间序列日内插和基于递减率的三种不同填充方式。该方法的验证使用了来自五个地区的复杂地形上的每小时地表温度观测集。填补缺失数据最准确的方法取决于可用站点的数量和缺失数据的小时数。如果至少有16个站点可用，使用EOF重建的时空相关性是最准确的。当只有一个或两个站点可用或间隔1小时时，时间内插法是最准确的方法。基于流失率的填充对于中间数量的站点是最准确的。研究发现，延迟率和EOF方法的精确度对台站的垂直间隔以及它们之间的相关程度很敏感，这也解释了一些地区业绩的差异。水平距离与方法性能的相关性不那么显著。根据这些发现，提出了根据缺失数据的持续时间和站点数量来选择填充方法的指导原则。
Near-surface air temperature observations often have periods of missing data, and many applications using these datasets require filling in all missing periods. Multiple methods are available to fill missing data, but the comparative accuracy of these approaches has not been assessed. In this comparative study, five techniques were used to fill in missing temperature data: spatiotemporal correlations in the form of empirical orthogonal functions (EOFs), time series diurnal interpolation, and three variations of lapse rate-based filling. The method validation used sets of hourly surface temperature observations in complex terrain from five regions. The most accurate method for filling missing data depended on the number of available stations and the number of hours of missing data. Spatiotemporal correlations using EOF reconstruction were most accurate provided that at least 16 stations were available. Temporal interpolation was the most accurate method when only one or two stations were available or for 1-h gaps. Lapse rate-based filling was most accurate for intermediate numbers of stations. The accuracy of the lapse rate and EOF methods was found to be sensitive to the vertical separation of stations and the degree of correlation between them, which also explained some of the regional differences in performance. Horizontal distance was less significantly correlated with method performance. From these findings, guidelines are presented for choosing a filling method based on the duration of the missing data and the number of stations.