Practically all records of eddy‐covariance flux measurements are affected by gaps, caused by several reasons. In this work, we propose analog period (AP) methods for gap‐filling, and test them for filling gaps of latent heat flux at five AmeriFlux sites. The essence of the methods is to look for periods in the record that bear a strong resemblance, in the variable to be filled, to the periods immediately before and after the gap. Similarity between periods is gauged by the coefficient of determination, and the search for similar periods and their ranking is straightforward. The methods are developed in a univariate version (that uses only the latent heat flux data series itself) and several multivariate ones, that incorporate sensible heat flux, ground heat flux and net radiation data. For each set of independent variables used for gap‐filling, the methods are tested against an existing gap‐filling procedure with similar data requirements. Thus, the univariate version is tested against the mean diurnal variation method, and the multivariate versions are tested against corresponding simple and multiple linear regression techniques that use energy‐budget data, and in one case the evaporative fraction as well. In our tests, the proposed univariate version performs better than the mean diurnal variation method, and the multivariate versions perform better than simple/multiple linear regression methods. An often used available computer package, REddyProc, was also tested as a basis of comparison. In general, the proposed methods (in univariate and multivariate versions) and simple/multiple linear regressions performed better than REddyProc. For the datasets analysed, gap filling via the evaporative fraction method performed poorly.

中文翻译：

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潜热通量测量的模拟填充的模拟周期方法

实际上，涡动协方差通量测量的所有记录都受到间隙的影响，这是由多种原因引起的。在这项工作中，我们提出了用于间隙填充的模拟周期（AP）方法，并对它们在五个AmeriFlux站点的潜热通量间隙进行了填充进行测试。这些方法的本质是在记录中查找与要填充的变量之间，紧接缺口前后的期间非常相似的期间。期间之间的相似性由确定系数来衡量，搜索相似期间及其排名很简单。这些方法以单变量版本（仅使用潜热通量数据系列本身）和几种多元变量（包含显热通量，地热通量和净辐射数据）开发。对于用于填充缺口的每组自变量，这些方法是根据现有的具有类似数据要求的填充程序进行测试的。因此，单变量版本针对平均日变化方法进行了测试，多变量版本针对使用能量预算数据的简单和多重线性回归技术进行了测试，在一种情况下还使用了蒸发分数进行了测试。在我们的测试中，提出的单变量版本的性能优于平均日变化方法，而多元版本的性能优于简单/多元线性回归方法。还比较了常用的计算机软件包REddyProc，作为比较的基础。通常，所提出的方法（单变量和多变量版本）和简单/多个线性回归的性能均优于REddyProc。对于分析的数据集，