An analog period method for gap‐filling of latent heat flux measurements

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. This study presents new methods for gap filling of latent heat flux measurements, based on the relationship between periods of time series that are similar to antecedent and posterior periods of the gap. We found that this method was better than other methods like REddy, MDV and methods based on linear regression. The univariate version of this unique method has the advantage of not depending on other measurements to fill the gaps.