Filling data gaps in long-term solar UV monitoring by statistical imputation methods

Abstract

Knowledge of long-term time trends of solar ultraviolet (UV) radiation on ground level is of high scientific interest. For this purpose, precise measurements over a long time are necessary. One of the challenges solar UV monitoring faces is the permanent and gap-free data collection over several decades. Data gaps hamper the formation and comparison of monthly or annual means, and, in the worst case, lead to incorrect conclusions in further data evaluation and trend analysis of UV data. For estimating data to fill gaps in long-term UV data series (daily radiant exposure and highest daily irradiance), we developed three statistical imputation methods: a model-based imputation, considering actual local solar radiation conditions using predictors correlated to the local UV values in an empirical model; an average-based imputation based on a statistical approach of averaging available local UV measurement data without predictors; and a mixture of these two imputation methods. A detailed validation demonstrates the superiority of the model-based imputation method. The combined method can be considered the best one in practice. Furthermore, it has been shown that the model-based imputation method can be used as an useful tool to identify systematic errors at and between calibration steps in long-term erythemal UV data series.