Abstract
Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.
Highlights
A meteorological or climate observation network is composed of a set of weather stations
The MeteoGalicia network provides a flag to identify the quality of each value measured
The training set is used to generate the models by different methods (MLR, Multiple Imputation by Chained Equations (MICE)); the test set is used to validate the models with independent data, since the test set did not provide information to be able to build the models
Summary
A meteorological or climate observation network is composed of a set of weather stations They are usually placed at isolated points of a geographical zone, in order to determine the values of the meteorology and climatology variables of that area. Multiple variables such as air temperature, atmospheric pressure, wind speed and direction, relative humidity, rainfall, solar radiation, etc., are measured and registered by each station and the data is sent to a central database of the network to be processed and stored [1]. Failures in the measurement process may occur for any variable, and lack of and/or incorrect data can appear. Atmospheric conditions modify the extraterrestrial solar irradiation in such an ostensibly random manner that the global solar irradiation on the horizontal surface presents evolution randomness, with temporal and spatial variations due to weather conditions [2]
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