Gap Filling and Quality Assessment of CO2 and Water Vapour Fluxes above an Urban Area with Radial Basis Function Neural Networks

Vertical turbulent fluxes of water vapour, carbon dioxide, and sensible heat were measured from 16 August to the 28 September 2006 near the city centre of Münster in north-west Germany. In comparison to results of measurements above homogeneous ecosystem sites, the CO2 fluxes above the urban investigation area showed more peaks and higher variances during the course of a day, probably caused by traffic and other varying, anthropogenic sources. The main goal of this study is the introduction and establishment of a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental conditions. We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative approach to fill data gaps in micrometeorological flux time series. We found that RBF networks are superior to multi-layer perceptron (MLP) neural networks in the reproduction of the highly variable turbulent fluxes. In addition, we enhanced the methodology in the field of quality assessment for eddy covariance data. An RBF neural network mapping system was used to identify conditions of a turbulence regime that allows reliable quantification of turbulent fluxes through finding an acceptable minimum of the friction velocity. For the data analysed in this study, the minimum acceptable friction velocity was found to be 0.15  m s−1. The obtained CO2 fluxes, measured on a tower at 65 m a.g.l., reached average values of 12 μmol m−2 s−1 and fell to nighttime minimum values of 3 μmol m −2 s−1. Mean daily CO2 emissions of 21 g CO2 m−2d −1 were obtained during our 6-week experiment. Hence, the city centre of Münster appeared to be a significant source of CO2. The half-hourly average values of water vapour fluxes ranged between 0.062 and 0.989 mmol m−2 s−1and showed lower variances than the simultaneously measured fluxes of CO2.