### Matching simultaneous spatially disjunct time series statistically for accurate concentration gradients

*Lukas Siebicke*, Martina Hunner

^{1}^{1}, Thomas Foken

^{1}

^{1}Abteilung Mikrometeorologie, Universität Bayreuth

P 1.3

*in*Ecosystem Function

Estimates of Net Ecosystem Exchange require measurements of CO2 advection. The concentration gradients involved in horizontal advection are small and thus a challenge to measure. The single analyzer approach improves accuracy but suffers from poor temporal resolution. This study relies on a novel design with simultaneous spatially separated measurements. A statistical method is proposed to deal with inter instrument offsets due to residual calibration errors. We assume that, in a well mixed atmosphere the most likely concentration difference of two time series from two spatially separated points Pmax(delta c) is zero. The reasoning follows from the characteristics of turbulence and can be verified theoretically by Large Eddy Simulation (LES) as well as by classical experiments. Technically the observed Pmax(delta c) can be determined by kernel density estimation of the maximum of the probability density distribution and can be corrected for. Since the assumption is valid for well mixed conditions only, the time series have to be filtered according to a proposed "mixing index" which relies on the cross correlation of the timeseries normalized by the mean variance. Offsets can be interpolated for non mixed conditions if the offset drift is small relative to the absolute value.