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Autokorrelation. 0. 10. 20. Lag days. Gap-filling algorithm. Assumptions: NEE = NEE(R, T, VPD, t) + e NEE(R, T, VPD, t) NEE(R+ D R, T+ DT, VPD+ D VPD, t+ D t) The smaller D t and the more environmental constraints available the better. Gap-filling algorithm.
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Autokorrelation 0 10 20 Lag days Gap-filling algorithm Assumptions: • NEE = NEE(R, T, VPD, t) + e • NEE(R, T, VPD, t) NEE(R+DR, T+DT, VPD+DVPD, t+Dt) • The smaller Dt and the more environmental constraints available the better
Gap-filling algorithm • General type of approach same as Falge et al. (2001) • Differences: • Dynamic averaging window size (as small as possible better exploitation of temporal autocorrelation) • „Moving“ look-up table ( value to be filled always in the center of the class) • Combination of MDV and LUT methods
MDS method • Exploits meteorological drivers as much as available • Localized (exploits autocorrelation) • Fills all gaps • Gives tentative quality index • Yields error estimates for the flux • Easy to understand • Fast • Heuristic • Exhibits discontinuities with large gaps
Don‘t fill: Yes NEE present ? f_met=0, f_qc=0; fqc_ok=1 No Fill with average of available values: Yes Rg, T, VPD available with |dt| ≤ 7 days f_met = 1; f_win=|dt|; f_qc=1; fqc_ok=1 No Yes Rg, T, VPD available with |dt| ≤ 14 days f_met = 1; f_win=|dt|; f_qc=1; fqc_ok=1 Yes Rg available with |dt| ≤ 7 days f_met = 2; f_win=|dt|; f_qc=2; fqc_ok=0 Yes |dt| ≤ 1 hour f_met = 3; f_win=|dt|; f_qc=1; fqc_ok=1 Yes |dt| ≤ 1 day (& same hour of day) f_met = 3; f_win=|dt|; f_qc=2; fqc_ok=0 Yes Rg, T, VPD available with |dt| ≤ 21, 28, ..., 140 days f_met = 1; f_win=|dt|; f_qc=2 or 3; fqc_ok=0 Yes Rg available with |dt| ≤ 14, 21, ..., 140 days f_met = 2; f_win=|dt|; f_qc=2 or 3; fqc_ok=0 Yes |dt| ≤ 7, 14, ... days f_met = 3; f_win=|dt|; f_qc= 3; fqc_ok=0 Quality-controlled half-hourly data (storage, ustar,...)
