Cuntz, M.

Drüe, C.

Graf, A.

Mauder, M.

Rebmann, C.

Schmid, H.P.

Schmidt, M.

Steinbrecher, R.

English

Schmid, H. P.

Repository KITopen

On the need for uncertainty assessment of long-term eddy-covariance measurements

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On the need for uncertainty assessment of long-term eddy-covariance measurementsMauder M1, Cuntz M2, Drüe C3, Graf A4, Rebmann C2, Schmid HP1, Schmidt M4, Steinbrecher R11Institute for Meteorology and Climatology – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany2UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany3Trier University, Environmental Meteorology, Trier, Germany4Forschungszentrum Jülich, Institute for Bio- and Geosciences – Agrosphere, Jülich, GermanyTests on high-frequency data:•usage of internal quality tests and diagnostic flags (e.g. Campbell CSAT3, Li-Cor LI-7500).•spike test based on Median Absolute Deviation (MAD) for outlier or spike detection•screening of the high-frequency data for  instrumental plausibilityIntroduction:Eddy-covariance measurements are performed at several hundred sites all over the world on a long-term basis. The increasing demand on standardised and comprehensive quality flagging and uncertainty quantification of these fluxes has led to this review of established quality assessment procedures and the development of a strategy, focusing on automatically applicable tests on high-frequency data, expanding existing tests on statistics, fluxes and corrections, plus quantification of errors which will be used within the Helmholtz-project TERENO.Tests on statistics:assumptions of the EC method (simplified flagging after FW96):•stationarity of the means•ITC: well-developed turbulence•zero mean vertical wind velocity•interdependence of flux conversions and corrections on fluxesErrors and uncertainty:• instrumental noise:only present in firstterm of auto-covariancefunction → error propagation• random error:~  1 / √ # independent observations,Finkelstein & Sims (2001):the statistical variance of a covariance is expressed as function of its auto-covariances and cross-covariance→ detrending through high-pass filter before calculation of random error• systematic error:the total surface flux is not represented by thecovariance in case of large eddies;indirect error definition via energy balance ratio:• source area – representativeness:application of footprint model(Kormann & Meixner, 2001) on each averaging intervalFendt Graswang Lackenberg Selhausen Wetz‐steinτ 1/1277 5/1348 0/1044 1/1383 2/1395H 1/916 7/1121 21/882 9/1262 19/1153λE 2/820 5/850 7/762 13/1127 18/1059Fc 3/757 9/888 8/765 7/1113 2/1064Table 2: Results of the MAD-based outlier test (Papale et al. 2006) after application of the proposed flagging scheme: (number of detected values by the Papale et al. 2006 procedure / number of available data with flag 0 and 1. 1440 data records were tested for each site.Figure 2: CO2-fluxes for Graswang: without quality control (red circles), flagging according to FW96/Spoleto (orange triangles), filtering accordingto qualityassessment scheme proposed for TERENO (green squares)Figure 3: Relative systematic errors (%) for three test data sets determined from the energy balance ratioOverview of the test data sets Site name Operator Ecosystem Measurement height (a.g.l.*) Sensor combination Data periodFendt  KIT  grassland in pre-alpine valley 3.5 m CSAT3/ LI-7500 25/07/2010 – 23/08/2010 Graswang KIT  grassland in pre-alpine valley 3.5 m CSAT3/ LI-7500 25/07/2010 – 23/08/2010 Lackenberg KIT wind throw on low mountain range 9.0 m CSAT3/ LI-7500 25/07/2010 – 23/08/2010 Selhausen FZJ  agricultural land, sugar beet 2.5 m CSAT3/ LI-7500 01/06/2011 – 30/06/2011 Wetzstein MPI-BGC Spruce forest on low mountain range 30.0 m Solent-R3/ LI-6262 15/07/2006 – 13/08/2006  Table 1References:Finkelstein PL, Sims PF, 2001: Sampling error in eddy correlation flux measurements. J Geophys Res 106, 3503-3509Foken T, Wichura B, 1996: Tools for quality assessment of surface-based flux measurements. Agr For Met 78, 83-105Kormann R, Meixner FX, 2001: An analytical footprint model for non-neutral stratification. Bound Layer Meteor 99, 207-224Papale D, Reichstein M, Canfora E, Aubinet M, Bernhofer C, Longdoz B, Kutsch W, Rambal S, Valentini R, Vesala T, Yakir D, 2006: Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3, 571-583.Results:•discarding of momentum flux data due to the quality flagging is less than 10%•noise errors typically ≤1%•random errors 20-30%•highest data quality associated with smallest random errors•systematic errors: existence to be known, but difficult to account forConclusion:Combination of diagnostic flags, robust spike detection, interdependence of fluxes, and footprint analysis improved  the quality assessment strategy compared to established onesFigure 1: Relative random flux error (%) for the investigated fluxes (median, lower and upper quartiles) as a function of their quality flags (orange: highest quality= flag 0, red: medium quality=flag1).

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On the need for uncertainty assessment of long-term eddy-covariance measurements

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