Workshop on. EddyUH: a software for eddy covariance flux calculation. Helsinki,

Transcription

1 Workshop on EddyUH: a software for eddy covariance flux calculation Helsinki, Eddy Covariance technique: flux quality criteria and random uncertainty Ivan Mammarella University of Helsinki, Dep. Of Physics, Division of Atmospheric Sciences

2 Outline Flux quality criteria Flux random uncertainty Methods to calculate random errors Flux uncertainty: some examples.

3 Flux quality criteria Variable Description Apply to Allowed values Flux instationarity Flux intermittency Friction velocity Kurtosis References Stationarity Co-variances FS < 0.30 Foken and Wichura, 1996 Intermittency Co-variances FI < 1 Mahrt et al., 1998 turbulence well developed? Is the probability distribution narrow Co-variances U * > Not discussed here Single variable time series 1 < KU < 8 Vickers and Mahrt, 1997 Skewness Is the probability distribution skewed Single variable time series -2 < SK < 2 Vickers and Mahrt, 1997 u /u *, T /T *, Integral turbulence characteristics Single variable time series ITC<0.3 Foken and Wichura, 1996 Spectra Power spectra Co-spectra 3 Visual inspection

4 Flux (non)stationarity (Foken and Wichura, 1996) Measure the quality of co-variances Often about 40% of data omitted due to these, especially during night The covariance calculated as a mean of the co-variances of 5min periods FS wx ' ' 5min wx ' ' wx ' ' 30min 30 min The covariance calculated for the whole period (e.g. 30min) x = u, T, CO2, H2O, etc. The flux is often considered non-stationary if FS>0.3 and the Reynolds decomposition is not valid 4

5 Flux intermittency (Mahrt et al. 1998) The flux is considered intermittent if FI>1. Often in stable cases. FI F F Sigma_F is the standard deviation of the 5min averaged co-variances F is the 30min covariance 5

6 Kurtosis and skewness Measure the quality of the time series of a single variable (Vickers and Mahrt, 1997) Kurtosis Measure of the peakness of the probability distribution High kurtosis: variance is due to infrequent extreme deviations (peaks in data) Hard flagged: 1< KU <8, soft flagged 2<KU<5 meanx KU 4 Skewness 4 ( ') Measure of the asymmetry of a probability distribution wikipedia SK meanx 3 3 ( ') Hard flagged: -2< SK <2, soft flagged -1<SK<1 6

7 Integral turbulence test Measure the quality of the time series of a single variable (Wichura and Foken, 1995) Is the turbulence well developed? Is the flux variance similarity followed? Normalized standard deviation for wind components and a scalar as a function of stability z d zd u L X L c uvw,, x c1 c1 * * 2 2 c An example from SMEAR III From Vesala et al. 2008a 7

8 If the measured normalized standard deviation deviates less than 30% from the model, the turbulence is considered well developed ITC ( x / X * ) ( mod x / ( X * ) x / mod X * ) mes From Lee et al. 2004, p.192 Originally from other papers. Parameter z/l c 1 C 2 w /u * 0>z/L> >z/L 2.0 1/8 u /u * 0>z/L> >z/L /8 T /T * 0.02<z/L< /4 0.02>z/L> / >z/L> /4-1>z/L 1.0-1/3 8

9 Spectral analysis >>>to verify that your EC system is working properly. N2O measurements by Campbell TDL White noise at high freq Laser drift at low freq (from Mammarella et al., 2010, BG) 9

10 Quality Flag system in EddyUH (Foken et al., 2004) Flag Steady of the state general test according Steady state test Integral Integral turbulence turbulence Horizontal Horizontal orientation orientation of the data quality characteristics according sonic anemometer Eq. (4.38) according to Equation characteristics of the sonic (4.38) Equation according (4.41) to Equation anemometer class Range class (4.41) range class Range % % ± % % ± % % ± % % ± % % ± % % ± % % 7 8 ± % % 8 8 ± > 1000 % 89 > % 9 8 > ± one flag equal to 9

11 Flux random uncertainty Flux uncertainty as random error (), is the measure of one standard deviation of 30 min covariance (turbulent flux) observed over an averaging period T. Random errors in flux measurements arise from a variety of sources. These include: 1) The stochastic nature of turbulence (Wesely and Hart, 1985), and associated sampling errors, including incomplete sampling of large eddies, and uncertainty in the calculated covariance between the vertical wind velocity (w) and the scalar of interest ( c ); 2) Errors due to the instrument system, including random errors in measurements of both w and c.

12 FLUX RANDOM ERROR ESTIMATION IN EDDYUH (FINKELSTEIN AND SIMS, 2001) 2 1 F n m m w' w'( p) c' c'( p) w' c'( p) c' w'( p) pm p m where and i1 i ip np 1 w' w'( p) w( t ) w w( t ) w n m200 n18000 This gives error variance of the covariance. In the calculation square root of this was used.

13 Other Methods for estimating Flux uncertainty IF 2 T wc ' ' 2 wc ' ' 2 instantaneous flux (Wyngaard, 1973) 1/ 2 SE N standard error (Vickers and Mahrt, 1997) 1 2 FM T Sw( f ) Sc ( f ) Swc( f ) df Fourier method (Rannik and Vesala, 1999)

14 CH4 flux random uncertainty (Peltola, MSc thesis) Instrumental noise according to (Billesbach, 2011) Standard deviation of covariance according to (Finkelstein & Sims, 2001)

15 CH4 flux uncertainty RMT-200 has the narrowest distribution

16 CH4 Flux uncertainty RMT-200 measures also small fluxes accurately

17 Flux uncertainty estimations

18 Particle flux data selection according to random flux error estimates and thresholds L < 0 Unstable F IF with estimated FIF with w F F 'c IF SP ' with % Z / u % passing % F< 0 100* v d /u * % passing % F< 0 100* v d /u * % passing % F< 0 RFE < RFE< RFE< z e U FSP 100* v d /u * L > 0 Stable % passing % F < 0 100* v d /u * % passing % F < 0 100* v d /u * % passing % F < 0 RFE < RFE < RE F< * v d /u *

19 An example of the same flux measured by two different EC systems (Rannik et al., 2006) Measured at SMEAR II Two EC systems located approx. at 30 m distance Measuring almost the same flux footprint But not exactly the same realisations of turbulence Turbulence not (fully) independent The uncertainty of annual C balance is 80 g(c) m-2, e.g. 30% of annual net exchange.

20 To take home. - Random errors tend to be quite large at the half-hourly time scale and cannot be ignored even in the context of annual flux integrals, especially as they propagate through to gap-filled and partitioned net ecosystem exchange (NEE) time series. - Typical random flux error for EC fluxes in the order of 20%. - Can be much larger depending on the instrumental noise (signal to noise ratio). - Random flux error should not be used as quality criteria (for filtering out the data).