Quality assurance for micrometeorological field experiments

In general quality assurance for eddy covariance measurements of micrometeorological field experiments comprises two major components: Sensor intercomparison studies and quality assessment of the flux data.

3.1.1 Eddy covariance sensor intercomparison

Mauder et al. (2005, Appendix B) and Mauder et al. (2006b, Appendix C) report the results of the eddy covariance sensor comparisons of EVA-GRIPS 2002 and EBEX-2000. The majority of sonic anemometer comparisons during EBEX-2000 have a coef-ficient of determination R² > 0.95 and have a regression coefcoef-ficient close to 1.00. Of immediate concern are the data from sensors where R² < 0.95 or exceed a 5% threshold in the slopes of the regression lines (regression coefficients 0.95 - 1.05). These are the wind statistics from the Kaijo-Denki TR90-AH and the ATI K-probe, friction velocities u* from the Metek USA-1 and the R.M. Young 81000 and the temperature statistics from the Gill Solent-HS. After wind-tunnel tests and an additional field intercomparison with the Kaijo-Denki TR90-AH, a correction of -13% is recommended for all vertical eddy covariance fluxes measured by this instrument. In opposition to a study of Kaimal et al. (1990), a single-path correction factor f of 16% is found to be preferable for the ATI K-probe instead of 20% to improve its comparison. Wake effects downstream of a sonic transducer or other supporting structures are suspected as reasons for the problems of the Metek USA-1 and the R.M. Young 81000. A polynomial equation of third degree was proposed for the correction of the raw sonic temperature data of the Solent-HS as a function of an independent slow-response reference temperature measurement (Mauder et al., 2006b, Appendix C). An excellent agreement was found between sonic anemome-ters of type Campbell CSAT3 and NCAR’s UW sonics, which justifies the choice of the first one as reference instrument.

More problems were met regarding the hygrometers that were tested in the intercom-parisons. Day to day drifts in the calibration coefficients of some of the KH20 krypton hygrometers were observed during EBEX-2000, which can partly be explained by scal-ing effects of the optical windows (Tanner and Campbell, 1985). Larger changes in the behaviour of a KH20 were attributed to its sensitivity to condensing humidity inside the sensor’s enclosure in combination with corrosion of electrical contacts. From the EVA-GRIPS 2002 comparison one can see that deviations within the group of KH20s are larger than within the group of LI-7500s. However, still unexplained deviations of the water vapour flux measurements on the order of 10% to 20% remain for both instru-ments, which can probably be reduced through frequent recalibrations as it was done before and after LITFASS-2003 (Mauder et al., 2005, Appendix B).

RESULTS 13 3.1.2 Quality assessment of eddy covariance flux estimates

In order to make a uniform data analysis of the eddy covariance measurements pre-sented in this thesis possible, the comprehensive software package TK2 (Mauder and Foken, 2004) was developed at the University of Bayreuth. It includes quality tests of the raw data and all necessary corrections of the covariances (Lee et al., 2004), as well as quality tests for the resulting turbulent fluxes (Foken and Wichura, 1996; Foken et al., 2004). Most of the processing steps are well described in the literature. They were brought into a reasonable sequence (Figure 1) and some own modifications and adapta-tions were made (Mauder et al., 2005, Appendix B).

Before the calculation of typically 30 minute covariances the high frequency dataset is screened using the algorithm of Vickers and Mahrt (1997) to eliminate spikes in the time series. An eventual time delay between two time series from two separate instru-ments, e.g. a sonic anemometer and a hygrometer, is determined automatically by cross-correlation analysis for each averaging interval. Inherent to turbulence measurements are deficiencies which cause more or less important violations of assumptions to the eddy covariance method necessitating a set of corrections to the calculated covariances.

Figure 1: Processing scheme of the software package TK2 developed at the University of Bayreuth (Mauder and Foken, 2004). It performs all post-processing of turbulence measurements and produces quality assured turbulent fluxes. (Figure taken from Mauder et al., 2005, Appendix B, Figure 1)

14 RESULTS

The first correction to be conducted is the crosswind correction of the sonic temperature (Schotanus et al., 1983) because it has to be applied to data in the sonic anemometer coordinate system (Liu et al., 2001). Then the coordinate system of the sonic measure-ments is transformed into a coordinate system parallel to the mean stream lines, using the planar fit method (Wilczak et al., 2001) or alternatively using the double rotation method (Kaimal and Finnigan, 1994). The correction according to Tanner et al. (1993) has to be applied to the data obtained by krypton hygrometers due to a cross sensitivity to oxygen of these instruments.

A correction for high frequency spectral loss is necessary for several reasons (Moore, 1986). Turbulent fluxes are corrected for line averaging of sonic anemometers and hy-grometers, spatial separation of sonic anemometers, hygrometers and fast response tem-perature sensors, and dynamic frequency response of fast response temtem-perature sensors.

If the longitudinal sensor separation was already corrected by the time delay corrected calculation of the covariance, only the lateral fraction of the sensor separation has to be corrected. Transfer functions are convoluted with parameterised spectra of vector and scalar quantities proposed by Moore (1986) for stable stratification and by Højstrup (1981) for unstable stratification. As the parameterisations of stable cospectra in Moore (1986) are erroneous (Moncrieff et al., 1997), cospectral models by Kaimal et al. (1972) were used instead for the whole stability range.

Since sonic anemometers do not directly measure temperature but the speed of sound, the humidity effect of this parameter was corrected according to the paper of Schotanus et al. (1983). To determine turbulent fluxes of air constituents like H2O, a correction according to Webb et al. (1980) or Liu (2005) is necessary. This procedure incorporates two aspects. The first is the conversion of the volume-related measurement of the content of a scalar quantity, e.g. absolute humidity [kg m^-3], into a mass-related parameter like specific humidity or mixing ratio [kg kg^-1]. The second aspect is the cor-rection of a positive vertical mass flow, which results from the mass balance equation, because vertical velocities of ascending parcels have to be different from descending ones due to density differences (Webb et al., 1980; Fuehrer and Friehe, 2002). Since some of the processing steps are interdependent, the whole sequence of flux corrections and conversions is iterated. The resulting flux data are tested on stationarity and devel-opment of turbulence according to the procedures proposed by Foken and Wichura (1996) in an updated version of Foken et al. (2004). The output of this entire procedure is quality-assured estimates of turbulent fluxes.

This scheme of quality assessment was applied to the corrected flux estimates of LITFASS-2003. It provides objective criteria to give an overview of the availability of highest quality latent heat flux data during daytime (0600 and 2000 UTC) for the LIT-FASS-2003 experiment (Figure 2). Most of the stations show an average availability of more than 80% of highest quality latent heat flux data. Significantly lower percentages on May 19, May 23 and June 5, 2003 for all stations are mainly caused by rain events.

RESULTS 15

Figure 2: Availability of highest quality latent heat flux data between 0600 and 2000 UTC for the LIT-FASS-2003 experiment, May 19 to June 17, 2003. Black boxes indicate days of less than 50% availabil-ity, including days with instrumental malfunction. (Figure taken from Mauder et al., 2005, Appendix B, Figure 2)

Lower data quality on May 21 and 22, 2003 and on June 6 and 7, 2003 can be attributed to distinct cumulus convection on the back side of a cold front, which causes instation-ary conditions. Data gaps due to instrumental malfunction are the reason that less than 50% of the latent heat flux data were of highest quality over several days at stations A1, A2, and SS. Partially lower data quality for site HV has to be noticed indicating resolu-tion problems of its data acquisiresolu-tion system.