Monday, 9 July 2012
St. George (Westin Copley Place)
There are many sources of bias which can affect eddy covariance measurements of turbulent fluxes. Shipboard measurements often have significant additional biases from platform motion and from air flow distortion. Spectral analysis can be used to identify biases as a function of the frequency. It is common to visually inspect cospectra or ogive curves (the cumulative integral of the cospectra) as a means of quality control of individual flux estimates. This becomes increasingly inefficient as the volume of data increases. Here we present an automated algorithm that quantifies the deviations of a measured ogive from the functional form predicted by Monin Obukhov similarity theory. We apply the algorithm to data obtained from long-term flux measurements made from a research ship. Of particular interest is distortion of the cospectra within the frequency band at which the ship moves in response to waves. The bias here is likely due to imperfect motion correction and ship attitude-correlated flow distortion. A second region of particular interest is at periods greater than several minutes, when non-turbulent processes can introduce low frequency contamination of the cospectra. These include environmental factors such as mesoscale variability, platform-induced contamination (for example a ship manoeuvre during the flux-averaging interval), and non-stationarity of the turbulence. The benefits of an automated method of quality control are a significant saving of time, objectivity and consistency of the quality control procedure, and that the nature of the biases may be identified more reliably and their magnitudes determined and related to probable sources. In some cases this may allow corrections to be applied. The algorithm was tested on momentum, heat and moisture cospectra collected continuously over 18 months as part of the Waves, Aerosol and Gas Exchange Study (WAGES) on the RSS James Clark Ross, mainly in moderate to high wind conditions in the Southern Ocean.
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