Wavelet analysis, using the non-orthogonal Morlet basis, have been used to examine what was often intermittent, and at least non-stationary, turbulence and eddy fluxes using the repeated, low-level passes of the WKA. Wavelets are used to describe the occurrence and scale of the turbulence and resulting fluxes. Wavelet co-spectra are integrated to estimate the eddy fluxes of sensible heat, water vapor, and momentum. Integrations over some portion of the spectra are used to examine fluxes at various scales and locations. Estimates obtained from the wavelets are compared with those from several other traditional eddy-covariance techniques, including 1) covariances are calculated for a fixed-length window that is moved along the flightline, and 2) covariances are calculated for 100-m intervals along the flight line, and then averaged over several lengths. In both these latter cases, the window size and the averaging length cause some degree of high-pass filtering. Comparison with the wavelet fluxes should tell what effect this has on the fluxes.
Finally, principal component analysis (PCA) has been applied to the WKA data as an alternative method to compute the fluxes and in particular, to examine the role of spatially-locked components in driving or modulating fluxes along the flight-line. Prior to this analysis, the high-rate WKA data are mapped relative to the flight line and interpolated onto a regular grid. The principal components are then determined from the covariance matrix based on all of the low-level flight legs for a single case. The contribution from the first few principal components to the various fluxes should provide insight into the relative importance of mesoscale and terrain-induced motions as compared to the smaller-scale turbulent fluxes calculated using conventional eddy covariance methods with a fixed window.