07-12, 14:30–14:40 (Europe/Amsterdam), While Loop (4.2)
We present a new methodology implemented in the Julia package TurbulenceFlux.jl to compute high time resolution flux from flux tower measurements. Through wavelet analysis, it operates in the time-scale domain to identify times and scales at which turbulence is sufficiently developed to integrate a flux.
Flux estimation from eddy-covariance flux tower measurements faces the problem of integrating fluxes only in the case of fully developed turbulence and in non-stationary environments with advective components. The standard eddy-covariance method operates on fixed-length signals, requiring the knowledge of a maximum correlation time-length as well as post-processing steps assessing the suitability and quality of the data. One important disadvantage of these statistical tests is that they discard whole time intervals such as half an hour if they detect failure.
Time-scale (time-frequency) analyses have been used as an alternative to the standard time-analysis approach to estimate ecosystem fluxes. In particular, wavelet analysis, which is well adapted to the study of non-stationary and scale invariant processes such as turbulence, has been used in previous works. It presents the ability of separating the different components of the flux in time-scale space and as such could be an efficient alternative for flux estimation.
In a new Julia package TurbulenceFlux.jl, we implement a general framework for analysing fluxes in time-scale space, and propose a new method for identifying and extracting turbulent transport. The Julia language and its package ecosystem made this implementation easy and natural. In particular, it allowed our code match the speed of the standard approach implemented in Fortran. It also helped us make our code differentiable, which hints to further investigations, such as the study of flux ecosystem response times, or sensitivity analysis against wavelet and averaging window parameters.
