Open AccessProcessing Turbulence Data Collected on board the Helicopter Observation Platform (HOP) with the Empirical Mode Decomposition (EMD) Method
Author(s) -
H. Holder,
M. A. Bolch,
Roni Avissar
Publication year - 2011
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/2011jtecha1410.1
Subject(s) - hilbert–huang transform , spurious relationship , mesoscale meteorology , turbulence , computer science , data processing , mode (computer interface) , remote sensing , noise (video) , signal processing , acoustics , algorithm , meteorology , artificial intelligence , physics , geology , computer vision , telecommunications , filter (signal processing) , machine learning , operating system , radar , image (mathematics)
The Duke University Helicopter Observation Platform (HOP) has previously been shown to be a useful instrument for the measurement of turbulent atmospheric fluxes. As with all such measurements, especially those made from moving platforms, spurious signals, such as instrument noise and mesoscale atmospheric motions, are superposed on the desired signal. Empirical mode decomposition (EMD) is applied in a novel way to identify and separate out different signals represented by intrinsic mode functions (IMFs) in the HOP data and is shown to be an effective tool for the task. The method produces a basis that is adaptive, unique, and orthogonal, all of which are required for this type of data processing, and none of which are present in more traditional techniques. The results of applying EMD are shown to be nonlinear, and occasionally the removal of the correct number of IMFs increases the observed value of energy and fluxes calculated with the eddy correlation technique.