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Turbulence, Intermittency and Chaos in High-Resolution Data, Collected At The Amazon Forest. Paula Agudelo. DATA. Data at 21m and 66m. 60m tower built in the Rebio Jaru reserve in (10º04'S 61º56'W), Brazilian state of Rondonia.
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Turbulence, Intermittency and Chaos in High-Resolution Data, Collected At The Amazon Forest. Paula Agudelo
DATA Data at 21m and 66m 60m tower built in the Rebio Jaru reserve in (10º04'S 61º56'W), Brazilian state of Rondonia. The data used consists of the wind velocity components along the three orthogonal directions and the temperature, all obtained using fast response sonic instruments. frequency of 60 Hz. (60 Samples/second) (9min) Data were collected as part of a LBA project (The Large Scale Biosphere-Atmosphere Experiment in the Amazon)
SERIES 12pm 6pm 6th 7th U V W T 12am March/1999 9pm 3pm
Fourier Vs Wavelets Fourier transform Decompose a time series in sines and cosines of different frequencies. Since sines and cosines are infinite functions, It only gives information of frequency Wavelet transform Decompose a time series in different functions The wavelet function goes to zero, giving information of frequency and localization in time
Kolmogorov law of -5/3 (n=2) 7 March, 12pm at 66m
Kolmogorov law of 5/3 8 March, 12pm at 66m
Removing intermittency WT:Wavelet Coefficients (Result of the transform) Sum over all WT = Series Variance Spectral density function Km=Wave Number Standard deviation Coefficient of energy variation Structure Function Flatness Factor (Similar to Kurtosis)
Chaotic Behavior Phase Space reconstruction how to go from scalar observations to multivariate phase space to apply the embedding theorem to say that what time lag (time delay) to use and what dimension to use are the central issues of this reconstruction. Average Mutual Information Embedding dimension dE. Global False Nearest Neighbors
Mutual Information 7 March, 12pm at 21m 7 March, 12pm at 66m
U Component 12am
U Component 12pm
T Component 5pm
T Component 5pm