1. Paula Agudelo Turbulence, Intermittency and Chaos in High-Resolution Data, Collected At The Amazon Forest.

2. The data used consists of the wind velocity components along the three orthogonal directions and the temperature, all obtained using fast response sonic instruments. 60m tower built in the Rebio Jaru reserve in (10º04'S 61º56'W), Brazilian state of Rondonia. (The Large Scale Biosphere-Atmosphere Experiment in the Amazon) Data were collected as part of a LBA project frequency of 60 Hz. (60 Samples/second) (9min) DATA Data at 21m and 66m

3. SERIES 6 th 7th March/1999 12pm 3pm 6pm 9pm 12am U V W T

4. Histograms

5. Examples

7. Profiles

8. Fourier Vs Wavelets Fourier transform Decompose a time series in sines and cosines of different frequencies. Wavelet transform Decompose a time series in different functions Since sines and cosines are infinite functions, It only gives information of frequency The wavelet function goes to zero, giving information of frequency and localization in time

9. 7 March, 12pm at 66m Kolmogorov law of -5/3 (n=2)

10. 8 March, 12pm at 66m Kolmogorov law of 5/3

11. Removing intermittency WT:Wavelet Coefficients (Result of the transform) Sum over all WT = Series Variance Km=Wave Number Spectral density function Standard deviation Coefficient of energy variation Structure Function Flatness Factor (Similar to Kurtosis)

12. Results

14. 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 d E. Global False Nearest Neighbors

15. 7 March, 12pm at 21m 7 March, 12pm at 66m Mutual Information

16. Embedding dimension

17. Lorenz Attractor

18. U Component 12am

19. U Component 12pm

20. T Component 5pm

21. T Component 5pm