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hyperspectral analysis. Different opticalparameters (planar and scalar irradiances) willbe estimated from the uprising and downrisingspectral profiles. The averaged values will beused to estimate apparent (AOP) and inherent(IOP) optical properties [7][8].

The last analysis block will combine the dataobtained with the sonde with external dataobtained from different instruments and methods.Calibration methods will be developed for usingthe hyperspectral measurements as a referencefor remote sensing (mult ispectral andhyperspectral) observations. Utilities for analyzingtime and space series will also be included,specially for relating the biological experimentaldata (obtained at much coarser resolution) withthe measurements obtained with the newinstrumentation.

3. AcknowledgementsThe project VARITEC-SAMPLER (CTM2004-04442-C02-2/MAR) is funded from the SpanishMinistry of Education and Science. We thankMaribel Perez and Nuria Pujol for theircollaboration on the design of the hyperspectralsensor control.

4. References [1] PME. Precision Measurement Engineering(). [2] J. Kuhnke D. Osterloh. Aufbau undAnpassung von LIGA-Mikrospektrometern fürdie On-Line Abwasseranalyse – Microparts,Dortmund, Forschungsbericht (16SV426/7),1999. [3] B. Ruddick, A. Anis and K. Thompson.Maximum likelihood spectral fitting: the BatchelorSpectrum. J. Atmos. Ocean. Tech., 17, 1541-1555. 2000. [4] T. R. Osborn Estimates of the local rate ofvertical diffusion from dissipation measurements.J. Phys. Oceanogr., 10, 83-89. 1980. [5] J. Piera, E. Roget and J. Catalan. Turbulentpatch identification in microstructure profiles: Amethod based on wavelet denoising and Thorpedisplacement analysis. J. Atmos. Ocean. Tech.,19 (9), 1390-1402. 2002. [6] J. Piera, R. Quesada and J. Catalan.Estimation of non-local turbulent mixingparameters derived from microstructure profiles.J. Mar. Res. In press. 2006. [7] J. T. O. Kirk, “Ligth and photosynthesis inaquatic ecosystems”, Cambridge UniversityPress. 1983. [8] C. D. Mobley, “Light and Water, radiativetransfer in natural waters”. Academic Press.1994.

Turbulent oceanic flow characterization derived from high-resolutionCTD data processing.

R.Quesada (1), J.Piera (2), I.Fernández (2), E.Torrecilla (1,2),S.Pons (1)(1) Technical University of Catalonia. Av. Canal Olímpic s/n 08860 Castelldefels, Spain.

934137120 ruben.quesada@upc.edu(2) Marine Technology Unit (CMIMA-CSIC), Passeig Marítim 37-49, Barcelona 08003, Spain.

1. IntroductionThe characterization of the turbulent oceanic flowdynamics has many important implications inenvironmental studies (to name a few: dispersionof contaminants, harmful algal blooms or climatechange).The analysis of microstructure density profiles,obtained from high-resolution measurements ofconductivity, temperature and depth (CTD), is acommon approach for character iz ingenvironmental turbulent fluid dynamics. Inparticular, Thorpe [1] proposed a simple methodfor analyzing the effects of the turbulent flows onthe microstructure density profiles, which allowsto compute the Thorpe displacement dT(z). Thorpedisplacement is the vertical distance that anindividual fluid particle (i.e. a single density value)of the original profile s(z) has to be moved inorder to generate the stable density profile sm(z)(figure 1).

Many applications are derived from Thorpedisplacement analysis like, for example, thedetection of turbulent regions or the scale analysisof turbulent flows [2].

2. Noise reduction methodThe characterization of the turbulent flow basedon Thorpe displacements has been usuallyfocused in high-stratified layers of the watercolumn, mainly because is in these regionswhere there are most of the critical turbulentfluxes but also because in these case it ispossible to avoid the problems related withinstrumental noise [2]. Due to the instrumentalnoise of CTD measurements, the previous

Figure 1 Method computing the Thorpedisplacement profile. Turbulent regions (in gray)

are identified as regions of non-null dT.

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methods reported in the literature, are usuallyunable to provide robust estimations of theThorpe displacements at low-density gradient.In order to achieve the highest resolution in dTestimation is necessary to find a method foroptimal signal recovering. This method shouldminimise the noise from the density profile,without loosing the small density perturbationsderived from the overturn motions at low densitygradients, which are the basis for estimatingthe Thorpe displacements.

When data are intermittent in nature, as is thecase of density fluctuations, wavelet transformsfor removing noise from data, is highlyadvantageous over either Fourier or real-spaceanalysis [3].

The proposed method for noise reduction isderived from a wavelet-based thresholdingalgorithm [4].This method can be decomposed in three steps(figure 2):

A. Multilevel decomposition. Fast wavelettransform [5] can be applied for decomposinga signal in two different parts, one that keepsthe global features (the approximationcoefficients) and the other with the local features(detail coefficients). Applying recursively suchdecomposition to the measured data [6], ispossible to obtain empirical wavelet coefficientsassociated to different levels of localcharacterisation.

B. Thresholding. In order to reduce the noisecontribution, a threshold is applied to the detailcoefficients, thereby suppressing thosecoefficients smaller than certain amplitude.

C. Multilevel reconstruction. Denoised profilecan be recovered from the transformedcoefficients, applying recursively the InverseWavelet Transform (IWT) over each level ofdecomposition.

3. Future WorkCharacterize and model the instrumental noise:The proposed method in Piera (2002) [7] usesthe wavelet denoising techniques to reduce thenoise level. To improve these processes weneed to estimate the noise features of the originaldata.The noise model will be used to analyze thedifferent options to reduce better the noise levelpresent in the density data.

Minimize the salinity-spiking effects: The differentresponse time of the temperature and conductivesensors produce the salinity-spiking error. Thiserror generates false density fluctuations,producing Thorpe displacements artefacts. Toreduce the effects we must detect and processthe affected samples.

Develop the method to analyze CTD data:Microstructure profiles are obtained withspecialized instruments known as micrsotructuresondes. The reduced number of microstructuredata is an important obstacle to generalize theresult obtained to different oceanic environments.For these reason, one of the objectives is toadapt the developed method to a more extendedoceanographic instrumentation: the CTDprofilers. The method extension to apply it tothe CTD data, require modifications like: increasethe work scale, reconsider the turbulent modelsused to obtain the method results. Theadjustment of the method will be an importantimprovement to be able to use the software, ofdetection and identification of turbulent zones,with field measurements.

4. AcknowledgementsThe project VARITEC-SAMPLER (CTM2004-04442-C02-2/MAR) is funded from the SpanishMinistry of Education and Science.

5. References[1] S. A. Thorpe, Turbulence and mixing in aScottish Loch. Philos. Trans. R. Soc. 1977

[2] J. N. Moum, Efficiency of mixing in the mainthermocl ine. J. Geophys. Res. 1996

[3] U. L. Pen, Application of wavelets to filteringof noisy data. Phil. Trans. R. Soc. Lond. 1999

[4] D. L. Donoho, Adapting to unknownsmoothness via wavelet shrinkage. J. Am. Stat.Assoc. 1995

[5] S. A. Mallat, theory for multiresolution signaldecomposition: the wavelet representation. IEEEPattern Anal. And Machine Intell. 1989

[6] A. Coen, I. Daubechies, B. Jawerth, and P.Vial, Multiresolution analysis, wavelets and fastwavelet transform on an interval. ComptesRendus Acad. Sci Paris (A). 1993

[7] J. Piera, Signal processing of microstructureprofiles: Integrating turbulent spatial scales inaquatic ecological modeling. PhD Dissertation

Figure 2 Schematic diagram of the denosingmethod: S original signal, S’ denoised signal