Multifractal Analysis Techniques and the Rain and Cloud Fields from 10−3 to 106m

Abstract. Phytoplankton patchiness has been investigated with multifractal analysis techniques. We analyzed oceanic chlorophyll maps, measured by the SeaWiFS orbiting sensor, which are considered to be good proxies for phytoplankton. The study area is the Senegalo-Mauritanian upwelling region, because it has a low cloud cover and high chlorophyll concentrations. Multifractal properties are observed, from the sub-mesoscale up to the mesoscale, and are found to be consistent with the Corssin-Obukhov scale law of passive scalars. This result indicates that, in this specific region and within this scale range, turbulent mixing would be the dominant effect leading to the observed variability of phytoplankton fields. Finally, it is shown that multifractal patchiness can be responsible for significant biases in the nonlinear source and sink terms involved in biogeochemical numerical models.

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Multifractal analysis of oceanic chlorophyll maps remotely sensed from space

Ocean Science Discussions ◽

10.5194/osd-8-55-2011 ◽

2011 ◽

Vol 8 (1) ◽

pp. 55-84

Author(s):

L. de Montera ◽

M. Jouini ◽

S. Verrier ◽

S. Thiria ◽

M. Crepon

Keyword(s):

Turbulent Mixing ◽

Multifractal Analysis ◽

Numerical Models ◽

Remotely Sensed ◽

Dominant Effect ◽

Passive Scalars ◽

Nonlinear Source ◽

Analysis Techniques ◽

Scale Range ◽

Source And Sink

Abstract. Phytoplankton patchiness has been investigated with multifractal analysis techniques. We analyzed oceanic chlorophyll maps, measured by the SeaWiFS orbiting sensor, which are considered to be good proxies for phytoplankton. Multifractal properties are observed, from the sub-mesoscale up to the mesoscale, and are found to be consistent with the Corssin-Obukhov scale law of passive scalars. This result indicates that, within this scale range, turbulent mixing would be the dominant effect leading to the observed variability of phytoplankton fields. Finally, it is shown that multifractal patchiness can be responsible for significant biases in the nonlinear source and sink terms involved in biogeochemical numerical models.

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Convergence of numerical box-counting and correlation integral multifractal analysis techniques

Pattern Recognition ◽

10.1016/s0031-3203(96)00162-8 ◽

1997 ◽

Vol 30 (9) ◽

pp. 1565-1570 ◽

Cited By ~ 4

Author(s):

L.V. Meisel ◽

M.A. Johnson

Keyword(s):

Multifractal Analysis ◽

Correlation Integral ◽

Analysis Techniques ◽

Box Counting

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BI-MULTIFRACTAL ANALYSIS AND MULTI-AFFINE MODELING OF NON-STATIONARY GEOPHYSICAL PROCESSES, APPLICATION TO TURBULENCE AND CLOUDS

Fractals ◽

10.1142/s0218348x93000587 ◽

1993 ◽

Vol 01 (03) ◽

pp. 560-567 ◽

Cited By ~ 6

Author(s):

A.B. DAVIS ◽

A.L. MARSHAK ◽

W.J. WISCOMBE

Keyword(s):

Multifractal Analysis ◽

Traditional Approach ◽

Singularity Analysis ◽

Transport Processes ◽

Scale Invariant ◽

Geophysical Field ◽

Multifractal Formalism ◽

Analysis Techniques ◽

Geophysical Processes ◽

Stationary Behavior

In recent years, considerable progress has been achieved in the description of natural variability, largely due to the widespread use of scale-invariant concepts such as fractals and multifractals. In particular, this last concept has been used to clarify the fuzzy notion of “inhomogeneity” by introducing and quantifying the effects of intermittency. In this paper, we present a more comprehensive approach to multifractal data analysis and simulation that includes and combines the currently popular singularity analysis techniques with the more traditional approach based on structure functions. Being related to the new idea of “multi-affinity”, these last statistics are regaining favor and constitute the proper framework to address the problem of quantifying and qualifying yet another outstanding fuzzy notion, that of “non-stationarity”. This is an important step because non-stationary behavior is ubiquitous in Nature. Using turbulence as an example, we also show how a unified multifractal formalism can help in extracting, from data alone, the “effective constitutive laws” that describe phenomenologically the nonlinearities of the macroscopic transport processes that shape the geophysical field represented by the dataset. Finally, we argue that the essential multifractality of any natural system can be captured on the “q=1 multifractal plane” and describe ways in which it can be used in practical geophysical problems.

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An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010008794x ◽

1991 ◽

Vol 49 ◽

pp. 726-727

Author(s):

John A. Hunt

Keyword(s):

Quantitative Analysis ◽

Large Data ◽

Difference Spectrum ◽

Large Data Sets ◽

Foil Thickness ◽

Data Sets ◽

Analysis Techniques ◽

Spectrum Imaging ◽

Normal Spectrum ◽

Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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Dissociated Σ=27 boundaries in silicon

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100105187 ◽

1988 ◽

Vol 46 ◽

pp. 624-625

Author(s):

A. Garg ◽

W.A.T. Clark ◽

J.P. Hirth

Keyword(s):

Electron Diffraction ◽

Local Minimum ◽

Boundary Energy ◽

Coincidence Site Lattice ◽

Boundary Plane ◽

Low Energy ◽

Theoretical Understanding ◽

Site Lattice ◽

Kikuchi Pattern ◽

Analysis Techniques

In the last twenty years, a significant amount of work has been done in the theoretical understanding of grain boundaries. The various proposed grain boundary models suggest the existence of coincidence site lattice (CSL) boundaries at specific misorientations where a periodic structure representing a local minimum of energy exists between the two crystals. In general, the boundary energy depends not only upon the density of CSL sites but also upon the boundary plane, so that different facets of the same boundary have different energy. Here we describe TEM observations of the dissociation of a Σ=27 boundary in silicon in order to reduce its surface energy and attain a low energy configuration.The boundary was identified as near CSL Σ=27 {255} having a misorientation of (38.7±0.2)°/[011] by standard Kikuchi pattern, electron diffraction and trace analysis techniques. Although the boundary appeared planar, in the TEM it was found to be dissociated in some regions into a Σ=3 {111} and a Σ=9 {122} boundary, as shown in Fig. 1.

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Material analysis techniques using the ion mill

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100167639 ◽

1996 ◽

Vol 54 ◽

pp. 1034-1035

Author(s):

J. P. Benedict ◽

R. M. Anderson ◽

S. J. Klepeis

Keyword(s):

Polished Surface ◽

Surface Material ◽

Analysis Techniques ◽

Material Analysis ◽

Optical Inspection ◽

Electron Microscopy Analysis ◽

Microscopy Analysis ◽

Argon Ions ◽

The Impact ◽

Scanning Electron

Ion mills equipped with flood guns can perform two important functions in material analysis; they can either remove material or deposit material. The ion mill holder shown in Fig. 1 is used to remove material from the polished surface of a sample for further optical inspection or SEM ( Scanning Electron Microscopy ) analysis. The sample is attached to a pohshing stud type SEM mount and placed in the ion mill holder with the polished surface of the sample pointing straight up, as shown in Fig 2. As the holder is rotating in the ion mill, Argon ions from the flood gun are directed down at the top of the sample. The impact of Argon ions against the surface of the sample causes some of the surface material to leave the sample at a material dependent, nonuniform rate. As a result, the polished surface will begin to develop topography during milling as fast sputtering materials leave behind depressions in the polished surface.

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Developmental Sentence Scoring

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.1503.154 ◽

1984 ◽

Vol 15 (3) ◽

pp. 154-168 ◽

Cited By ~ 9

Author(s):

Mary Ann Lively

Keyword(s):

Expressive Language ◽

Analysis Techniques ◽

Language Analysis ◽

Scoring Errors

Developmental Sentence Scoring (DSS) is a useful procedure for quantifying thegrammatic structure of children's expressive language. Like most language analysis techniques, however, DSS requires considerable study and practice to use it correctly and efficiently. Clinicians learning DSS tend to make many scoring errors at first and they display similar confusions and mistakes. This article identifies some of these common "problem" areas and provides scoring examples to assist clinicians in learning the DSS procedure.

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