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Overview of LISST data Quantities of interest: C n,z, w f,n Program: u Vertical profiler data with LISST-100 u Bottom boundary layer size distribution.

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Presentation on theme: "Overview of LISST data Quantities of interest: C n,z, w f,n Program: u Vertical profiler data with LISST-100 u Bottom boundary layer size distribution."— Presentation transcript:

1 Overview of LISST data Quantities of interest: C n,z, w f,n Program: u Vertical profiler data with LISST-100 u Bottom boundary layer size distribution w LISST-100 u Settling velocity spectrum with LISST-ST u and by the way..New Observations reveal differences in scattering by spheres vs random shaped particles

2 Principles of the LISST LISST-100 Signature of size is in location of peak. Observed multi-angle scattering is inverted >> size distribution.

3 VSF from measured scattering u Computing Optical Volume Scattering Function  i = E i /[      i-1) (1-   )P 0 G] where  i is VSF at angle corresponding to ring detector no. i    is voltage output from ring i after amplification  is attenuation in water, exp(-cl)  0 is smallest angle of VSF measurement, r min /f  is the ratio of outer/inner radius of each ring (=1.18) P 0 is incident laser power into the water, watts G is photo-detection gain of rings, Volts/Watt

4 Field measurement of VSF Data from HYCODE, LISST on a profiler.

5 Estimating settling velocity with the -ST u Method is to trap water in settling column, sample over quasi-log time scale, invert for 8 size-classes; –Fit expected history curve for concentration history of each size-class by adjusting settling time.

6 Settling velocity vs Stokes/Gibbs u Large particles show departure from Gibbs law (or modified Stokes law) due to flocculation;

7 Mean settling velocity ‘law’ A simple power-law w f ~ d q is not suitable. Fractal more suitable. Gibb’s law

8 4 Questions and a new study 1. Why do fine particles appear to settle at ‘super- stokes’ velocities? 2. Why the systematic offset in the calibration for spheres vs natural particles? 3. Why do natural particles ~8 micron appear as ~3 micron (literature, Milligan, pers. Comm.)? 4. Why does laser diffraction method always produce a peak at the fine particle end from field data, but not with lab spheres?

9 Ongoing research- Natural Particles  settling column techniques used to isolate narrow sizes with 0.1  resolution.

10 Ongoing research- Natural Particles u Some key points: – Jones(1988) presented pure diffraction solution, his results depend only on ka  ; observations on ka and . –Volten(2000) presents the most recent work with natural particles, but only for >5 o –new insights into size-specific counterpart to Mie theory –This research will produce empirical matrix for use with LISST data when observing natural sediments –The data qualitatively explain the ‘super-stokes’ settling rates produced by the -ST for finest particles

11 in conclusion u The analysis task is to integrate size and settling velocity data with Trowbridge’s on velocity structure u Integrate natural particle scattering data in interpretation of multi-angle scattering to tighten estimates of size distribution, settling velocity distribution etc.

12 Mie Calculation vs Pure Diffraction u Mie and diffraction

13 A new family of possibilities u In October 2001, we found new comets, blobs, stars, to: – Measure concentration in a size-range –Measure concentration > or < a cut-off –Measure concentration for specified fractal dimension of particles. u These family of comets are found by replacing the unit vector U of previous slide u This development was prompted by a question by Dr Ted Melis, USGS, Flagstaff.

14 New focal plane detectors (‘other comets’)


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