Example forward-difference: Difference between revisions

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[[File:Velocity data.png|frameless|center|400px]]
[[File:Velocity data.png|frameless|center|400px]]
Note: <math> b^{\prime}</math> is synonymous with <math> v^{\prime}</math> in this figure
Note: <math> b^{\prime}</math> is synonymous with <math> v^{\prime}</math> in this figure and the one below.


The square of the velocity difference between bins separated by <math>\delta</math> bins is then evaluated for each <math>t</math>.  So for bin 4, <math>\delta=3</math> and <math>t=2</math>, we get:
The square of the velocity difference between bins separated by <math>\delta</math> bins is then evaluated for each <math>t</math>.  So for bin 4, <math>\delta=3</math> and <math>t=2</math>, we get:

Latest revision as of 13:01, 23 May 2022

Consider the example of an ADCP with a beam angle of 20, configured with a vertical bin size of 10 cm, recording profiles at 1 second intervals with a data segment length of 300 seconds. The Level 1 QC of the data identified that good data was typically returned from bins 1 to 30.

The velocity data from a single beam for a single data segment can therefore be visualised as:

Note: b is synonymous with v in this figure and the one below.

The square of the velocity difference between bins separated by δ bins is then evaluated for each t. So for bin 4, δ=3 and t=2, we get:

Δ2(4,3,2)=[b(4,2)b(7,2)]2

For bin 1 the squared velocity difference can be evaluated for 1δ29, whilst for bin 2 it is restricted to 1δ28, reducing by 1 with each bin, so that for bin 29, it can only be evaluated for δ=1 and there are no options for bin 30. This is summarised as follows:

The mean is then taken across the 300 profiles in the data segment i.e.

D(1,δ)=t=1300Δ2(1,δ,t)

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