ambiguity jumps in AquaDopp HR

Hi there,

In a deployment with Nortek AquaDopp HR,  we used a maximum horizontal velocity value of 0.49 m/s(given in the software) which seems like it is lower than some of the values HR faced. Because data have some irregularities in profile and checking with the Beam Data shows that there are ambiguity jumps, similar problem discussed in the thread below:

http://www.nortek-as.com/cgi-bin....=6;t=22

We are a little bit confused with the discussions made in the above thread.

1) Firstly, what is exactly the case called as 'large' jump , other than spikes between Vmax and Vmin values?  Because, there are spikes which do not reach to the peak value in Beam coordinates, but they are also certainly spikes and need to be corrected probably.

2) How can we calculate this ambiguity velocity for a specific case? Checking with the literature and information related to some similar instruments do not give consistent values. It seems like that it has a formula depending on max.velocity settings and sound speed (and in my opinion distance to bottom). Bcause  in the thread above, an ambiguity velocity of 20.3 cm/s is suggested for a speed of sound of 1500 m/s in the case of a 30 cm/s configuration. If it does not have a certain formula, what should we take it for maximum velocity of 0.49 m/s.  ?

3)After a jump(either positive or negative ), if the velocity does not turn back to its original sign at the next time step, what should we do to this value? Shift in a way that it will be aligned with the previous value, or anything else? The attached file(a part of beam1 velocity time series of a cell of HR in the related deployment) shows a time series which is hard to predict whether the jumps are in positive or negative directions and to postprocessing it seems like too hard.

4) In order to avoid these jumps and therefore these difficulties in the post-processing, is it enough to deploy HR to regions that we are sure that there will not be velocities greater than its maximum value or are there any other considerations?  

Anybody have a suggestion or met with the same problems before?

Thanks,

Ilgar

Dear Ilgar,

There is a paper on our WEB site that you may find of use:
HR-NDP paper from Switzerland

It describes the predecessor to the Aquadopp but it has a section about how the system works that may be of interest.

As for the ambiguity velocity, it is a function of spacing between the two transmit pulses ("lag").  This lag has to be larger than the profiling range so in this sense the profiling range is the key parameter that determines the amb. vel.  In other words, you can avoid the problem by reducing the profiling range enough that you are sure that the local velocity is smaller than the ambiguity velocity.

The Vector/Vectrino is different in the sense that the profiling range does not enter into the picture.  In these instruments, the velocity ranges are there simply to allow the user to minimize the random noise in his velocity data.

Best regards, Atle Lohrmann

PS I realize that it is not simple to understand how these systems work.  The best solution is thus to adjust the profiling range and avoid situations where the actual velocity exceeds the ambiguity velocity " />'>

Dear Atle ,

Thank you very much for your urgent answer and helps upto now. I still fail to comprehend some points from your answer and references I went through. From our previous talks and the thread below:

http://www.nortek-as.com/cgi-bin....=6;t=22

there is a certain  Vmax or amb.vel. value for each case that you can process and filter your data with;

if the jump is negative, then
 newvel = oldvel + 2*ambiguity velocity
else
 newvel = oldvel - 2*ambiguity velocity
end

So the first issue is how to calculate this ambiguity velocity used in the loop above.
Lastly, in a time series similar to the one in the attached file, is there a way-easy or difficult, no matters- to determine which of these are the jumps and which are the valid data? (I know that it may be hard to comment from a small portion of data but just as an example) Because, it makes jumps in negative and positive direction. Or should we say that such a data set, containing timeseries with too much of such behaviour, is useless?
 
Suggestions of users with similar problem will also be appreciated.
Regards,

Ilgar

Hi

An estimate of the ambiguity velocity is (max(V1) - min(V1))/2

Best regards, Atle Lohrmann

Thank you very much.

Regards,

Ilgar

Moved to new forum category for HR-profilers