I tried to simulate the trajectory of an drifting object in the oceans by using the data of the OSCAR project http://www.oscar.noaa.gov/. The dataset actually used consist of grid sampled mean 2d current vectors averaged on a monthly interval indicating the speed and direction of water near the sea surface.

However, moving an object by the gradients shows that the gradient field is riddled with attractors and repellors. There may be several reasons for this, like the water flowing vertically or the mean operation introducing artifacts.

Thus moving an object along trajectories made up by a static snapshot of the dataset isn't very useful, as it usually get stuck in one of the hundreds of sink attractors. This contradicts the usual knowledge of drifting particles to accumulate in very large vortices and eventually reach almost every coastal point on earth.

So how should the mean current be interpreted in respect to drifting object movement? Is there a simple solution to get a coarse drifting simulation that qualitative resembles the expected behavior ?

  • $\begingroup$ Excellent question, and there's also the issue of wind. Would it make sense to filter it, to smooth out local noise? $\endgroup$ – Mike Dunlavey May 17 '15 at 16:35
  • $\begingroup$ I don't think so, as there a hundreds of attractors each hundreds of kilometres in diameter. Filtering them out would result in a very rough picture that maybe would not be natural anymore. My guess is, that those attractors themselfes will move by a current on a larger scale on larger timescales. So the monthly mean may show attractors that would not exist on some-year time scales. Particles that would seem trapped by an attractor in a monthly mean may travel with it so they move in larger patterns over the years may hit a coast eventually. $\endgroup$ – dronus May 17 '15 at 22:30
  • $\begingroup$ Although I am just starting to learn quantum mechanics, I see a similarity between going from the motion of individual atoms, to the average motion of millions of atoms (like in a fluid). Perhaps the equations used for this, might help you deal with your case. $\endgroup$ – Guill May 20 '15 at 7:05

I believe these attractors you are referring to are generally referred to as eddies in the ocean. These features are similar to the hurricanes, and low and high pressure systems in the atmosphere, and just like in the atmosphere they move around.

With monthly mean data (monthly climatology) you can advect particles around in a variety of different ways. The simplest way might be to do a linear interpolation in time between the months and then using that as a time series of velocities. Advecting particles using a single time snap shot of the velocity is not very realistic as the advection of drifters in the real ocean is due to both the velocities and the patterns that these velocities change in. Also by using the monthly mean velocity with linear interpolation you lose the effects of shorter than one month velocity variabilities effect, that have not been sampled by the data.

Quite often the effect of unresolved motions (both in time or space) in the ocean are assumed to be diffusive and you will be able to find a lot of literature on how to parameterize it using some assumptions.

It might be better to post what your ultimate goal is for the trajectory advection experiments and one route over another might be more useful.

  • $\begingroup$ In fact binning the data by weeks instead of month and switching the data according to time remedies the effect. So I guess averaging the flows doesn't predict the actuall motion well which is highly non-linear. $\endgroup$ – dronus Jan 18 '16 at 11:55

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