lagrangian

01We have a Winner!

Our first Where’s the Monk contest asked one question: where would The Monk — one of Seatrec’s infiniTE™ floats, drifting along the North Atlantic — surface on July 5, 2026, at the closest time before 11:59 PM PDT? Predictions came in from around the world. Landing within 100 km of a float that’s been riding the Gulf Stream’s eddies and meanders for two weeks is genuinely hard — only four entries managed it. And when the Monk surfaced at 38.5626°N, 50.6690°W, at 3:27 PM PDT, one prediction stood closest of all, just 52 km away: José Manuel Echevarría Rubio, PhD Candidate in Marine Sciences at CICIMAR-IPN. (Shout out to his advisor, Dr. Guillermo Martínez-Flores!). 

02The Results

José Manuel’s prediction (green) landed closest of all, with three more entries (yellow) also within 100 km — a genuinely tough target to hit. The rest of the yellow dots, spread across the map, show just how far the ocean can carry even a well-researched prediction.

The field made it a real race. Entries came in from around the world, and only four landed within 100 km of The Monk’s surfacing position — José Manuel’s the closest among them. Between them, the top four spanned Mexico, France, and both coasts of the United States — and all four are expert oceanographers.

03The Winning Prediction

Oceanographers describe moving water in two ways. The Eulerian view watches the ocean from fixed points, like a moored buoy reporting the current flowing past it, or a weather station reporting the wind. The Lagrangian view follows the water itself, like a message in a bottle going wherever the ocean takes it. Predicting The Monk is a classic Lagrangian problem: the float is a kind of diving bottle, and the question is where the ocean carries it.

That is what makes it hard. A Lagrangian forecast has to get many things right at once, and an error in any one of them can grow day by day:

• The mean current — the Gulf Stream is a geostrophic current, driven by sea surface height gradients, that can advect a float over 100 km a day in its core and far less just outside it. Get the current’s strength or position wrong and the whole trajectory shifts.
• Mesoscale eddies — warm-core and cold-core rings, tens to hundreds of kilometers across, that pinch off from meanders in the main current. A float caught in one gets trapped in rotational flow instead of advecting downstream — you can see exactly this looping behavior in The Monk’s track.
• Meanders — baroclinic instability makes the Gulf Stream snake rather than flow straight; whether a given meander sweeps the float north or south of the mean path can matter by hundreds of kilometers.
• Vertical shear — a profiling float spends much of its time below the surface, where current speed and direction can differ substantially from the surface, so the model has to resolve the current field at depth, not just at the surface.
• Chaotic dynamics — Lagrangian trajectories are sensitive to initial conditions; small position uncertainties amplify over time, the same underlying reason weather forecasts lose skill with lead time. A few kilometers of error early on can compound into a much larger miss days later.
• Data latency — position updates stopped on June 28; the prediction target was July 5. That’s seven days of dead reckoning, forecasting motion with no fresh GPS fix to correct against.

04The Interview

What made you enter Where’s the Monk?

I saw the LinkedIn post, and part of my PhD thesis is modeling the Lagrangian transport of pelagic sargassum.

Walk us through your approach — what data and models did you use?

First, I got the whole track of the float since its deployment in the Gulf of Mexico. I used Copernicus Marine data — the Global Ocean Physics Reanalysis (GLORYS) current fields — with eight depth levels from the surface down to about 900 meters and built a cube of currents. I modeled the float’s trajectory going down into the deepest layer and back up to the surface every 11 hours, at about 0.1 meters per second for the descent and buoyant ascent rate.

I used two different datasets: a reanalysis for the hindcast (past ocean current data)  and a forecast product for the actual prediction — which has more uncertainty, so I had to build an envelope of possible trajectories rather than a single path. I ran the simulations using the OpenDrift library, developed by the Norwegian Meteorological Institute. To validate the model, I did a grid search to tune all the parameters against the float’s own past positions (hindcast) before generating the forecast — the same validation approach I use in my PhD work with in-situ drifters for sargassum transport.

What was the hardest part of the prediction? What almost threw you off?

I had to use two different datasets — a reanalysis for the hindcast, the past ocean current data, and a different forecast product for the actual prediction. The forecast has uncertainties; it’s not as accurate, so I had to build an envelope of all the possible trajectories and adjust parameters accordingly. That was the trickiest part.  

But it was a good exercise, because it’s the same approach I’m using right now to forecast the date and location of the sargassum beaching events.

The Monk surfaced 52 km from your coordinates. What did you get right?

I followed a good methodology — it wasn’t a lucky guess.

What first drew you to oceanography?

I grew up two blocks away from the Caribbean Sea, and the ocean has always been part of my life. My cousin, who is like my sister, studied marine biology and worked at the National Aquarium in Havana (now a Professor of Marine Science in Spain). I went to the Aquarium frequently with her, and she was the one who introduced me to this field of science.

You’re working on your dissertation — what’s it about, and what’s next for you?

My dissertation is on the factors that are controlling the proliferation of sargassum in the equatorial Atlantic — known as the Great Atlantic Sargassum Belt. First, I need to detect the sargassum, calculate the coverage, and simulate the trajectories. And the final part of my PhD is to try to find what is driving the blooms. Looking ahead, I want to stay right at that intersection, continuing to work on projects that combine remote sensing with ocean modeling to solve complex environmental challenges.

Any advice for people entering the next contest?

The first advice I’ll give is to have a solid ground truth — the past track the float actually took — and validate your model against that in-situ data before you attempt a forecast. Try to do the validation first. I do a lot of machine learning too, and it’s the same principle: you train your model, then validate on data the model hasn’t seen before

Plans after your dissertation defense?

I want to do a postdoc, maybe at the same institution — I’m not sure yet, but for sure I want it to combine remote sensing and physical oceanography. I’ve worked a lot with remote sensing already; for example, I did a project researching kelp forests on the West Coast, in the Pacific Northwest, and I really love kelp forests and the role they play in coastal ecosystems. I want to continue working on combining remote sensing with physical oceanography and ecological modeling. The first advice I’ll give is to have a solid ground truth — the past track the float actually took — and validate your model against that in-situ data before you attempt a forecast. Try to do the validation first. I do a lot of machine learning too, and it’s the same principle: you train your model, then validate on data the model hasn’t seen before

05What’s Next

José Manuel takes home the exclusive Seatrec “Infinite Energy · Deeper Insights” infiniTE™ T-shirt — and he’s not done.  He’s going to compete in the next contest, and on Tuesday, August 18, 2026, at 9:00 AM PDT, he’ll join Dr. Paul Chamberlain, Postdoctoral Researcher at Scripps Institution of Oceanography, for our webinar “Finding The Monk: How to Predict Where the Ocean Takes a Float” — a walkthrough of real trajectory-prediction techniques, including how the winning forecast came together.  José Manuel will defend his Ph.D. thesis early August, and by the time of our webinar, he will be Dr. Manuel.