Does anyone have any experience with the Surface Water and Ocean Topography (SWOT) datasets for making topo/bathy maps?
How do this/these compare with e.g. GEBCO, earth_relief, etc?
Relevant links:
I’m especially interested in it’s ability to pick up unchartered seamounts (see link #3).
No experience but our @earth_synbath
has the predicted seamounts, so likely a related thing.
Thanks @Joaquim. It appears that SWOT is baked into GEBCO, so maybe it’s already available through that dataset.
Hi @Andreas
not a specialist on this but as far as I understand, the SWOT data are satellite altimetry data of the ocean surface topography (not ocean basin bathymetry). Like the earlier Sandwell gravity from satellite altimetry products, these allow to compute free air gravity anomalies (see Sandwell’s page) and can be used to generated the predicted bathymetry grids. I am not sure whether there is a SWOT-infused bathymetry model yet. There are standard netCDF grids for download on the Scripps website which you can use with the GMT toolchain to plot/analyse, but again, I am only aware of the FA gravity from SWOT altimetry (see the Yu et al., Science paper).
A recent paper on using the SWOT data for seamount analysis is this one
Cheers,
Christian
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Hi @chhei-s,
Thank you for an excellent answer and interesting link - I’ll be sure to read it!
Yes, I think that’s what I thought (..) it was like; ocean topography (tiny tiny bumps) → gravity → bathymetry. I read somewhere that the SWOT data was included/infused in the GEBCO grid(s) from 2024, but I can’t remember where. I’ll link to it if it suddenly occurs to me.
Thanks again! Always fun with new data.
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I think not. This is from the readme file for the just released SRTM15+ grid:
SRTM15+V2.7 - February 14, 2025
This version has three main improvements with respect to V2.6.
- The predicted bathymetry is greatly improved for two reasons. First the gravity field accuracy and resolution are dramatically improved because of the new sea surface gravity data collected by the SWOT radar altimeter.
…
In addition we have formed a large working group to refine machine learning methods for predicting depth from dense satellite gravity (i.e. SWOT) and sparse ship soundings. Much of that work was done by Farshad Salajegheh (Farshad.Salajegheh@newcastle.edu.au) at the University of Newcastle in Australia. We chould include Farshad as on of the co-authors on the GEBCO_2025 grid.
- We have added imany more soundings from the deep ARGO project. We may want to include Nathalie Zilberman (nzilberman@ucsd.edu) as a co-author on the GEBCO_2025 grid.
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