You should look at Icechunk. Your imaging data is structured (it's a multidimensional array), so it should be possible be to represent it as "Virtual Zarr". Then you could commit it to an Icechunk store.
If you're wondering this you should look at Icechunk too, which was open-sourced just this week. It's Apache Iceberg but for multidimensional data (e.g. Zarr).
So the equivalent of these balloons in oceanography are called ARGO floats, which similarly cannot be driven laterally but can control their own depth like a submarine. So far millions of timeseries have been collected across the world ocean using these floats.
One difference though is that the ARGO floats are unfortunately not recycled, and just wash up on various beaches. (I'm curious whether you think you can realistically collect many of these mini balloons?)
If you do want to control the lateral position of fleets of sensors, oceanographers also now have "gliders", which are basically small powered drone submarines. These are used by a few groups, but most of the gliders in the world are operated by the US Navy, who launch them out of torpedo tubes to survey local ocean conditions (which is badass).
The recorded measurements present an interesting data assimilation challenge - they record data along 3D trajectories (4D including time), sampling jagged and twisting lines through the 4D space. But we normally prefer to think of weather/ocean data as gridded, so you need to interpolate the trajectory data onto the grid, whilst keeping the result physically-consistent. Oceanographers use systems like ECCO for ocean state estimation, which effectively find the "ocean of best fit" to various data sources.
Interestingly ECCO uses an auto-differentiable form of the governing equations for the ocean flow to ensure that updates stay physically consistent. This works by using a differentiable ocean fluid model called [MITgcm](https://github.com/MITgcm/MITgcm) to perform runs which match experimental data as closely as possible, and minimizing a loss function through gradient descent. The gradient is of a loss function (error) with respect to model input parameters + forcings, which is calculated by running MITgcm in adjoint mode - i.e. automatic differentation. Therefore this approach is sort of ML before it was cool (they were doing all this well before the new batch of AI weather models). See slides 9-18 of this deck for a nice explanation
The trajectory data is also interesting because it's sort of tabular, but also you often want to query it in an array-like 4D space. You could also call it a "ragged" array. We have nice open-source tools for gridded (non-ragged) arrays (e.g. xarray and zarr, and the pangeo.io project) but I think we could provide scientists with better tools for trajectory-like data in general. If that seems relevant to you I would love to chat.
P.S: Sorceror seems awesome, and I applaud you for working on something hard-tech & climate-tech!
This is super interesting, I'd never come across ARGO before. Data assimilation is a similar problem for our data, and there currently exist systems for assimilating weather balloon observations into gridded reanalysis data (https://www2.mmm.ucar.edu/wrf/users/). One thing we believe, however, is that the reanalysis step in weather forecasting is unnecessary in the long term, and that future (ML) weather models will eventually opt to generate predictions based on un-assimilated raw data and will get better results in doing so.
That being said, trajectory-based data tooling could be super interesting to us. Let's definitely chat: austin@sorcerer.earth
And re: recovery, we're pretty confident we'll be able to recover the majority of our systems. Being in the air has the advantage that we can choose to 'beach' ourselves in a specific location, rather than the first place we run across land like with the buoys. At his previous company, Alex wrote a prediction engine able to get similar balloon systems to land in a predicted 1kmx1km zone for recovery
> One thing we believe, however, is that the reanalysis step in weather forecasting is unnecessary in the long term, and that future (ML) weather models will eventually opt to generate predictions based on un-assimilated raw data and will get better results in doing so.
The idea that we'll be able to run ML weather models using "raw" observations and skip or implicitly incorporate an assimilation is spot-on - there's been an enormous shift in the AI-weather community over the past year to acknowledge that this is coming, and very soon.
But... in your launch announcement you seem to imply that you're already using your data for building and running these types of models. Can you clarify how you're actually going to be using your data over the next 12-24 months while this next-generation AI approach matures? Are you just doing traditional assimilation with NWP?
Also, to the point about reanalysis - that's almost certainly not correct. There are massive avenues of scientific research which rely on a fully-assimilated and reconciled, corrected, consistent analysis of atmospheric conditions. AI models in the form of foundation models or embeddings might provide new pathways to build reanalysis products, but they are a vital and critical tool and will likely be so for the foreseeable future.
> There are massive avenues of scientific research which rely on a fully-assimilated and reconciled, corrected, consistent analysis of atmospheric conditions.
That’s a good point! In fact, the outputs for observation based foundational models will likely include a "reanalysis-like" step for the final output.
Regarding the next 6-12 months, we will be integrating our data with traditional NWP models and utilizing AI for forecasting. We've developed a compact AI model that can directly assimilate our "ground truth" data with reanalysis, specifically for use in AI forecasting models.
Once we have hundreds of systems deployed, we'll use the collected observations, combined with historical publicly available data, to train a foundational model that will directly predict specific variables based on raw observations.
https://earthmover.io/blog/icechunk
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