avecchi-sorc

Internally yes we use a data warehouse to store the raw observations. We go through a similar process to traditional NWP to produce forecasts except with a few AI steps in between. Unfortunately, the standard currently is to store these as large multi-dimensional data files like grib2/netCDF/zarr.

In case you’re curious, here’s where NOAA stores all their GFS related forecasts: https://registry.opendata.aws/noaa-gfs-bdp-pds/

Yes, however, like with traditional forecasts, we weigh our balloon observations much higher.

I was in the same boat. I've been a software engineer for as long as I can remember and always wanted to do more than just build B2B SaaS.

Max, the first engineer at Urban Sky, hit me up and asked if I wanted to build their mission control. At the time, Urban Sky was just a four-person team, so they couldn’t pay me as much, but I jumped at the chance, even though it meant taking about half my usual salary.

Funny enough, my SaaS background actually helped me create mission control software that was way ahead of the curve!

I guess my advice is, find a small company you're passionate about, where you can make a big impact, and be open to taking a pay cut. It helps the company take less of a risk on you, and you get to work on something that really matters. Plus, when you’re solving real problems, things tend to work out, and eventually, you’ll end up making what you should in salary.

Here's a video of one of our recent recoveries: https://youtu.be/8DWYLG_95V0

> 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.