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Takes into account lots of stuff (e.g. attenuation from air, ozone, and water vapor) with the goal of estimating solar power at any altitude/latitude/day/time.
> In 1981, weather disasters caused $3.5 billion in damages in the United States. In 2023, that number was $94.9 billion ( https://www.ncei.noaa.gov/access/billions/time-series ). Does that surprise someone? I think I would not have guessed this growth to be on such a scale. The chart suggests that severe storms are the main culprit.
This looks amazing. Super exciting to see how this project grows!
Very cool! How are the balloons transferring telemetry back to earth for analysis, etc? Asking because my research at the University of Oxford was around hyper space-efficient data transfer from remote locations for a fraction of the price. The result was an award-winning technology ( https://jsonbinpack.sourcemeta.com ) to serialise plain JSON that was proven to be more space-efficient than every tested alternative…
This is awesome, how do you manage climbing and descending with a balloon. Are you compressing the gas on board or using thermals?
Loon used pumps and an interior air ballast like blimps do. So clearly there are a few ways.
> In 1981, weather disasters caused $3.5 billion in damages in the United States. In 2023, that number was $94.9 billion ( https://www.ncei.noaa.gov/access/billions/time-series ). Does that surprise someone? I think I would not have guessed this growth to be on such a scale. The chart suggests that severe storms are the main culprit.
Obviously what we're doing can't prevent severe weather from happening, but even very small improvements in accuracy and timelines can have a massive beneficial effect when a disaster does happen. My cofounders and I are all from Florida, so hurricanes are the most visceral examples for us. When hurricanes hit, there are always issues along the lines of "we didn't have the right resources in the right places to respond effectively." Those types of issues can be combated with better info.
[0]: https://www.statista.com/statistics/818411/weather-catastrop...
This is awesome, nice work! In case it's useful, I made a Python package for calculating solar irradiance at altitude: https://github.com/gusgordon/airmass Takes into account lots of stuff (e.g. attenuation from air, ozone, and water vapor) with the goal of estimating solar power at any altitude/latitude/day/time.
This sounds like super interesting and meaningful work. Are you hiring, or do you have any advice for your average software engineer on getting into this space?
1) What parameters are you measuring ? Did you think about also measuring gases?
2) What's your business model?