Launch HN: Sorcerer (YC S24) – Weather balloons that collect more data

1. Wind speed/direction, air pressure, temperature, humidity, and solar irradiance. We are considering doing atmospheric composition as well, there are a couple of partners that are interested in GHGs, VOCs, water vapor, etc. We have the weight budget to do it, but we haven't flown any production payloads with those sensors yet.

2. The US National Weather Service actually has a commercial data buy program called MESONET [0], where they buy weather data from both academic and commercial partners. We're in the process of becoming one of their commercial partners now. Once we are, a single balloon will pay for itself in a matter of days with the data it can collect, which will let us scale up the number of systems we have deployed. The data we collect right now also lets us build niche weather forecast products like the stratospheric wind forecast mentioned in the post. Once we have enough balloons up, we can start producing useful weather forecast products at a regional and then global scale.

[0]: https://nationalmesonet.us/

a little bit of each right now, depending on what the customer wants

Bill Brown is a legend! Love Lee Meadows with SBS as well - 767+ days is unreal

https://www.scientificballoonsolutions.com/news/

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

Indeed, and not just building more in more at-risk places but also the cost of building materials, construction labor and code compliance requirements have all generally increased more than baseline inflation. Factors like these tend to greatly increase recent estimates vs historical.

I read a paper a few years back which dove into how the data sources for weather damage assessment have changed a lot over the years. Much of the increase is due to more complete reporting and changes in categorization. Also, nowadays more things are insured and modern IT has made gathering the insurance reporting far more exhaustive. Plus local, state and federal agencies responsible for relief and/or recovery are gathering and reporting increasing amounts of data with each decade since the 70s (in part because their budgets rely on it). Factors like these mean in prior decades the total damage costs may have been more similar to today's than they appear but a lot of the damage data we gather and report now wasn't counted or gathered then.

Although I have no experience related to weather science, I remember the paper because it made me realize how many broad-based, multi-decadal historical data comparisons we see should have sizable error bars (which never make it into the headline and rarely even into the article). Data sources, gathering and reporting methods and motivations are rarely constant on long time scales - especially since the era of modern computing. Of course, good data scientists try to adjust for known variances but in a big ecosystem with so many evolving sources, systems, entities and agencies, it quickly gets wickedly complex.