u/Better_Werewolf1216

Integrated weather API Dashboard, started two days ago. It will be frame of visualizing my own physical model & ensemble MOS.

I’ve been tracking weather prediction markets recently and started building a simple dashboard for my own workflow.

I’ve worked on airport-targeted MOS models before, so I know that naive model comparison by itself probably won’t produce great win rates. But I still think there may be value in a clean trader-facing dashboard that pulls market pricing and weather models into one place, so just made it!

Right now the prototype is basic:

• market view
• model comparison
• quick visual comparison against pricing

No serious calibration yet. Just an early attempt to make the information easier to use.

My main question is: for people who trade these markets (not HFT nor bot developers), is there already value in a clean comparison layer, or does it only become useful once calibration / MOS / edge summary is added?

I’m planning to eventually add market-specific MOS and other weather inputs that matter for decisions, but I’d rather learn from actual reactions first than overbuild in private.

Would something like this be worth checking during your decision, even in an early version?

*No promotion, I just want some opinions from community..

Coming at this as a student who cares about both forecasting and market-useful weather info, so apologies if this is a naive question.

My rough mental model is basically:

better LEO obs -> better initial state -> better DA/assimilation -> better forecast skill -> real decision value in energy, ag, insurance, shipping, maybe even weather-linked prediction markets.

But I’m not sure where that logic actually breaks down.

Obviously satellite data is already core to modern NWP, so this is not a “why don’t we use space data?” post. I’m more wondering whether denser / faster / more specialized LEO observations could still move the needle a lot from here, especially not just for day 1–3, but maybe even into S2S territory.

If the answer is yes, I’m kind of surprised private capital doesn’t seem more aggressive here. Spire, GeoOptics, PlanetiQ, Tomorrow.io, etc. exist, but the scale still feels small relative to how strong the science case seems from the outside.

If the answer is no, is it mostly because:

• obs quality just isn’t the main bottleneck anymore
• assimilation / model physics dominate after a certain point
• S2S skill is just intrinsically very hard to improve
• public agencies capture most of the value, so private ROI is weak
• buyers don’t actually pay much for marginal forecast skill gains
• the business case is worse than the science case

Basically I’m trying to figure out how people in the field think about this in practice.

Is more / better LEO weather obs still one of the most underinvested levers in forecasting? Or has the frontier moved more toward modeling, coupling, and post-processing (GraphCast, Pangu, etc.)?

Also, if there are serious players I’m missing here, would love names.