KODON WEATHER

The national forecast doesn't know your valley.

Professional weather models (ICON, ECMWF, GFS) are extraordinary - at the scale of a 6-13 km grid box. But the decision you make happens at one point: this slope, this vineyard, this array. Between the grid and the point sits real money, and the generic forecast can't see it.

That gap is fine when the weather is just conversation. It's expensive when it drives a snow cannon, a spray rig, a grit truck, an energy bid, or a solar-market position. Those are point decisions, and they want a point forecast.

A weather station is a decade of local truth. We forecast from it.

Every automatic weather station quietly records its own microclimate for years. That history is the training set no global model has: how this spot actually behaves. We learn one model per forecast step from it, benchmark honestly against the professional baselines, and only ship what beats them where it matters.

The result isn't "better weather" in the abstract. It's a tighter number on the one variable a specific customer is paying to get right - and a measured claim about how much tighter.

Six decisions, six forecasts, one station.

Each product maps to a decision with a hard cost of being wrong. Every one is measured on held-out data (see the proof table below).

This isn't a pitch. It's measured, out-of-sample.

Every number below comes from a fully held-out test set on 9 years of real station data, benchmarked against the fair baseline - not a strawman. The figures are honest, and where a professional model still wins, we say so.

On raw temperature at synoptic scale, a well-placed operational model (ICON) is still stronger - and we say so plainly. Our edge is the hyperlocal, decision-shaped variables above.

A held-out week, independently re-verified from the raw data.

The solar forecaster, checked line-by-line against the station's own 5-minute records - training counts, held-out split and baseline all reproduced from scratch.

Nothing here is asserted - it's re-runnable on the same machine the data lives on. And here is that week, drawn straight from the held-out test set:

Near-zero cost to run, a hard number saved per customer.

The economics are unusually clean: the data is free and it runs on a Raspberry Pi, so cost-to-serve is almost nothing. Revenue comes from one measurable saving per product. Ordered by how easily the buyer can do the math themselves:

Why the unit economics work

For scale reference, not parity: Tomorrow.io, a weather-intelligence company built on satellites and ~500 staff, is valued above $1B. KODON WEATHER attacks the same value - the decision, not the map - from one sensor and a Pi.

Station in, decision out. Four layers.

Data falls through the stack from raw readings to a priced decision, all on one owned box.

The whole stack runs on a Raspberry Pi, air-gapped, next to the station. The customer's data never leaves their site, and every forecast is an auditable, re-runnable computation.

From one station to a network.

We publish the claims that failed, too.

And what we won't claim: long-range prediction (physically impossible), replacing the global forecasting systems, or beating them at synoptic scale. A product whose whole value is an honest number has to be honest about its own.