Why local sensing matters
Public reference stations measure conditions at a single point, sometimes tens of miles from where you operate. The delta between that reading and your local water is not noise. It is signal. It reflects bathymetry, tidal phase, freshwater input, thermal stratification, and the specific microclimate of your site.
PurpleAir demonstrated this for air quality: sparse regulatory monitors miss local variation. In the Puget Sound region alone, a single NOAA station covers hundreds of square miles of water that behaves differently at every embayment, marina, and farm lease. The gap between "regional" and "your exact site" is where decisions get made wrong.
The comparison you see in Tab 03 is that gap, made visible. A calibrated in-situ sensor anchored to public reference data is the observational foundation a site-specific digital twin is built on. Halcyon delivers that foundation today: normalized streams from heterogeneous public sources, your local readings, and the deltas between them. The predictive model that turns this foundation into a working twin is on the roadmap below.
From observation to digital twin
[built] 1. Collect local readings from your device
[built] 2. Normalize heterogeneous public sources to common schema
[built] 3. Compute local-to-reference delta with source-aware tolerance
[built] 4. Surface delta over time as a first-class view
[partial] 5. Apply domain-specific decision rules
[partial] 6. Detect anomalies and trigger alerts
[roadmap] 7. Fit a site-specific forward model — the digital twin
[roadmap] 8. Publish forecasts and operational guidance
A digital twin is a model that predicts your site's behavior under conditions it has not yet seen. Building one requires 30+ days of co-located local readings, multi-parameter coverage, and a fitted relationship between regional drivers and local response. The data foundation you see here is what makes that fit possible later.