Mission Statement
From the TerraPulse Team
Not just collecting data — collapsing uncertainty into insight.
Earth's systems are coupled. The Sun's wind touches the crust. The Moon's gravity biases faults. Temperature inversions trap pollutants. Tides modulate waves. Drought feeds wildfire feeds air quality. These connections are real, measurable, and — with enough data and statistical discipline — predictable.
TerraPulse exists to measure those connections with mathematical rigor. We ingest environmental data from government and scientific sources across every domain — seismology, solar physics, meteorology, oceanography, volcanology, atmospheric chemistry, hydrology, and beyond. We normalize it into a unified schema. And then we ask: what predicts what?
What separates us
Dashboards show you what happened. We show you what's connected, what predicts what, and where the models are wrong — with p-values, Bonferroni corrections, and honest null results.
When our initial analysis found solar wind correlated with earthquakes at r=0.54, we didn't publish — we backfilled six months of data and watched the effect shrink to r=0.09. That correction is the science. The wave function collapses one honest analysis at a time.
The four layers
The Living Map
Real-time global visualization — earthquakes pulsing, volcanoes glowing, storm alerts sweeping, solar wind in the header. The planet's vital signs at a glance.
The Anomaly Engine
Every night, the system scores each day across all metrics. When multiple domains spike simultaneously, it flags a compound event and publishes the analysis automatically.
The Prediction Products
Earthquake-tidal forecasts. AQI-from-weather models. A daily compound risk index combining solar, seismic, atmospheric, and oceanic anomalies into one number. Math as a service.
The Paper Machine
Every prediction product is backed by a reproducible workspace with Schuster tests, Granger causality, and published papers. The math is open. The code is on GitHub. The data is on the API.
The Data Garden
We call TerraPulse the Data Garden — where datasets cross-pollinate. Michael plants the seeds (URLs to government data sources). The autosense fetcher waters them (ingestion). The Lab cross-pollinates them (Granger causality, compound anomalies, tidal forcing). Every new dataset makes the existing ones more valuable.
Sunspots from 1818 meet earthquakes from 2021 meet tornadoes from 1950 meet gravitational wave mergers from 2015 meet fireballs from 1998 meet the Keeling Curve from 1974. No single discipline would put these in the same database. We did. And the connections we found surprised us.
The truth we're collapsing toward
Earth systems are coupled. The coupling is measurable. And the measurements can be turned into forecasts — if you have the data breadth and the statistical discipline to separate signal from artifact.
The wave function collapses one honest analysis at a time.