Pull the current METAR reading, the multi-model forecast trajectory to settlement, and the model that's calling it, fetched on demand from AWC, IEM and Open-Meteo, normalized in your own runtime. Cached locally. TypeScript at parity.
1from mostlyright import weather 2 3# live obs + forecast trajectory, cached local 4df = weather.pairs(station, today, today)
Current observations from the AWC feed, reconciled into a single NOW
reading per station: the same number the contract settles against.
NCEP, ECMWF, DWD, Météo-France and more via Open-Meteo, each with a conservative
issued_at so live and backtest never see a forecast before it existed.
Past observations, the live NOW point, and the forecast curve to the contract close, one continuous, settlement-aligned series.
Every reading is timestamped and unit-correct, US extremes in °F, international in °C, so the live curve lines up with the contract's buckets, not a converted approximation.
The same research() call ships in two runtimes. Python drops into
notebooks, pandas pipelines, and agent tools; the TypeScript build runs as an ES module in
Node, inside browser and Web Workers, on Cloudflare/Bun edge, or as one IIFE tag. CSP-clean,
no eval.
1<!-- one tag (bundle from your access distribution) --> 2<script src="/vendor/mostlyright.global.js"></script> 3<script> 4 dataset("KNYC", d0, d1) 5 .then(console.log); 6</script>
Access is currently by request. Tell us about your use case and we will get your team set up with the SDK.
Pass a station and a date window. First call warms the local cache; the rest are offline.
Schema-versioned rows, JSON-serializable, drop into a chart, an agent tool, or a backtest.