Daily and seasonal weather forecasts anchored to NOAA Weather Forecast Office guidance. Every value comes back with a high, a low, and an explicit standard deviation, so the caller can size decisions against the uncertainty instead of guessing at it. Every value also names where it came from.
{ "succeeded": true, "station_id": "NYC", "day_points": [ { "date": "2026-06-21", "high_f": { "mu": 82.1, "sigma": 3.4, "p10": 77.7, "p90": 86.5 }, "low_f": { "mu": 66.4, "sigma": 2.8, "p10": 62.8, "p90": 70.0 }, "provenance": { "source": "nws-wfo-gridpoint", "wfo": "OKX", // New York / Upton "issued_at": "2026-06-20T18:14:00Z", "model_cycle": "12Z" } } ], "method": "wfo+climatology-residual-ci", "calibration": { "coverage_p80": 0.81, "coverage_p90": 0.89 } }
What you get
A single forecast number is the wrong input to a decision. The Oasis Weather API ships the distribution instead, anchored to the same Weather Forecast Office guidance the NWS issues, with calibration receipts and per-day provenance you can audit before you commit.
Every value ships with μ, σ, and a p05–p95 confidence interval fit so its stated coverage actually holds on out-of-sample data. The receipts endpoint lets you verify coverage on your own historical window before wiring the API into anything load-bearing.
Every day in every response names its source, the issuing Weather Forecast Office, and the model cycle that produced it. If the engine falls back to climatology, the response says so. No silent substitutions, no opaque blends.
Forecasts ride on the actual gridpoint guidance from the WFO covering your station — the same authoritative source emergency managers and broadcasters use. Climatology and a 30-year observational baseline fill the gaps between forecast lead times.
Endpoints
All endpoints return the same confidence-interval envelope and the same provenance fields. The credit cost varies by how much work the request does. Coverage is the contiguous US in v0; Alaska, Hawaii, and US territories are on the roadmap.
Pricing
Credits debit per call, weighted by endpoint cost. Plans renew monthly; unused credits do not roll over.
Pro
$99 /mo
Team
$499 /mo
Enterprise
Custom
Why this exists
They give you a number — a high, a low, a probability of precipitation — and leave you to figure out how much to trust it. That is fine for a weather widget; it is not fine for a trade, a control loop, or a multi-day plan.
The interval is what tells you how seriously to take the number. Oasis Weather is built around shipping intervals that hold their stated coverage on real out-of-sample data, with the per-day provenance and the receipts to prove it.
Oasis Weather is the productized version of the forecast-calibration and verification work we already do as bespoke engagements — energy-system modeling, microclimate work for siting, and feasibility studies where the uncertainty itself is the deliverable.
If the API does not fit your shape and you need calibrated weather wired into a larger decision system, that work is a separate conversation. Either way, the calibration discipline is the same.
Who this is for
The angle — calibrated intervals, WFO-anchored guidance, full provenance — opens specific kinds of work that a point-forecast API can't support cleanly.
Power, gas, agricultural, and weather-derivative desks that need a real distribution to size positions against. The receipts endpoint is the audit trail that satisfies a risk committee.
Grid operators, HVAC fleets, agricultural irrigation, supply-chain ETA models. Anywhere a control loop needs to weigh a worst-case against a best-case before it commits.
Atmospheric researchers, climate-impact modelers, and backtest harnesses that need provenance per value to keep results reproducible. Mixture-model parameters expose drift, regime change, and seasonal heterogeneity in a way you can cite.
Product teams building forecast UIs that want to show the range, not just the headline number — "85°F with a 7° spread, low confidence" beats "85°F" every time. Same call shape, your app decides how much of the distribution to expose.
Engineering and planning teams that need a defensible probabilistic baseline — for HVAC sizing, microclimate impact, renewable-resource assessment, or stormwater design — with cited provenance per data point.
Parametric coverage, energy hedges, weather derivatives, and event-cancellation triggers — where the strike, the payout, and the price all depend on the calibrated tail, not just the central forecast.
The free tier is enough to evaluate end-to-end against a real workload. Send a short note with the use case and the rough monthly call volume; we will mint a key while the self-serve signup is being finished.