Joint-variance scoring across a portfolio of spots
Score N spots at once and get back not just per-spot verdicts but the joint distribution + per-spot variance contribution. Aimed at operators running multi-location bookings and at parametric-underwriting research on diversified covers, which is a research direction with qualified partners rather than a live pricing product.
Pro plan and above. Up to 50 spots per request. Runs over the same ERA5 reanalysis climatology as /v1/score/historical (this is a distribution endpoint, not a live forecast), plus cross-spot correlation; budget accordingly for larger portfolios.
What it returns
Per-spot historical score distributions (same shape as the historical endpoint) plus a portfolio-level summary: joint expected score, joint variance, per-spot variance contribution. The variance contribution tells you which spot drives the most uncertainty in your portfolio's joint outlook, useful for analysing a diversified cover or prioritising which spot to investigate first.
Request
{
"spots": [
{ "point": { lat, lng }, "activity": "kitesurfing", "weight": 1 },
{ "point": { lat, lng }, "activity": "windsurfing" },
… up to 50 spots
],
"yearsRange": { "from": 2006, "to": 2026 },
"granularity": "month",
"selector": { "month": 6 },
"correlation": { "model": "exponential", "lengthKm": 50 }
}spots[].point (not location) holds the { lat, lng } pair; weight and spotId are optional. yearsRange, granularity and selector follow the exact same shape as /v1/score/historical. Optional cancellationThreshold (0–100) marks which score band counts as a "cancelled" day for the joint-variance math.
Correlation models
none: treat spots as independent. Simplest, fastest; good when your spots are climatologically very different (Alps + Mediterranean + Atlantic).
exponential (default): covariance decays exponentially with Haversine distance, parameterised by correlation.lengthKm (default 50km). Good when your spots cluster in one region (a fleet of Tarifa schools, a Costa Blanca chain).
Use cases
- Multi-location seasonality planning: compare a network's spots month-by-month + a joint "how often is the whole network workable together" view for scheduling and marketing.
- Parametric-pricing research: feed the joint variance into a portfolio-policy analysis (the underwriting-research endpoints accept portfolio input directly; this is the unbundled version).
- Diversification analysis: which spots in the network are climatologically correlated vs. independent, so an operator or underwriter isn't over-exposed to one weather regime?