Hours without rain or strong thermal stress.

Two ways to ask the engine.

Forelore's audit engine answers two queries over the same 25-year climate floor dataset:

The Harbor Island case study above is the first query type, applied to a wedding date selection. The same engine serves vacation rentals, property buys, festivals, multi-venue portfolios, and destination comparisons.

Microclimate-aware. Not a regional average.

Forelore audits at 0.1° lat/lon resolution — each pixel is approximately 11 km × 11 km, the size of a small coastal town. Two villas 20 km apart on the same coast may have meaningfully different floors. The audit is venue-specific: your exact location, not a regional or city-level smoothed average.

Rain — the primary filter.

An outdoor event lives or dies on rain. Forelore uses NASA GPM IMERG Final V07 — the gold-standard satellite precipitation dataset, with 0.1° lat/lon resolution (~11 km) and 30-minute temporal cadence. Each 30-minute slot counts as rain-free below 0.1 mm/h rain rate. No rain, no event lost.

Why satellite rain, not reanalysis? Validated against NOAA Stage IV gauge ground truth, ERA5 reanalysis exhibits a −2.3-hour drizzle bias in places like Miami — counting low-rate rain the gauges don't register. IMERG matches the gauge within ±0.4 hours. The choice of rain dataset alone can shift the audit by hours per day.

Then: strong thermal stress.

Once rain has cleared, what's left is thermal comfort. A 30°C day with full sun, no wind, and 80% humidity feels around 38°C to a person standing outside in formal clothing — the thermometer reads 30°C, the body feels 38°C. Forelore measures what your people actually feel, using the UTCI (Universal Thermal Climate Index) — the scientific standard for outdoor thermal stress.

UTCI uses 4 inputs (not just temperature).

UTCI consumes air temperature, mean radiant temperature (sun + ground + buildings), wind speed, and water vapor pressure (humidity). It accounts for all of them simultaneously using a 6th-order polynomial fit (Bröde et al. 2012, IJBM). Below −13°C or above +32°C UTCI, the body experiences strong thermal stress — those hours are excluded from "pleasant" in the audit. The UTCI dataset is Di Napoli et al. 2021 (DOI 10.1002/gdj3.102), published in Geoscience Data Journal.

Why ~90% one-sided confidence.

When Forelore reports a "high-confidence floor" of X hours of pleasant weather, what we mean is: at this venue × date, 24 of the last 25 observed years matched or exceeded this floor. That works out to approximately 90% one-sided confidence — the inter-annual climate variability has been measured directly, not modeled.

Why "~90%" and not "96%" (= 24/25 raw)? Because successive climate years are not statistically independent. Multi-year cycles like ENSO and AMO mean that 25 calendar years carry roughly 18–22 effective independent samples. After accounting for that autocorrelation, the confidence is conservative at ~90%.

What this means for you: in any single future year at this venue on this date, there is approximately a 1-in-10 chance that the pleasant-hours value will fall below the floor. The audit's job is to tell you what the worst plausible value has been.

ENSO sensitivity: our 2001–2025 sample contains 3 La Niña / 5 El Niño / 17 Neutral years (long-run climatology ~25/30/45%). Bootstrap re-weighting to climatological ENSO frequencies moves the audited floor by ≤0.3 hours — the 2nd-lowest order statistic already absorbs the worst La Niña year in the sample directly.

Full statistical derivation (Weibull plotting position, climate-autocorrelation adjustment via Bretherton 1999 + Wilks 2011, ENSO robustness analysis, references to David & Nagaraja 2003 and IPCC AR6): read the methodology note.