On a narrow (2-up) phone card, a long grade like 'Extreme shift — windier'
crammed into the card's top-right corner and wrapped into a cluster. Restructure
each card to a clean vertical stack — metric name, score, tier, detail line — so
the label always has room, on mobile and desktop alike.
Also reword the wet-bulb explainer to state plainly what it is (the lowest
temperature evaporating sweat can cool you to).
Annual scores were computed from an all-year pooled distribution, which widens
the reference spread and hides a shift confined to one season — Seattle's daily
high read 16 despite a summer high of 65. Build each metric's annual score
(and the overall) as the mean of its four seasonal divergences instead, so a
real seasonal shift shows through (that high now reads 28). Frequency read-outs
(precip wet days, wet-bulb heat-stress days) stay pooled over the year.
Also lock the by-season table to fixed, uniform columns (min-width to scroll on
a phone) so each metric lines up vertically across the seasons, and show the
per-percentile detail as the season-averaged shift.
Bumps the score cache version.
The by-season chips wrapped freely, so a metric's scores didn't line up
across seasons. Render it as a table instead — seasons as rows, one aligned
column per metric (plus an Overall column), tinted cells, horizontal scroll
on narrow screens.
- Explain on the page what wet-bulb temperature measures (the evaporative-cooling
ceiling on shedding heat), so the metric isn't opaque.
- Report the share of heat-stress "wet-bulb" days (peak wet bulb >= 26 C) vs
normal days, recent window vs the full record — mirroring the precip wet-day
frequency.
- Present the overall total as a direction-agnostic net change (magnitude only),
not "warmer/cooler"; per-metric cards still carry direction.
Bumps the score cache version.
Score how far a location's last 6 years have drifted from its full 45-year
record. For each metric and percentile category (p10/p25/p50/p75/p90), the
recent-years value is placed on the baseline distribution and the gap from the
expected percentile is the divergence — unit-free, so metrics compare directly.
Scored per meteorological season plus annual, weighted into per-metric and
overall scores (temps, humidity and feels-like weighted heaviest).
- backend/scoring.py: divergence math, seasonal slicing, precip zero-inflation
split (wet-day frequency + amount), tier mapping onto the existing temp scale.
- climate.py: derive a wet-bulb column (Stull 2011) at the read boundary, before
the humidity column is converted to absolute — via a shared _derive_metrics
wrapper at all four read sites.
- api/v2/score endpoint + build_score payload, cached on the history token with
a scoring-version key.
- frontend score page: overall hero, per-metric cards, by-season chips, and a
button-revealed summary (sentences + metrics×season table + per-percentile
detail). Score nav link across all headers.
- Tests for the scoring math, wet-bulb formula, payload shape and route.
