☀ Summer Strength Monitor

━━ DATA SOURCES ━━

Open-Meteo (29 cities): Uses ERA5 reanalysis + high-resolution national weather models. Grid resolution ~14km. Data is gridded (not station-level), so readings may be 1-2°C below IMD station data for inland cities. All YoY comparisons use the same grid cell, so relative signals are internally consistent.

Visual Crossing — Mumbai only: Uses actual METAR station data from Santa Cruz airport (IMD station 43003). This is the same data IMD publishes. Open-Meteo's grid cannot resolve Mumbai's narrow 3km-wide peninsula — every grid cell either blends with the Arabian Sea (reads 28-31°C) or falls in inland Thane (reads 37-40°C). VC solves this with direct station observation data.

5yr Baseline: Daily normals computed from 2020-2024 data (Open-Meteo) or 2022-2024 (Mumbai/VC). Per-city, per-day-of-season average. Used for "vs Normal" deviation and the green dashed line on charts.

━━ SEASON WINDOW ━━

Jan 1 – Jun 30 is the full tracking window. Data is fetched for this entire period for both current and previous year.
Summary cards, city table, regional assessment, and SSI use Mar 1 onwards only — because Jan-Feb temperatures (15-25°C in North India) would dilute the summer signal. These sections reflect actual summer intensity.
Charts, onset tracker, heating rate, and monthly heatmap use the full Jan-Jun window — because they're designed to show how summer builds up, including the winter-to-summer transition that drives onset timing.

━━ SSI (SUMMER STRENGTH INDEX) ━━

A YoY ratio measuring whether this summer is stronger or weaker than last year. Computed per city on Mar-Jun data, then averaged across all cities.

Formula: SSI = (Temp Score × 50%) + (Hot Days Score × 30%) + (Rain Score × 20%)

Temp Score (50%)	Mar avg max 2026 ÷ Mar avg max 2025. Example: 34.4°C / 31.0°C = 1.11 (11% hotter)
Hot Days Score (30%)	Days ≥35°C ratio (2026 / 2025), capped between 0.5x and 2.0x. When base year has <5 hot days, uses dampened absolute difference (+0.05 per extra day, capped at 1.3x). This prevents small-number distortion — e.g. Mumbai going from 2→7 hot days doesn't overwhelm the index.
Rain Score (20%)	Rainfall 2025 ÷ Rainfall 2026 (inverted — less rain = drier = higher score). Capped at 1.5x. Dry heat amplifies beverage demand more than humid heat.

Interpretation: SSI > 1.05 → STRONGER (bullish for summer plays) | SSI 0.95–1.05 → IN-LINE | SSI < 0.95 → WEAKER (cautious)

Important: SSI is a relative (YoY) measure. A city can show SSI = 0.95 (WEAKER) even if it's 4°C above historical normal — because last year was even hotter. Always read SSI alongside "vs Normal" for the complete picture.

━━ vs 5yr NORMAL ━━

Shows how current temperatures compare to the 2020-2024 average for the same calendar period. This is the same framework IMD uses in their bulletins (e.g. "7.6°C above normal").
WELL ABOVE (≥3°C) | ABOVE (≥1.5°C) | NEAR NORMAL (±1.5°C) | BELOW (≤-1.5°C)

For your thesis: Even when SSI is in-line (~1.0), a high deviation from normal (e.g. +2°C) means both this year AND last year are running well above historical levels — the elevated demand base is structurally sustained.

━━ ONSET TRACKER ━━

Tracks the first date each city's max temperature crosses two thresholds:
≥30°C — Fan/cooler consideration starts. AC showroom footfall picks up. Consumer durables lead indicator.
≥35°C — Impulse beverage territory. Non-linear demand uplift for cold drinks, glucose, juices, ice cream. This is the threshold that drives quarterly volume surprises.

Delta: Negative = 2026 crossed earlier than 2025 (green, bullish — more selling days).
Positive = 2026 crossed later (red — fewer selling days so far).
Example: If Delhi crosses 35°C on Mar 7 (2026) vs Mar 25 (2025) = 18d earlier. At peak-season daily run-rates, that's 18 extra days of peak demand in Q1.

━━ CHARTS ━━

Avg Temp: Daily pan-India average max temperature. Amber = 2026, grey dashed = 2025 full season, green dashed = 5yr normal. The gap between amber and green is your deviation-from-normal.
Cities ≥35°C: How many of the 30 cities crossed 35°C each day. Higher = broader geographic heat spread.
Cumulative Hot Days: Running total of city-days. Amber = ≥30°C (warm days), Red = ≥35°C (hot days). Solid = 2026, dashed = 2025. If the 2026 line is above 2025 at the same point in the season → more heat exposure × population reach → higher beverage/cooling volumes.
Heating Rate: Weekly average max temperature. The slope shows how fast summer is building. Steeper than normal (green) = earlier onset. Useful as a February leading indicator.
Rainfall: Lower rainfall during Mar-Jun = drier heat = stronger beverage demand signal. Hot + dry > hot + wet.

━━ MONTHLY HEATMAP ━━

Shows each city's deviation from its own 5yr normal for each month (Jan-Jun). Color-coded: deep red = well above normal (+4°C+), orange = above (+2°C), grey = normal, blue = below.
How to read: Scan horizontally to see a city's seasonal progression. Scan vertically to see which month was the anomaly. If March is deep red across North India but grey in South → the heat belt thesis is concentrated in the right geography.

━━ KEY RISKS & CAVEATS ━━

• Western disturbances can bring temporary relief (5-10°C drop for 2-3 days), pulling down weekly averages. This is noise, not signal — look at the trend, not individual days.
• Grid resolution bias: Open-Meteo reads 1-2°C below IMD station data due to grid averaging. All comparisons are internally consistent (same grid cell both years), so relative signals are valid.
• Extreme heat → drought risk: If heat persists without rain, rural demand can be hurt (negative for FMCG distribution). Monitor rainfall chart.
• Crude oil / LPG prices: Iran conflict → oil spike → disposable income squeeze could offset volume gains from heat. Consider macro overlay.
• SSI stabilizes by late April — with 50+ days of data, the index becomes reliable. Early March readings (21 days) can be noisy.