feat(transform): add serving.weather_daily with rolling analytics and crop stress index
Incremental serving model for 12 coffee-growing locations. Adds: - Rolling aggregates: precip_sum_7d/30d, temp_mean_30d, temp_anomaly, water_balance_7d - Gaps-and-islands streak counters: drought_streak_days, heat_streak_days, vpd_streak_days - Composite crop_stress_index 0–100 (drought 30%, water deficit 25%, heat 20%, VPD 15%, frost 10%) - lookback 90: ensures rolling windows and streak counters see sufficient history on daily runs Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Deeman
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transform/sqlmesh_materia/models/serving/weather_daily.sql
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transform/sqlmesh_materia/models/serving/weather_daily.sql
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/* Serving mart: daily weather analytics for 12 coffee-growing regions. */
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/* Source: foundation.fct_weather_daily (already has seed join for location metadata). */
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/* Adds rolling aggregates, water balance, gaps-and-islands streak counters, */
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/* and a composite crop stress index (0–100) as a single severity gauge. */
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/* Grain: (location_id, observation_date) */
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/* Lookback 90: rolling windows reach up to 30 days, streak counters can extend */
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/* up to ~90 days; without lookback a daily run sees only 1 row and all window */
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/* functions degrade to single-row values. */
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MODEL (
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name serving.weather_daily,
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kind INCREMENTAL_BY_TIME_RANGE (
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time_column observation_date,
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lookback 90
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),
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grain (location_id, observation_date),
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start '2020-01-01',
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cron '@daily'
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);
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WITH base AS (
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SELECT
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observation_date,
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location_id,
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location_name,
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country,
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lat,
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lon,
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variety,
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temp_min_c,
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temp_max_c,
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temp_mean_c,
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precipitation_mm,
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humidity_max_pct,
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cloud_cover_mean_pct,
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wind_max_speed_ms,
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et0_mm,
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vpd_max_kpa,
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is_frost,
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is_heat_stress,
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is_drought,
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is_high_vpd,
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in_growing_season,
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/* Rolling precipitation — w7 = trailing 7 days, w30 = trailing 30 days */
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SUM(precipitation_mm) OVER w7 AS precip_sum_7d_mm,
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SUM(precipitation_mm) OVER w30 AS precip_sum_30d_mm,
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/* Rolling temperature baseline */
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AVG(temp_mean_c) OVER w30 AS temp_mean_30d_c,
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/* Temperature anomaly: today vs trailing 30-day mean */
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temp_mean_c - AVG(temp_mean_c) OVER w30 AS temp_anomaly_c,
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/* Water balance: net daily water gain/loss (precipitation minus evapotranspiration) */
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precipitation_mm - et0_mm AS water_balance_mm,
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SUM(precipitation_mm - et0_mm) OVER w7 AS water_balance_7d_mm,
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/* Gaps-and-islands group markers for streak counting. */
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/* Pattern: ROW_NUMBER() - running_count_of_true creates a stable group ID */
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/* for each consecutive run of TRUE. Rows where flag=FALSE get a unique group ID */
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/* (so their streak length stays 0 after the CASE in with_streaks). */
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ROW_NUMBER() OVER (
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PARTITION BY location_id
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ORDER BY observation_date
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) - SUM(
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CASE WHEN is_drought THEN 1 ELSE 0 END
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) OVER (
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PARTITION BY location_id
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ORDER BY observation_date
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ROWS UNBOUNDED PRECEDING
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) AS _drought_group,
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ROW_NUMBER() OVER (
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PARTITION BY location_id
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ORDER BY observation_date
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) - SUM(
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CASE WHEN is_heat_stress THEN 1 ELSE 0 END
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) OVER (
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PARTITION BY location_id
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ORDER BY observation_date
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ROWS UNBOUNDED PRECEDING
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) AS _heat_group,
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ROW_NUMBER() OVER (
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PARTITION BY location_id
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ORDER BY observation_date
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) - SUM(
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CASE WHEN is_high_vpd THEN 1 ELSE 0 END
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) OVER (
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PARTITION BY location_id
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ORDER BY observation_date
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ROWS UNBOUNDED PRECEDING
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) AS _vpd_group
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FROM foundation.fct_weather_daily
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WHERE
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observation_date BETWEEN @start_ds AND @end_ds
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WINDOW
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w7 AS (
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PARTITION BY location_id
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ORDER BY observation_date
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ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
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),
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w30 AS (
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PARTITION BY location_id
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ORDER BY observation_date
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ROWS BETWEEN 29 PRECEDING AND CURRENT ROW
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)
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), with_streaks AS (
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SELECT
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base.*,
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/* Drought streak: number of consecutive dry days ending on observation_date. */
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/* Returns 0 when flag is FALSE (not a drought day). */
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CASE
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WHEN NOT is_drought
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THEN 0
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ELSE ROW_NUMBER() OVER (
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PARTITION BY location_id, _drought_group
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ORDER BY observation_date
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)
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END AS drought_streak_days,
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/* Heat stress streak: consecutive days with temp_max > 35°C */
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CASE
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WHEN NOT is_heat_stress
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THEN 0
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ELSE ROW_NUMBER() OVER (
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PARTITION BY location_id, _heat_group
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ORDER BY observation_date
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)
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END AS heat_streak_days,
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/* VPD stress streak: consecutive days with vpd_max > 1.5 kPa */
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CASE
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WHEN NOT is_high_vpd
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THEN 0
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ELSE ROW_NUMBER() OVER (
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PARTITION BY location_id, _vpd_group
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ORDER BY observation_date
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)
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END AS vpd_streak_days
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FROM base
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)
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SELECT
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observation_date,
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location_id,
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location_name,
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country,
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lat,
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lon,
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variety,
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temp_min_c,
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temp_max_c,
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temp_mean_c,
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precipitation_mm,
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humidity_max_pct,
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cloud_cover_mean_pct,
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wind_max_speed_ms,
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et0_mm,
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vpd_max_kpa,
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is_frost,
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is_heat_stress,
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is_drought,
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is_high_vpd,
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in_growing_season,
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ROUND(precip_sum_7d_mm, 2) AS precip_sum_7d_mm,
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ROUND(precip_sum_30d_mm, 2) AS precip_sum_30d_mm,
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ROUND(temp_mean_30d_c, 2) AS temp_mean_30d_c,
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ROUND(temp_anomaly_c, 2) AS temp_anomaly_c,
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ROUND(water_balance_mm, 2) AS water_balance_mm,
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ROUND(water_balance_7d_mm, 2) AS water_balance_7d_mm,
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drought_streak_days,
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heat_streak_days,
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vpd_streak_days,
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/* Composite crop stress index (0–100).
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Weights: drought streak 30%, water deficit 25%, heat streak 20%,
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VPD streak 15%, frost (binary) 10%.
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Each component is normalized to [0,1] then capped before weighting:
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drought: 14 days = fully stressed
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water: 20mm 7d deficit = fully stressed
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heat: 7 days = fully stressed
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vpd: 7 days = fully stressed
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frost: binary (Arabica highland catastrophic event) */
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ROUND(
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GREATEST(0.0, LEAST(100.0,
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LEAST(1.0, drought_streak_days / 14.0) * 30.0
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+ LEAST(1.0, GREATEST(0.0, -water_balance_7d_mm) / 20.0) * 25.0
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+ LEAST(1.0, heat_streak_days / 7.0) * 20.0
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+ LEAST(1.0, vpd_streak_days / 7.0) * 15.0
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+ CASE WHEN is_frost THEN 10.0 ELSE 0.0 END
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)),
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1
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) AS crop_stress_index
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FROM with_streaks
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ORDER BY
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location_id,
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observation_date
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