690691ea36
- obt_cot_positioning.sql: replace final SELECT * with explicit column list so linter can resolve schema without foundation.fct_cot_positioning in DB - fct_weather_daily.sql: fix HASH(location_id, src."date") → located."date" (cast_and_clean CTE references FROM located, not FROM src) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
126 lines
5.1 KiB
SQL
126 lines
5.1 KiB
SQL
/* Foundation fact: daily weather observations for 12 coffee-growing regions. */
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/* Source: Open-Meteo (ERA5 reanalysis archive + forecast model for recent days) */
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/* Landing: LANDING_DIR/weather/{location_id}/{year}/{date}.json.gz */
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/* One file per (location_id, date). Content: flat Open-Meteo JSON per day. */
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/* Open-Meteo returns parallel arrays; execute.py splits them into per-day files. */
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/* Grain: (location_id, observation_date) — one row per location per day. */
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/* Dedup key: hash(location_id, date) — past weather is immutable. */
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/* location_id is parsed from filename: split(filename, '/')[-3] */
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/* Path structure: .../weather/{location_id}/{year}/{date}.json.gz */
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/* Crop stress flags: */
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/* is_frost — temp_min_c < 2.0°C (ICO Arabica frost damage threshold) */
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/* is_heat_stress — temp_max_c > 35.0°C (photosynthesis impairment) */
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/* is_drought — precipitation_mm < 1.0 (agronomic dry day) */
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/* is_high_vpd — vpd_max_kpa > 1.5 (significant plant water stress) */
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/* in_growing_season — simplified month-range flag by variety */
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MODEL (
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name foundation.fct_weather_daily,
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kind INCREMENTAL_BY_TIME_RANGE (
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time_column observation_date
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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 src AS (
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/* Open-Meteo files are flat JSON: all variables at top level (no nested structs). */
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/* read_json(format='auto') infers column types directly from the numeric values. */
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SELECT
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*
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FROM READ_JSON(@weather_glob(), format = 'auto', compression = 'gzip', filename = TRUE)
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), located AS (
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SELECT
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src.*,
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STR_SPLIT(filename, '/')[-3] AS location_id, /* location_id is the 3rd-from-last path segment: */
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/* e.g. .../weather/brazil_minas_gerais/2024/2024-01-15.json.gz → 'brazil_minas_gerais' */
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TRY_CAST(src."date" AS DATE) AS observation_date
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FROM src
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), cast_and_clean AS (
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SELECT
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location_id,
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observation_date,
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/* Temperature (°C) */
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TRY_CAST(located.temperature_2m_min AS DOUBLE) AS temp_min_c,
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TRY_CAST(located.temperature_2m_max AS DOUBLE) AS temp_max_c,
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TRY_CAST(located.temperature_2m_mean AS DOUBLE) AS temp_mean_c,
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/* Precipitation (mm total for the day) */
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COALESCE(TRY_CAST(located.precipitation_sum AS DOUBLE), 0.0) AS precipitation_mm,
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/* Humidity (% — daily max) */
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TRY_CAST(located.relative_humidity_2m_max AS DOUBLE) AS humidity_max_pct,
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/* Cloud cover (% — daily mean) */
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TRY_CAST(located.cloud_cover_mean AS DOUBLE) AS cloud_cover_mean_pct,
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/* Wind (m/s max — Open-Meteo requested with wind_speed_unit=ms) */
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TRY_CAST(located.wind_speed_10m_max AS DOUBLE) AS wind_max_speed_ms,
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/* ET₀ (mm/day — FAO Penman-Monteith; direct crop water demand signal) */
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TRY_CAST(located.et0_fao_evapotranspiration AS DOUBLE) AS et0_mm,
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/* VPD (kPa — max; >1.5 kPa = significant plant water stress) */
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TRY_CAST(located.vapour_pressure_deficit_max AS DOUBLE) AS vpd_max_kpa,
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/* Crop stress flags */
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TRY_CAST(located.temperature_2m_min AS DOUBLE) < 2.0 AS is_frost,
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TRY_CAST(located.temperature_2m_max AS DOUBLE) > 35.0 AS is_heat_stress,
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COALESCE(TRY_CAST(located.precipitation_sum AS DOUBLE), 0.0) < 1.0 AS is_drought,
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TRY_CAST(located.vapour_pressure_deficit_max AS DOUBLE) > 1.5 AS is_high_vpd,
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HASH(location_id, located."date") AS hkey,
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filename
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FROM located
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WHERE
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NOT observation_date IS NULL AND NOT location_id IS NULL AND location_id <> ''
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), deduplicated AS (
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SELECT
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ANY_VALUE(location_id) AS location_id,
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ANY_VALUE(observation_date) AS observation_date,
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ANY_VALUE(temp_min_c) AS temp_min_c,
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ANY_VALUE(temp_max_c) AS temp_max_c,
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ANY_VALUE(temp_mean_c) AS temp_mean_c,
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ANY_VALUE(precipitation_mm) AS precipitation_mm,
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ANY_VALUE(humidity_max_pct) AS humidity_max_pct,
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ANY_VALUE(cloud_cover_mean_pct) AS cloud_cover_mean_pct,
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ANY_VALUE(wind_max_speed_ms) AS wind_max_speed_ms,
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ANY_VALUE(et0_mm) AS et0_mm,
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ANY_VALUE(vpd_max_kpa) AS vpd_max_kpa,
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ANY_VALUE(is_frost) AS is_frost,
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ANY_VALUE(is_heat_stress) AS is_heat_stress,
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ANY_VALUE(is_drought) AS is_drought,
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ANY_VALUE(is_high_vpd) AS is_high_vpd,
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hkey
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FROM cast_and_clean
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GROUP BY
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hkey
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)
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SELECT
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d.observation_date,
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d.location_id,
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loc.name AS location_name,
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loc.country,
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loc.lat,
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loc.lon,
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loc.variety,
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d.temp_min_c,
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d.temp_max_c,
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d.temp_mean_c,
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d.precipitation_mm,
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d.humidity_max_pct,
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d.cloud_cover_mean_pct,
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d.wind_max_speed_ms,
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d.et0_mm,
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d.vpd_max_kpa,
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d.is_frost,
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d.is_heat_stress,
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d.is_drought,
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d.is_high_vpd,
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CASE loc.variety
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WHEN 'Arabica'
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THEN EXTRACT(MONTH FROM d.observation_date) BETWEEN 4 AND 10
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WHEN 'Robusta'
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THEN EXTRACT(MONTH FROM d.observation_date) BETWEEN 4 AND 11
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ELSE FALSE
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END AS in_growing_season /* Growing season: simplified month-range flag by variety. */
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/* Arabica: Apr–Oct (covers northern + southern hemisphere risk windows). */
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/* Robusta: Apr–Nov (Vietnam/Indonesia main cycle). */
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FROM deduplicated AS d
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LEFT JOIN seeds.weather_locations AS loc
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ON d.location_id = loc.location_id
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WHERE
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d.observation_date BETWEEN @start_ds AND @end_ds
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