Name: Metro 7-day weather-forecast demand panel, daily
Creator: metro_weather_forecast_intel
License: https://eccc-msc.github.io/open-data/licence/readme_en/
Keywords: daily, weather, forecast, demand, ml-features, seasonality, us, canada

Forward-looking weather features for 20 US/Canadian metros (15 US + 5 CA, ICAO join keys shared with metro_degree_days and metro_daylight_intel): today + 6 days x 20 metros = ~140 (forecast_date, metro) rows per daily snapshot. US rows from the NWS gridpoint 7-day forecast; CA rows from ECCC citypageweather-realtime. Per row: temp_max_c / temp_min_c, precip_prob_max (0-100), precip_type (rain/snow/mixed/none), wind_max_kmh, short summary, lead_days (0-6), flags (heat >= 35C, deep freeze <= -15C, heavy precip PoP >= 70, snow, high wind >= 60 km/h, severe = any), and demand_disruption_score (0-100 = min(100, round(30*heat_i + 30*cold_i + 25*precip_i + 15*wind_i)) with documented ramps). Units: degC, %, km/h, 0/1 flags, 0-100 score. Caveats: forecasts, not observations — a later same-day re-run can legitimately differ; wind is honest null when the provider reports none.

Source: metro_weather_forecast_intel
Rows: 141
Columns: 23
Source cadence: Daily
Last refreshed: Sep 30, 2026
Theme: economy

Column	Type	Description
metro	string	Metro name (matches metro_degree_days).
state_province	string	US state or Canadian province.
country_code	string	ISO alpha-3 country code: USA or CAN.
icao	string	ICAO airport code of the metro's primary international airport — the join key shared with metro_degree_days.
issued_date	date	UTC date the forecast snapshot was issued. (unit: date)
forecast_date	date	Local calendar date the forecast applies to (ISO date). (unit: date)
lead_days	integer	Days ahead of the issue date: 0 = today, 6 = six days out.
temp_max_c	float	Forecast daily maximum temperature (NWS F converted). (unit: degC)
temp_min_c	float	Forecast daily minimum temperature (NWS F converted). (unit: degC)
precip_prob_max	integer	Maximum precipitation probability across the day's periods (NWS period PoP; ECCC 'N percent chance' text). (unit: %)
precip_type	string	Dominant precipitation type: rain, snow, mixed (sleet/freezing), or none.
wind_max_kmh	float	Maximum forecast wind speed for the day in km/h (NWS mph converted; ECCC hourly already km/h). Honest null when the provider reports none. (unit: km/h)
summary	string	Short provider forecast summary for the day.
heat_flag	integer	1 when temp_max_c >= 35.
deep_freeze_flag	integer	1 when temp_min_c <= -15.
heavy_precip_flag	integer	1 when precip_prob_max >= 70.
snow_flag	integer	1 when precip_type is snow or mixed.
high_wind_flag	integer	1 when wind_max_kmh >= 60.
severe_flag	integer	1 when any disruption flag is set.
demand_disruption_score	integer	0-100: min(100, round(30heat_i + 30cold_i + 25precip_i + 15wind_i)) with heat_i=clip((tmax-32)/13), cold_i=clip((-12-tmin)/18), precip_i=clip((pop-30)/70), wind_i=clip((wind-50)/50). Higher = more weather-driven demand disruption expected.
source	string	Upstream provider: 'nws' or 'eccc'.
source_url	string	Canonical upstream URL the row was built from.
row_hash	string	Deterministic row identifier: 16-hex sha256 of 'metro_wxfcst\|<icao>\|<issued>\|<forecast_date>\|<tmax>\|<tmin>\|<pop>\|<wind>'.

First 10 sample rows — a preview, not the complete dataset.

metro	state_province	country_code	icao	issued_date	forecast_date	lead_days	temp_max_c	temp_min_c	precip_prob_max	precip_type	wind_max_kmh	summary	demand_disruption_score	source	source_url	row_hash
Denver	Colorado	USA	KDEN	2026-09-30	2026-09-29	0	12.2	12.2	36	rain	9.7	Mostly Cloudy then Chance Rain Showers	2	nws	https://api.weather.gov/gridpoints/BOU/75,66/forecast	284822c29c3fbd54
Los Angeles	California	USA	KLAX	2026-09-30	2026-09-29	0	17.2	17.2	0	none	24.1	Partly Cloudy then Patchy Fog	0	nws	https://api.weather.gov/gridpoints/LOX/149,41/forecast	abfa7b6eb0150025
Philadelphia	Pennsylvania	USA	KPHL	2026-09-30	2026-09-29	0	16.1	16.1	0	none	8	Mostly Cloudy then Patchy Fog	0	nws	https://api.weather.gov/gridpoints/PHI/48,75/forecast	ec0a584e430b3315
Phoenix	Arizona	USA	KPHX	2026-09-30	2026-09-29	0	20	20	13	rain	16.1	Slight Chance Showers And Thunderstorms then Partly Cloudy	0	nws	https://api.weather.gov/gridpoints/PSR/161,57/forecast	e9f05c1f5a651b0a
Seattle	Washington	USA	KSEA	2026-09-30	2026-09-29	0	10.6	10.6	26	rain	8	Chance Light Rain then Mostly Cloudy	0	nws	https://api.weather.gov/gridpoints/SEW/124,60/forecast	7d20b3f3ca1dd7b7
San Francisco	California	USA	KSFO	2026-09-30	2026-09-29	0	13.3	13.3	0	none	14.5	Mostly Clear	0	nws	https://api.weather.gov/gridpoints/MTR/85,98/forecast	b83ae637da4bb2bd
Ottawa	Ontario	CAN	CYOW	2026-09-30	2026-09-30	1	21	10	0	none	20	Cloudy. Becoming a mix of sun and cloud in the morning. Fog patches dissipating in the morning. Wind becoming south 20 km/h in the morning. High 21. Humidex 25.	0	eccc	https://api.weather.gc.ca/collections/citypageweather-realtime/items/on-118	6a40635d453d927a
Montreal	Quebec	CAN	CYUL	2026-09-30	2026-09-30	1	21	10	0	none	15	Increasing cloudiness in the morning. Fog patches dissipating in the morning. High 21. UV index 4 or moderate.	0	eccc	https://api.weather.gc.ca/collections/citypageweather-realtime/items/qc-147	bdfadd7e55aa599a
Vancouver	British Columbia	CAN	CYVR	2026-09-30	2026-09-30	1	15	9	0	none	15	Clearing. Fog patches dissipating in the morning. High 15 except 18 inland. UV index 3 or moderate.	0	eccc	https://api.weather.gc.ca/collections/citypageweather-realtime/items/bc-74	0037f8adbb61f594
Calgary	Alberta	CAN	CYYC	2026-09-30	2026-09-30	1	13	1	0	none	20	Clearing early in the morning. Wind becoming northwest 20 km/h near noon. High 13. UV index 3 or moderate.	0	eccc	https://api.weather.gc.ca/collections/citypageweather-realtime/items/ab-52	7849ce554be69e55

Sep 30, 2026Current
20260930T065033Z-579b26ee3954 · sha256 579b26ee3954…
141 rows · -9 rows vs previous
Sep 30, 2026
20260930T020309Z-e0ca20703cfe · sha256 e0ca20703cfe…
150 rows · +0 rows vs previous
Sep 30, 2026
20260930T020151Z-e51f325d3d85 · sha256 e51f325d3d85…
150 rows · +11 rows vs previous
Sep 29, 2026
20260929T104049Z-7fc594b13436 · sha256 7fc594b13436…
139 rows · first snapshot

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/metro_weather_forecast_intel/metro_weather_forecast_daily" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/metro_weather_forecast_intel/metro_weather_forecast_daily").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/metro_weather_forecast_intel/metro_weather_forecast_daily

Tip: fetch /llms.txt for the full machine-readable catalog.

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

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Pinned to snapshot 20260930T065033Z-579b26ee3954 and its content hash, so readers get exactly the data you used.

APA

metro_weather_forecast_intel. (2026). Metro 7-day weather-forecast demand panel, daily [Data set, snapshot 20260930T065033Z-579b26ee3954, sha256 579b26ee3954]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/metro_weather_forecast_intel/metro_weather_forecast_daily?snapshot=20260930T065033Z-579b26ee3954

BibTeX

@misc{dz_metro_weather_forecast_intel_metro_weath_579b26ee,
  title        = {{Metro 7-day weather-forecast demand panel, daily}},
  author       = {{metro\_weather\_forecast\_intel}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/metro_weather_forecast_intel/metro_weather_forecast_daily?snapshot=20260930T065033Z-579b26ee3954}},
  note         = {Snapshot 20260930T065033Z-579b26ee3954, sha256 579b26ee395467f113a526d3c9e9eebb5857d11af39f11d7dc0f56c2a9c72672; accessed 2026-09-30}
}

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Metro 7-day weather-forecast demand panel, daily

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