Canadian metro observed-weather demand panel (daily)

Commercial use OKFreshLicense: LicenseRef-ECCC-Data-Services-End-use-2.1.1

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Daily observed-weather demand panel for the 5 Canadian metros of the 20-metro family (Toronto, Montreal, Vancouver, Calgary, Ottawa) from ECCC MSC GeoMet climate-daily, fetched keyless: the trailing 365 days of observed TMAX/TMIN, precipitation, snowfall and snow depth at the metro airport station, and the 10-year (2016-2025) day-of-year climatology baseline from the same collection. Per (date, metro): daily mean temperature, HDD18/CDD18 degree-days, temperature and precipitation anomaly z-scores versus a 10-year (2016-2025) day-of-year climatology, heat-wave / cold-snap / heavy-rain / snow-day flags, a pleasant-day flag, and a documented 0-100 weather-demand score with per-day cross-metro rank — high means pleasant foot-traffic/shopping weather. Same ICAO metro keys as the US sibling metro_weather_demand_intel (no key collisions), so the two panels stack into one 20-metro North America panel; same formulas throughout. One deliberate schema difference: ECCC climate-daily publishes daily maximum gust only (no mean-wind column), so the CA panel omits awnd_ms/high_wind_flag and the score's wind penalty is 0 by construction. ECCC data is under the ECCC Data Services End-use Licence v2.1.1 (commercial reuse allowed, attribution required). Who joins this: an online shop or subscription business joins (date, icao) to daily orders/signups to model weather-driven demand in Canada; a sales team joins it to territory-day activity.

Source: Environment and Climate Change Canada
Rows: 1,825
Columns: 30
Source cadence: Daily
Last refreshed: Oct 1, 2026
Theme: environment

Column	Type	Description
date	string	Calendar date of the observation day (station local day), ISO date. Primary join key with icao; stacks 1:1 with the US sibling metro_weather_demand_intel. (unit: ISO date)
metro	string	Metro name, shared with the metro_degree_days / metro_daylight_intel / metro_thermal_demand_intel family for 1:1 joins. (unit: name)
state_prov	string	Canadian province containing the metro (airport station). (unit: name)
country_code	string	ISO alpha-3 country code (CAN for every row). (unit: ISO 3166-1 alpha-3)
icao	string	Metro airport ICAO code (CYYZ, CYUL, CYVR, CYYC, CYOW): the stable metro key shared with the sibling weather datasets. Primary join key with date. (unit: code)
lat	float	Airport station latitude, decimal degrees. (unit: decimal degrees)
lon	float	Airport station longitude, decimal degrees. (unit: decimal degrees)
tz_name	string	IANA timezone of the metro. (unit: IANA name)
tmax_c	float	Observed daily maximum temperature (ECCC MAX_TEMPERATURE, degC). Null where the station did not report; never zero-filled. (unit: degC)
tmin_c	float	Observed daily minimum temperature (ECCC MIN_TEMPERATURE, degC). Null where the station did not report. (unit: degC)
tmean_c	float	Daily mean temperature as (TMAX+TMIN)/2. Null when either is null. (unit: degC)
prcp_mm	float	Observed daily precipitation (ECCC TOTAL_PRECIPITATION, mm). Null where the station did not report. (unit: mm)
snow_mm	float	Observed daily snowfall (ECCC TOTAL_SNOW, cm -> mm). Null where not reported. (unit: mm)
snow_depth_mm	float	Observed snow on ground (ECCC SNOW_ON_GROUND, cm -> mm). Null where not reported. (unit: mm)
hdd18_c	float	Heating degree-days, base 18 degC: max(0, 18 - tmean_c). Null when tmean_c is null. Base 18 matches the ECCC convention. (unit: degree-days (degC))
cdd18_c	float	Cooling degree-days, base 18 degC: max(0, tmean_c - 18). Null when tmean_c is null. (unit: degree-days (degC))
tmax_anom_z	float	TMAX anomaly z-score versus the station's 10-year (2016-2025) day-of-year climatology: (tmax - clim_mean)/max(clim_std, 0.5). Null when the climatology cell has < 7 baseline years or tmax is null. (unit: z-score)
tmin_anom_z	float	TMIN anomaly z-score versus the 10-year day-of-year climatology (std floor 0.5 degC). Null when the climatology cell has < 7 baseline years or tmin is null. (unit: z-score)
tavg_anom_z	float	TAVG=(TMAX+TMIN)/2 anomaly z-score versus the 10-year day-of-year climatology (std floor 0.5 degC). (unit: z-score)
prcp_anom_z	float	Precipitation anomaly z-score versus the 10-year day-of-year climatology: (prcp - clim_mean)/max(clim_std, 2.0 mm). (unit: z-score)
heat_wave_flag	integer	1 on days inside a run of >= 3 consecutive calendar days with tmax_c >= 35.0, else 0. Same threshold as the US sibling for comparability (rare in Canada by design). Runs are computed on the station's full fetched history so window edges cannot split a run. (unit: 0/1 flag)
cold_snap_flag	integer	1 on days inside a run of >= 2 consecutive calendar days with tmin_c <= -12.0, else 0. (unit: 0/1 flag)
heavy_rain_flag	integer	1 when prcp_mm >= 25.0, else 0 (null when prcp null). (unit: 0/1 flag)
snow_day_flag	integer	1 when snow_mm >= 25.0 (one inch), else 0 (null when snow null). (unit: 0/1 flag)
pleasant_day_flag	integer	1 when 15 <= tmean_c <= 26 and prcp_mm < 1.0 and tmax_c < 32.0 and tmin_c > 5.0, else 0 (null when any input null). (unit: 0/1 flag)
weather_demand_score	float	Documented 0-100 pleasant-weather proxy: 100 minus min(45, 2.2\|tmean_c - 21\|) for temperature distance from the 21 degC ideal, minus min(30, 1.2prcp_mm) for rain, minus 15/15/10/10 for heat-wave / cold-snap / heavy-rain / snow-day days (floored at 0). High = pleasant foot-traffic/shopping weather. Null when tmean_c is null. The US sibling's wind penalty is absent: ECCC climate-daily has no mean-wind column, so the wind term is 0 by construction. (unit: 0-100 score)
weather_demand_rank	integer	Per-date dense rank of weather_demand_score across the 5 metros, 1 = best weather that day. Null when the score is null. (unit: dense rank)
as_of	string	Latest observation date in the fetched panel (ISO date): the latest date on which >= 4 stations reported. Identical on every row and across runs on the same vintage, so a re-fetch of unchanged data produces a byte-identical panel and the ingest is a no-op. (unit: ISO date)
source_station	string	ECCC climate identifier behind the row's observations. (unit: code)
row_hash	string	Deterministic 16-hex id: sha256('WXDEMAND\|<icao>\|<date>') — same scheme as the US sibling, collision-free across the two panels because the ICAO keys do not overlap. (unit: hex)

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

date	metro	state_prov	country_code	icao	lat	lon	tz_name	tmax_c	tmin_c	tmean_c	prcp_mm	snow_mm	snow_depth_mm	hdd18_c	tmax_anom_z	tmin_anom_z	tavg_anom_z	prcp_anom_z	pleasant_day_flag	weather_demand_score	weather_demand_rank	as_of	source_station	row_hash
2025-10-02	Ottawa	Ontario	CAN	CYOW	45.317	-75.667	America/Toronto	18.6	2.4	10.5	0	0	—	7.5	0.56	-0.81	-0.14	-0.75	0	76.9	4	2026-10-01	6106001	c93577ac818596ed
2025-10-02	Montreal	Quebec	CAN	CYUL	45.47	-73.74	America/Toronto	18.7	3.9	11.3	0	—	—	6.7	0.65	-1.22	-0.29	-0.78	0	78.66	3	2026-10-01	702S006	a80a3724d3f9e6d4
2025-10-02	Vancouver	British Columbia	CAN	CYVR	49.195	-123.184	America/Vancouver	17.6	11	14.3	10.3	0	—	3.7	0.67	—	—	2.01	0	72.9	5	2026-10-01	1108395	cb5d481098145100
2025-10-02	Calgary	Alberta	CAN	CYYC	51.123	-114.013	America/Edmonton	17.9	8.4	13.1	0	0	—	4.9	0.59	1.79	1.06	-0.37	0	82.73	2	2026-10-01	3031092	c8aaa425d5f5ae87
2025-10-02	Toronto	Ontario	CAN	CYYZ	43.677	-79.631	America/Toronto	18.8	9.5	14.2	0	0	—	3.8	-0.13	-0.13	-0.14	-0.63	0	84.93	1	2026-10-01	6158731	15afe33f36ae65ce
2025-10-03	Ottawa	Ontario	CAN	CYOW	45.317	-75.667	America/Toronto	25.4	4.3	14.8	0	0	—	3.2	1.08	-0.51	0.53	-0.35	0	86.47	3	2026-10-01	6106001	0fda981c0364ae39
2025-10-03	Montreal	Quebec	CAN	CYUL	45.47	-73.74	America/Toronto	22.4	8.3	15.3	0	—	—	2.7	—	—	—	—	1	87.57	2	2026-10-01	702S006	34ef582a8d9b0b4b
2025-10-03	Vancouver	British Columbia	CAN	CYVR	49.195	-123.184	America/Vancouver	15.7	8	11.8	0	0	—	6.2	-0.05	0.32	0.16	-0.13	0	79.87	5	2026-10-01	1108395	47d1982b10ef5e9c
2025-10-03	Calgary	Alberta	CAN	CYYC	51.123	-114.013	America/Edmonton	18.5	6.8	12.7	0	0	—	5.3	0.6	1.19	0.87	-0.04	0	81.63	4	2026-10-01	3031092	b536685309b7750c
2025-10-03	Toronto	Ontario	CAN	CYYZ	43.677	-79.631	America/Toronto	24.3	8.9	16.6	0	0	—	1.4	0.83	-0.18	0.44	-0.45	1	90.32	1	2026-10-01	6158731	829479e55f9e89b5

Profiled Oct 2, 2026 from snapshot 20261001T235646Z-7437a91d70dd

Measured

Completeness: 95%
Rows: 1,825
Columns: 30
Columns with gaps: 10

Rows per yeardate · 2025–2026 · 2 years

20252026

Column	Missing	Distinct	Range	Distribution
datevarchar	0%	575	—	2025-10-025 2025-10-035 2025-10-045
metrovarchar	0%	5	—	Calgary365 Montreal365 Ottawa365
state_provvarchar	0%	3	—	Ontario730 Alberta365 British Columbia365
country_codevarchar	0%	1	—	CAN1,825
icaovarchar	0%	4	—	CYOW365 CYUL365 CYVR365
latdouble	0%	5	43.68 → 51.12median 45.47
londouble	0%	5	-123.18 → -73.74median -79.63
tz_namevarchar	0%	3	—	America/Toronto1,095 America/Edmonton365 America/Vancouver365
tmax_cdouble	2.5%	434	-22.1 → 36.8median 13.3	36 outside 1st–99th percentile
tmin_cdouble	2.5%	519	-28 → 24median 4.6	36 outside 1st–99th percentile
tmean_cdouble	2.5%	491	-24.4 → 30median 8.8	35 outside 1st–99th percentile
prcp_mmdouble	0.77%	198	0 → 118.4median 0	19 outside 1st–99th percentile
snow_mmdouble	20.4%	57	0 → 462median 0	14 outside 1st–99th percentile
snow_depth_mmdouble	71.6%	57	0 → 560median 80	6 outside 1st–99th percentile
hdd18_cdouble	0%	355	0 → 42.4median 8.9	18 outside 1st–99th percentile
cdd18_cdouble	0%	82	0 → 12median 0	19 outside 1st–99th percentile
tmax_anom_zdouble	13.7%	486	-4.16 → 5.65median -0.14	32 outside 1st–99th percentile
tmin_anom_zdouble	14%	472	-4.89 → 4.78median -0.05	32 outside 1st–99th percentile
tavg_anom_zdouble	15.1%	479	-4.13 → 4.19median -0.1	31 outside 1st–99th percentile
prcp_anom_zdouble	6.9%	392	-1.23 → 21.51median -0.35	33 outside 1st–99th percentile
heat_wave_flagbigint	0%	1	0 → 0median 0
cold_snap_flagbigint	0%	2	0 → 1median 0
heavy_rain_flagbigint	0%	2	0 → 1median 0
snow_day_flagbigint	0%	2	0 → 1median 0
pleasant_day_flagbigint	0%	2	0 → 1median 0
weather_demand_scoredouble	0%	964	8.64 → 100median 68.86	31 outside 1st–99th percentile
weather_demand_rankbigint	0%	5	1 → 5median 3
as_ofvarchar	0%	1	—	2026-10-011,825
source_stationvarchar	0%	5	—	1108395365 3031092365 6106001365
row_hashvarchar	0%	1,968	—	0034cb1b84b052ac1 0055568b7a7faa291 0089f7914f46812a1

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/ca_metro_weather_demand_intel/ca_metro_weather_demand_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/ca_metro_weather_demand_intel/ca_metro_weather_demand_daily

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

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Pinned to snapshot 20261001T235646Z-7437a91d70dd and its content hash, so readers get exactly the data you used.

APA

Environment and Climate Change Canada. (2026). Canadian metro observed-weather demand panel (daily) [Data set, snapshot 20261001T235646Z-7437a91d70dd, sha256 7437a91d70dd]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/ca_metro_weather_demand_intel/ca_metro_weather_demand_daily?snapshot=20261001T235646Z-7437a91d70dd

BibTeX

@misc{dz_ca_metro_weather_demand_intel_ca_metro_w_7437a91d,
  title        = {{Canadian metro observed-weather demand panel (daily)}},
  author       = {{Environment and Climate Change Canada}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/ca_metro_weather_demand_intel/ca_metro_weather_demand_daily?snapshot=20261001T235646Z-7437a91d70dd}},
  note         = {Snapshot 20261001T235646Z-7437a91d70dd, sha256 7437a91d70dd94aa50b69426ab226a4b3937fed7d104d7547a93cd4d34cfb0f9; accessed 2026-10-02}
}

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Canadian metro observed-weather demand panel (daily)

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