Canadian metro observed-weather demand panel (daily)

Usage commercial OKÀ jourLicence: LicenseRef-ECCC-Data-Services-End-use-2.1.1

Le jeu complet n’est pas encore publié — revenez après la prochaine actualisation.Télécharger l'exemple CSV Télécharger l'exemple JSONLignes d’exemple seulement (jusqu’à 20) — pas le jeu de données complet.

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.

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: Environment and Climate Change Canada
Lignes: 1 825
Colonnes: 30
Cadence de la source: Quotidienne
Dernière actualisation: 1 oct. 2026
Thème: environment

Colonne	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)

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

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

Profilé le 2 oct. 2026 à partir de l’instantané 20261001T235646Z-7437a91d70dd

Mesuré

Complétude: 95 %
Lignes: 1 825
Colonnes: 30
Colonnes incomplètes: 10

Lignes par annéedate · 2025–2026 · 2 ans

20252026

Colonne	Manquant	Distinctes	Plage	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 → 51,12médiane 45,47
londouble	0 %	5	-123,18 → -73,74médiane -79,63
tz_namevarchar	0 %	3	—	America/Toronto1 095 America/Edmonton365 America/Vancouver365
tmax_cdouble	2,5 %	434	-22,1 → 36,8médiane 13,3	36 hors du 1er–99e centile
tmin_cdouble	2,5 %	519	-28 → 24médiane 4,6	36 hors du 1er–99e centile
tmean_cdouble	2,5 %	491	-24,4 → 30médiane 8,8	35 hors du 1er–99e centile
prcp_mmdouble	0,77 %	198	0 → 118,4médiane 0	19 hors du 1er–99e centile
snow_mmdouble	20,4 %	57	0 → 462médiane 0	14 hors du 1er–99e centile
snow_depth_mmdouble	71,6 %	57	0 → 560médiane 80	6 hors du 1er–99e centile
hdd18_cdouble	0 %	355	0 → 42,4médiane 8,9	18 hors du 1er–99e centile
cdd18_cdouble	0 %	82	0 → 12médiane 0	19 hors du 1er–99e centile
tmax_anom_zdouble	13,7 %	486	-4,16 → 5,65médiane -0,14	32 hors du 1er–99e centile
tmin_anom_zdouble	14 %	472	-4,89 → 4,78médiane -0,05	32 hors du 1er–99e centile
tavg_anom_zdouble	15,1 %	479	-4,13 → 4,19médiane -0,1	31 hors du 1er–99e centile
prcp_anom_zdouble	6,9 %	392	-1,23 → 21,51médiane -0,35	33 hors du 1er–99e centile
heat_wave_flagbigint	0 %	1	0 → 0médiane 0
cold_snap_flagbigint	0 %	2	0 → 1médiane 0
heavy_rain_flagbigint	0 %	2	0 → 1médiane 0
snow_day_flagbigint	0 %	2	0 → 1médiane 0
pleasant_day_flagbigint	0 %	2	0 → 1médiane 0
weather_demand_scoredouble	0 %	964	8,64 → 100médiane 68,86	31 hors du 1er–99e centile
weather_demand_rankbigint	0 %	5	1 → 5médiane 3
as_ofvarchar	0 %	1	—	2026-10-011 825
source_stationvarchar	0 %	5	—	1108395365 3031092365 6106001365
row_hashvarchar	0 %	1 968	—	0034cb1b84b052ac1 0055568b7a7faa291 0089f7914f46812a1

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (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)

Point d’accès API : https://datazimuts.com/v1/datasets/ca_metro_weather_demand_intel/ca_metro_weather_demand_daily

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

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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/fr/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/fr/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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