US metro observed-weather demand panel (daily)
Daily observed-weather demand panel for 15 US metros from NOAA NCEI GHCN-Daily, fetched keyless two ways: the trailing 365 days of observed TMAX/TMIN, precipitation, snowfall, snow depth and wind at the metro airport station via the NCEI Data Access Service (daily-summaries), and the 10-year (2016-2025) day-of-year climatology baseline from the per-station bulk files. 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 / high-wind 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 15 metro keys (ICAO) as metro_degree_days / metro_daylight_intel for 1:1 joins; the 5 Canadian metros are excluded because GHCN-Daily's Canadian airport stations are stale in NCEI's feed. GHCN-Daily is U.S. federal public domain (commercial reuse allowed); source: NOAA National Centers for Environmental Information.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 5 475
- Colonnes
- 32
- Cadence de la source
- Quotidienne
- Dernière actualisation
- 29 sept. 2026
- Thème
- environment
| Colonne | Type | Description |
|---|---|---|
| date | string | Calendar date of the observation day (station local day), ISO date. Primary join key with icao. (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 | US state containing the metro (airport station). (unit: name) |
| country_code | string | ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3) |
| icao | string | Metro airport ICAO code (KJFK, KLAX, ...): the stable metro key shared with the sibling metro 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 (GHCN-Daily TMAX, tenths of degC -> degC). Null where the station did not report; never zero-filled. (unit: degC) |
| tmin_c | float | Observed daily minimum temperature (GHCN-Daily TMIN). 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 (GHCN-Daily PRCP, tenths of mm -> mm). Null where the station did not report. (unit: mm) |
| snow_mm | float | Observed daily snowfall (GHCN-Daily SNOW, tenths of mm -> mm). Null where not reported. (unit: mm) |
| snow_depth_mm | float | Observed snow depth (GHCN-Daily SNWD, mm). Null where not reported. (unit: mm) |
| awnd_ms | float | Observed daily average wind speed (GHCN-Daily AWND, tenths of m/s -> m/s). Null where not reported. (unit: m/s) |
| hdd18_c | float | Heating degree-days, base 18 degC: max(0, 18 - tmean_c). Null when tmean_c is null. Base matches the city_climate_stress_signals HDD18 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. 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 | float | 1 when prcp_mm >= 25.0, else 0 (null when prcp null). (unit: 0/1 flag) |
| snow_day_flag | float | 1 when snow_mm >= 25.0 (one inch), else 0 (null when snow null). (unit: 0/1 flag) |
| high_wind_flag | float | 1 when awnd_ms >= 12.0, else 0 (null when awnd null). (unit: 0/1 flag) |
| pleasant_day_flag | float | 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, 1.2*prcp_mm) for rain, minus 15/15/10/10/8 for heat-wave / cold-snap / heavy-rain / snow-day / high-wind days (floored at 0). High = pleasant foot-traffic/shopping weather. Null when tmean_c is null. (unit: 0-100 score) |
| weather_demand_rank | integer | Per-date dense rank of weather_demand_score across the 15 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 >= 13 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 | GHCN-Daily station identifier behind the row's observations. (unit: code) |
| row_hash | string | Deterministic 16-hex id: sha256('WXDEMAND|<icao>|<date>'). (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 | awnd_ms | hdd18_c | cdd18_c | tmax_anom_z | tmin_anom_z | tavg_anom_z | prcp_anom_z | heat_wave_flag | cold_snap_flag | heavy_rain_flag | snow_day_flag | high_wind_flag | pleasant_day_flag | weather_demand_score | weather_demand_rank | as_of | source_station | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2025-09-23 | Atlanta | Georgia | USA | KATL | 33,63 | -84,442 | America/New_York | 32,2 | 20 | 26,1 | 0 | 0 | 0 | 2,3 | 0 | 8,1 | 0,58 | 0,53 | 0,57 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88,78 | 10 | 2026-09-22 | USW00013874 | d3fd0c48de57cffc |
| 2025-09-23 | Boston | Massachusetts | USA | KBOS | 42,361 | -71,01 | America/New_York | 26,7 | 15,6 | 21,1 | 0 | 0 | — | 4 | 0 | 3,1 | 0,56 | 0,29 | 0,49 | -0,63 | 0 | 0 | 0 | 0 | 0 | 1 | 99,67 | 1 | 2026-09-22 | USW00014739 | bfe310c8c7e2fd4a |
| 2025-09-23 | Denver | Colorado | USA | KDEN | 39,847 | -104,656 | America/Denver | 11,7 | 9,4 | 10,6 | 32,5 | 0 | 0 | 5,7 | 7,4 | 0 | -1,98 | -0,12 | -1,65 | 2,46 | 0 | 0 | 1 | 0 | 0 | 0 | 37,01 | 15 | 2026-09-22 | USW00003017 | 18066542e636a48c |
| 2025-09-23 | Dallas | Texas | USA | KDFW | 32,897 | -97,022 | America/Chicago | 36,1 | 25,6 | 30,9 | 0 | 0 | 0 | 5,3 | 0 | 12,9 | 0,78 | 0,96 | 0,91 | -0,34 | 1 | 0 | 0 | 0 | 0 | 0 | 63,33 | 13 | 2026-09-22 | USW00003927 | ec14ed898804b17b |
| 2025-09-23 | Detroit | Michigan | USA | KDTW | 42,231 | -83,331 | America/Detroit | 20,6 | 14,4 | 17,5 | 0,5 | 0 | 0 | 2,1 | 0,5 | 0 | 0,1 | 1,06 | 0,53 | -0,34 | 0 | 0 | 0 | 0 | 0 | 1 | 91,7 | 8 | 2026-09-22 | USW00014847 | 65c5a7f397c9d675 |
| 2025-09-23 | Houston | Texas | USA | KIAH | 29,984 | -95,361 | America/Chicago | 33,9 | 23,9 | 28,9 | 5,3 | 0 | 0 | 3,4 | 0 | 10,9 | 0,54 | 0,66 | 0,67 | 0,19 | 0 | 0 | 0 | 0 | 0 | 0 | 76,26 | 11 | 2026-09-22 | USW00012960 | bd7c86c58aa1ebb9 |
| 2025-09-23 | New York | New York | USA | KJFK | 40,639 | -73,764 | America/New_York | 25,6 | 18,3 | 22 | 0 | 0 | 0 | 4,8 | 0 | 4 | 0,27 | 0,7 | 0,49 | -0,51 | 0 | 0 | 0 | 0 | 0 | 1 | 97,91 | 2 | 2026-09-22 | USW00094789 | ced60789573d70fd |
| 2025-09-23 | Los Angeles | California | USA | KLAX | 33,938 | -118,387 | America/Los_Angeles | 27,2 | 18,9 | 23 | 0 | — | — | 3,4 | 0 | 5 | 1,51 | 1,2 | 1,52 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 95,49 | 4 | 2026-09-22 | USW00023174 | 1665ef785ea8a9bd |
| 2025-09-23 | Miami | Florida | USA | KMIA | 25,788 | -80,317 | America/New_York | 31,1 | 23,3 | 27,2 | 15 | 0 | 0 | 1,5 | 0 | 9,2 | -0,62 | -1,67 | -1,25 | 0,37 | 0 | 0 | 0 | 0 | 0 | 0 | 68,36 | 12 | 2026-09-22 | USW00012839 | 15df47b2b97e2d6b |
| 2025-09-23 | Minneapolis | Minnesota | USA | KMSP | 44,885 | -93,231 | America/Chicago | 23,9 | 14,4 | 19,1 | 0 | 0 | 0 | 2,7 | 0 | 1,1 | 0,02 | 0,14 | 0,08 | -0,49 | 0 | 0 | 0 | 0 | 0 | 1 | 95,93 | 3 | 2026-09-22 | USW00014922 | a630c9eb93c4b30a |
- Actuelle
20260929T184025Z-92c84da78f3f · sha256 92c84da78f3f…
5 475 lignes · premier instantané
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 "https://datazimuts.com/v1/datasets/metro_weather_demand_intel/us_metro_weather_demand_daily" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/metro_weather_demand_intel/us_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/metro_weather_demand_intel/us_metro_weather_demand_daily
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20260929T184025Z-92c84da78f3f et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
NOAA National Centers for Environmental Information. (2026). US metro observed-weather demand panel (daily) [Data set, snapshot 20260929T184025Z-92c84da78f3f, sha256 92c84da78f3f]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/fr/datasets/metro_weather_demand_intel/us_metro_weather_demand_daily?snapshot=20260929T184025Z-92c84da78f3f
@misc{dz_metro_weather_demand_intel_us_metro_weat_92c84da7,
title = {{US metro observed-weather demand panel (daily)}},
author = {{NOAA National Centers for Environmental Information}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/metro_weather_demand_intel/us_metro_weather_demand_daily?snapshot=20260929T184025Z-92c84da78f3f}},
note = {Snapshot 20260929T184025Z-92c84da78f3f, sha256 92c84da78f3f6926277b73d5fb8249e37d0635e29155590df74559667d51035c; accessed 2026-09-30}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=metro_weather_demand_intel%2Fus_metro_weather_demand_daily&lang=fr&theme=auto&snapshot=20260929T184025Z-92c84da78f3f&x=date&y=lat&agg=avg" title="US metro observed-weather demand panel (daily)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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