US metro observed-weather demand panel (daily)

Name: US metro observed-weather demand panel (daily)
Creator: NOAA National Centers for Environmental Information
License: https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00861
Keywords: weather, us, daily, signals, metros, demand-forecasting, temperature, precipitation

Commercial use OKAging

Query in workbench

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

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.

Source: NOAA National Centers for Environmental Information
Rows: 5,475
Columns: 32
Source cadence: Daily
Last refreshed: Sep 29, 2026
Theme: environment

Column	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, 1.2prcp_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)

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	awnd_ms	hdd18_c	cdd18_c	tmax_anom_z	tmin_anom_z	tavg_anom_z	prcp_anom_z	heat_wave_flag	heavy_rain_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	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	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	1	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	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	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	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	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	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	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	1	95.93	3	2026-09-22	USW00014922	a630c9eb93c4b30a

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_demand_intel/us_metro_weather_demand_daily" | jq '{title, rows, columns_count, license}'

Python

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)

API endpoint: https://datazimuts.com/v1/datasets/metro_weather_demand_intel/us_metro_weather_demand_daily

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

Cite this snapshot

Pinned to snapshot 20260929T184025Z-92c84da78f3f and its content hash, so readers get exactly the data you used.

APA

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/en/datasets/metro_weather_demand_intel/us_metro_weather_demand_daily?snapshot=20260929T184025Z-92c84da78f3f

BibTeX

@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/en/datasets/metro_weather_demand_intel/us_metro_weather_demand_daily?snapshot=20260929T184025Z-92c84da78f3f}},
  note         = {Snapshot 20260929T184025Z-92c84da78f3f, sha256 92c84da78f3f6926277b73d5fb8249e37d0635e29155590df74559667d51035c; accessed 2026-09-30}
}

Embed a table or a chart

Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.

Type

X axis

Value

Aggregate

Theme

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=metro_weather_demand_intel%2Fus_metro_weather_demand_daily&lang=en&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>

US metro observed-weather demand panel (daily)

Follow this dataset

Cite this snapshot

Embed a table or a chart

Use your own AI key