NWS surface-station extreme-weather intelligence: daily

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Daily extreme-weather digest over 164 principal U.S. surface stations (major airports + AK/HI/PR/GU/AS/MP): hourly NWS observations for the last complete UTC day collapsed to daily Tmax/Tmin/Tmean (degC), total precipitation (mm), max wind gust (m/s), and observation completeness. Each station gets a documented 0-100 extreme_weather_score (25% heat + 25% cold + 25% precipitation + 25% gust, ramped 0-1 intensities) ranked as extreme_rank, top-10 category leaderboards (hottest/coldest/wettest/windiest), and extreme flags (heat >=100F, freeze <=32F, heavy precip >=1in, strong gust >=40kt). Stations with <12 valid temperature observations are dropped; precipitation totals are honest null under <6 hourly readings. Primary key: (obs_date, station_id). Cadence: daily; obs_date is the window's complete UTC day, so identical input produces an identical content hash. Caveats: principal-station roster, not a full NWS census; stations in outage are absent that day; precip sums are reported hourly increments. NWS data is U.S. public domain — commercial_use = yes. Sample use: order by extreme_rank for the day's most extreme stations, or filter hottest_rank <= 10 for the day's heat leaders.

Source: NWS Surface-Station Extreme Weather Intelligence
Rows: 162
Columns: 27
Source cadence: Daily
Last refreshed: Sep 26, 2026
Theme: environment

Column	Type	Description
obs_date	string	The last complete UTC day the digest covers. Fixed per run so identical input produces an identical content hash. (unit: date)
station_id	string	NWS station identifier (ICAO id, e.g. KJFK). Together with obs_date it is the primary key. (unit: id)
station_name	string	Station name as reported by GET /stations/{id} (e.g. 'New York, Kennedy International Airport').
time_zone	string	IANA time zone reported for the station (e.g. America/New_York).
latitude	float	Station latitude (degrees). (unit: deg)
longitude	float	Station longitude (degrees). (unit: deg)
country_code	string	ISO alpha-3 country code (USA for every roster station, incl. territories).
n_obs	integer	Number of observation records parsed for the station on obs_date. (unit: count)
obs_completeness	float	Share of the 24 UTC hours of obs_date with at least one valid observation (0-1). (unit: share)
tmax_c	float	Day's maximum 2m air temperature. (unit: degC)
tmin_c	float	Day's minimum 2m air temperature. (unit: degC)
tmean_c	float	Mean of the day's valid temperature readings. (unit: degC)
precip_mm	float	Total precipitation: sum of the day's valid hourly precipitationLastHour readings (mm). Honest null when fewer than 6 hourly readings are valid. (unit: mm)
precip_valid_hours	integer	Number of valid hourly precipitation readings behind precip_mm. (unit: count)
max_gust_ms	float	Day's maximum reported wind gust, normalized from the API's km/h to m/s. (unit: m/s)
heat_flag	boolean	True when tmax_c >= 37.8 (100F). (unit: bool)
freeze_flag	boolean	True when tmin_c <= 0.0 (32F). (unit: bool)
heavy_precip_flag	boolean	True when precip_mm >= 25.4 (1 inch). (unit: bool)
strong_gust_flag	boolean	True when max_gust_ms >= 20.6 (40 knots). (unit: bool)
extreme_weather_score	float	Documented 0-100 composite = min(100, 25heat_i + 25cold_i + 25precip_i + 25gust_i) with ramped 0-1 intensities: heat_i = clip((tmax-30)/15), cold_i = clip((-15-tmin)/20), precip_i = clip((precip_mm-5)/45), gust_i = clip((gust-10)/25). (unit: score)
extreme_rank	integer	Station rank within the day's snapshot (1 = highest extreme_weather_score; ties broken by tmax_c desc, then station_id asc). (unit: rank)
hottest_rank	integer	Rank by tmax_c desc within the day's top 10; null when not in the top 10. (unit: rank)
coldest_rank	integer	Rank by tmin_c asc within the day's top 10; null when not in the top 10. (unit: rank)
wettest_rank	integer	Rank by precip_mm desc within the day's top 10; null when not in the top 10 (or precip_mm null). (unit: rank)
windiest_rank	integer	Rank by max_gust_ms desc within the day's top 10; null when not in the top 10. (unit: rank)
source_url	string	Canonical api.weather.gov URL for the station. (unit: URL)
row_hash	string	SHA-256 (16 hex chars) over the row's content fields; identical input yields an identical hash, so a re-run on an unchanged window is a no-op. (unit: hash)

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

obs_date	station_id	station_name	time_zone	latitude	longitude	country_code	n_obs	obs_completeness	tmax_c	tmin_c	tmean_c	precip_mm	precip_valid_hours	max_gust_ms	heat_flag	freeze_flag	heavy_precip_flag	strong_gust_flag	extreme_weather_score	extreme_rank	hottest_rank	coldest_rank	wettest_rank	windiest_rank	source_url	row_hash
2026-09-24	PHTO	Hilo, Hilo International Airport	Pacific/Honolulu	19.722	-155.056	USA	30	0.833	27.2	23.3	25	57.2	17	0	false	false	true	false	25	1	—	—	1	—	https://api.weather.gov/stations/PHTO	66c215b4b89d6b42
2026-09-24	KBFL	Bakersfield, Meadows Field Airport	America/Los_Angeles	35.434	-119.057	USA	200	0.667	38	19	27.9	—	0	8.8	true	false	false	false	13.33	2	1	—	—	—	https://api.weather.gov/stations/KBFL	9f3a4c1ed1a3c8a7
2026-09-24	KFAT	Fresno, Fresno Air Terminal	America/Los_Angeles	36.78	-119.719	USA	200	0.667	38	19	27	—	0	0	true	false	false	false	13.33	3	2	—	—	—	https://api.weather.gov/stations/KFAT	063f3fab3bf5d49d
2026-09-24	KHRL	Harlingen, Rio Grande Valley International Airport	America/Chicago	26.23	-97.655	USA	200	0.667	37	23.9	30.8	—	0	11.3	false	false	false	false	12.99	4	5	—	—	—	https://api.weather.gov/stations/KHRL	7b49f2508028e7c4
2026-09-24	KBRO	Brownsville, Brownsville / South Padre Island International Airport	America/Chicago	25.914	-97.423	USA	200	0.667	37	27	31.7	—	0	10.8	false	false	false	false	12.47	5	4	—	—	—	https://api.weather.gov/stations/KBRO	aa3391326b6b1b97
2026-09-24	KAUS	Austin-Bergstrom International Airport	America/Chicago	30.183	-97.68	USA	200	0.667	37	22	29.6	—	0	8.2	false	false	false	false	11.67	6	3	—	—	—	https://api.weather.gov/stations/KAUS	b14b2c76005c9f35
2026-09-24	TJSJ	San Juan, Luis Munoz Marin International Airport	America/Puerto_Rico	18.45	-66	USA	21	0.708	31.7	26.7	28.5	10.4	6	15.4	false	false	false	false	11.23	7	—	—	5	6	https://api.weather.gov/stations/TJSJ	d8c666990819ba61
2026-09-24	KLRD	Laredo, Laredo International Airport	America/Chicago	27.544	-99.461	USA	200	0.667	36	25	29.5	—	1	10.8	false	false	false	false	10.8	8	7	—	—	—	https://api.weather.gov/stations/KLRD	f9dc879b985a0481
2026-09-24	KDRT	Del Rio, Del Rio International Airport	America/Chicago	29.375	-100.924	USA	200	0.667	35	25	29.4	—	0	12.4	false	false	false	false	10.73	9	9	—	—	—	https://api.weather.gov/stations/KDRT	b65748b2b36d4bb8
2026-09-24	KORF	Norfolk, Norfolk International Airport	America/New_York	36.904	-76.192	USA	200	0.667	20	18	19	0	11	20.1	false	false	false	false	10.1	10	—	—	—	1	https://api.weather.gov/stations/KORF	bb9ffc258687c0c2

Profiled Sep 26, 2026 from snapshot 20260926T000847Z-8af57c086633

Measured

Completeness: 82.8%
Rows: 162
Columns: 27
Columns with gaps: 5

Column	Missing	Distinct	Range	Distribution
obs_datevarchar	0%	1	—	2026-09-24162
station_idvarchar	0%	172	—	KABE1 KABQ1 KAGS1
station_namevarchar	0%	215	—	Albany International Airport1 Albuquerque, Albuquerque International Airport1 Allentown, Lehigh Valley International Airport1
time_zonevarchar	0%	20	—	America/Chicago49 America/New_York40 America/Los_Angeles24
latitudedouble	0%	179	13.48 → 71.29median 38.99	4 outside 1st–99th percentile
longitudedouble	0%	157	-165.45 → 145.73median -95.77	4 outside 1st–99th percentile
country_codevarchar	0%	1	—	USA162
n_obsbigint	0%	12	15 → 200median 200	2 outside 1st–99th percentile
obs_completenessdouble	0%	7	0.625 → 1median 0.6667
tmax_cdouble	0%	43	-1 → 38median 23	4 outside 1st–99th percentile
tmin_cdouble	0%	45	-4 → 27median 13.9	3 outside 1st–99th percentile
tmean_cdouble	0%	107	-2.5 → 31.7median 17.5	4 outside 1st–99th percentile
precip_mmdouble	90.1%	12	0 → 57.2median 4.85	1 outside 1st–99th percentile
precip_valid_hoursbigint	0%	16	0 → 21median 0	2 outside 1st–99th percentile
max_gust_msdouble	0%	24	0 → 20.1median 8.8	2 outside 1st–99th percentile
heat_flagboolean	0%	2	—	false160 true2
freeze_flagboolean	0%	2	—	false158 true4
heavy_precip_flagboolean	0%	2	—	false161 true1
strong_gust_flagboolean	0%	1	—	false162
extreme_weather_scoredouble	0%	50	0 → 25median 0	1 outside 1st–99th percentile
extreme_rankbigint	0%	169	1 → 162median 81.5	4 outside 1st–99th percentile
hottest_rankbigint	93.8%	11	1 → 10median 5.5	2 outside 1st–99th percentile
coldest_rankbigint	93.8%	11	1 → 10median 5.5	2 outside 1st–99th percentile
wettest_rankbigint	93.8%	11	1 → 10median 5.5	2 outside 1st–99th percentile
windiest_rankbigint	93.8%	11	1 → 10median 5.5	2 outside 1st–99th percentile
source_urlvarchar	0%	146	—	https://api.weather.gov/stations/KABE1 https://api.weather.gov/stations/KABQ1 https://api.weather.gov/stations/KAGS1
row_hashvarchar	0%	180	—	00964afc29e970581 00b1ee49858392911 04bcc69a6e131e471

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/nws_station_extremes/nws_station_extremes_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/nws_station_extremes/nws_station_extremes_daily

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

Cite this snapshot

Pinned to snapshot 20260926T000847Z-8af57c086633 and its content hash, so readers get exactly the data you used.

APA

NWS Surface-Station Extreme Weather Intelligence. (2026). NWS surface-station extreme-weather intelligence: daily [Data set, snapshot 20260926T000847Z-8af57c086633, sha256 8af57c086633]. Datazimuts. Retrieved 2026-09-26, from https://datazimuts.com/en/datasets/nws_station_extremes/nws_station_extremes_daily?snapshot=20260926T000847Z-8af57c086633

BibTeX

@misc{dz_nws_station_extremes_nws_station_extreme_8af57c08,
  title        = {{NWS surface-station extreme-weather intelligence: daily}},
  author       = {{NWS Surface-Station Extreme Weather Intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/nws_station_extremes/nws_station_extremes_daily?snapshot=20260926T000847Z-8af57c086633}},
  note         = {Snapshot 20260926T000847Z-8af57c086633, sha256 8af57c08663362673d1ebc879843a9526e0f6a90e71457b43a34062bec569baa; accessed 2026-09-26}
}

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NWS surface-station extreme-weather intelligence: daily

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