NWS surface-station extreme-weather intelligence: daily

Usage commercial OKÀ jour

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 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.

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

Source: NWS Surface-Station Extreme Weather Intelligence
Lignes: 162
Colonnes: 27
Cadence de la source: Quotidienne
Dernière actualisation: 26 sept. 2026
Thème: environment

Colonne	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)

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

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

Profilé le 26 sept. 2026 à partir de l’instantané 20260926T000847Z-8af57c086633

Mesuré

Complétude: 82,8 %
Lignes: 162
Colonnes: 27
Colonnes incomplètes: 5

Colonne	Manquant	Distinctes	Plage	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 → 71,29médiane 38,99	4 hors du 1er–99e centile
longitudedouble	0 %	157	-165,45 → 145,73médiane -95,77	4 hors du 1er–99e centile
country_codevarchar	0 %	1	—	USA162
n_obsbigint	0 %	12	15 → 200médiane 200	2 hors du 1er–99e centile
obs_completenessdouble	0 %	7	0,625 → 1médiane 0,6667
tmax_cdouble	0 %	43	-1 → 38médiane 23	4 hors du 1er–99e centile
tmin_cdouble	0 %	45	-4 → 27médiane 13,9	3 hors du 1er–99e centile
tmean_cdouble	0 %	107	-2,5 → 31,7médiane 17,5	4 hors du 1er–99e centile
precip_mmdouble	90,1 %	12	0 → 57,2médiane 4,85	1 hors du 1er–99e centile
precip_valid_hoursbigint	0 %	16	0 → 21médiane 0	2 hors du 1er–99e centile
max_gust_msdouble	0 %	24	0 → 20,1médiane 8,8	2 hors du 1er–99e centile
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 → 25médiane 0	1 hors du 1er–99e centile
extreme_rankbigint	0 %	169	1 → 162médiane 81,5	4 hors du 1er–99e centile
hottest_rankbigint	93,8 %	11	1 → 10médiane 5,5	2 hors du 1er–99e centile
coldest_rankbigint	93,8 %	11	1 → 10médiane 5,5	2 hors du 1er–99e centile
wettest_rankbigint	93,8 %	11	1 → 10médiane 5,5	2 hors du 1er–99e centile
windiest_rankbigint	93,8 %	11	1 → 10médiane 5,5	2 hors du 1er–99e centile
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

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/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)

Point d’accès API : https://datazimuts.com/v1/datasets/nws_station_extremes/nws_station_extremes_daily

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

Citer cet instantané

Épinglé à l’instantané 20260926T000847Z-8af57c086633 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.

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/fr/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/fr/datasets/nws_station_extremes/nws_station_extremes_daily?snapshot=20260926T000847Z-8af57c086633}},
  note         = {Snapshot 20260926T000847Z-8af57c086633, sha256 8af57c08663362673d1ebc879843a9526e0f6a90e71457b43a34062bec569baa; accessed 2026-09-26}
}

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.

Type

Axe X

Valeur

Agrégat

Thème

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=nws_station_extremes%2Fnws_station_extremes_daily&lang=fr&theme=auto&snapshot=20260926T000847Z-8af57c086633&x=obs_date&y=latitude&agg=avg" title="NWS surface-station extreme-weather intelligence: daily" width="100%" height="380" style="border:0" loading="lazy"></iframe>

NWS surface-station extreme-weather intelligence: daily

Suivre ce jeu de données

Citer cet instantané

Intégrer un tableau ou un graphique

Utiliser votre propre clé d’IA