Global city climate stress signals (degree days, precipitation, renewable-resource anomalies)

Name: Global city climate stress signals (degree days, precipitation, renewable-resource anomalies)
Creator: Global City Climate Stress Signals (derived)
License: https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-and-information-policy
Keywords: climate, nasa, degree-days, energy-demand, drought, solar, wind, cities, signals

Value-added monthly climate-stress signals for 54 world cities from free NASA POWER data: cooling/heating degree days (energy demand proxies), precipitation anomalies (drought/wetness), solar and wind resource anomalies, and heat/cold stress flags — 1981 to present. All computation is local pandas/numpy; no paid models or APIs.

Source: Global City Climate Stress Signals (derived)147,690 lignesMis à jour: 22/09/2026Licence: CC0-1.0

climatenasadegree-daysenergy-demanddroughtsolarwindcitiessignals

Qualité

99.9

Attribution

The data was obtained from the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) Project; derived signals by Frontier Data Hub

Schéma

Colonne	Type	Description
date	string	Reference month of the observation (first day of the month), as published in the NASA POWER monthly climatology product.
country	string
country_code	string
city	string	World city the observation belongs to (one of the 54 cities in the NASA POWER monthly climate dataset).
series_id	string	Derived series identifier: CDD18_MONTH (cooling degree days), HDD18_MONTH (heating degree days), PRECIP_Z (precipitation anomaly), SOLAR_RES_Z (solar irradiance anomaly), or WIND_RES_Z (wind-speed anomaly).
series_label	string	Human-readable series label naming the climate-stress metric and the city/country, computed from NASA POWER parameters.
value	float	Derived monthly value: degree days (degree-days) from the NASA POWER 2-meter air temperature (T2M); precipitation z-scores from bias-corrected total precipitation (PRECTOTCORR); solar and wind anomalies from all-sky surface shortwave downward irradiance (ALLSKY_SFC_SW_DWN) and 50m wind speed (WS50M).
heat_stress_flag	integer
cold_stress_flag	integer
rank	float

Exemple de lignes

date	country	country_code	city	series_id	series_label	value	cold_stress_flag	rank
1981-01-01	Ethiopia	ETH	Addis Ababa	CDD18_MONTH	Cooling degree days, base 18 degC (monthly, NASA POWER) — 0 Addis Ababa 1 Addis Ababa 2 Addis Ababa 3 Addis Ababa 4 Addis Ababa ... 29533 Warsaw 29534 Warsaw 29535 Warsaw 29536 Warsaw 29537 Warsaw Name: city, Length: 29538, dtype: str, 0 Ethiopia 1 Ethiopia 2 Ethiopia 3 Ethiopia 4 Ethiopia ... 29533 Poland 29534 Poland 29535 Poland 29536 Poland 29537 Poland Name: country, Length: 29538, dtype: str	0	0	20
1981-01-01	Algeria	DZA	Algiers	CDD18_MONTH	Cooling degree days, base 18 degC (monthly, NASA POWER) — 0 Addis Ababa 1 Addis Ababa 2 Addis Ababa 3 Addis Ababa 4 Addis Ababa ... 29533 Warsaw 29534 Warsaw 29535 Warsaw 29536 Warsaw 29537 Warsaw Name: city, Length: 29538, dtype: str, 0 Ethiopia 1 Ethiopia 2 Ethiopia 3 Ethiopia 4 Ethiopia ... 29533 Poland 29534 Poland 29535 Poland 29536 Poland 29537 Poland Name: country, Length: 29538, dtype: str	0	0	20
1981-01-01	Netherlands	NLD	Amsterdam	CDD18_MONTH	Cooling degree days, base 18 degC (monthly, NASA POWER) — 0 Addis Ababa 1 Addis Ababa 2 Addis Ababa 3 Addis Ababa 4 Addis Ababa ... 29533 Warsaw 29534 Warsaw 29535 Warsaw 29536 Warsaw 29537 Warsaw Name: city, Length: 29538, dtype: str, 0 Ethiopia 1 Ethiopia 2 Ethiopia 3 Ethiopia 4 Ethiopia ... 29533 Poland 29534 Poland 29535 Poland 29536 Poland 29537 Poland Name: country, Length: 29538, dtype: str	0	1	20
1981-01-01	Greece	GRC	Athens	CDD18_MONTH	Cooling degree days, base 18 degC (monthly, NASA POWER) — 0 Addis Ababa 1 Addis Ababa 2 Addis Ababa 3 Addis Ababa 4 Addis Ababa ... 29533 Warsaw 29534 Warsaw 29535 Warsaw 29536 Warsaw 29537 Warsaw Name: city, Length: 29538, dtype: str, 0 Ethiopia 1 Ethiopia 2 Ethiopia 3 Ethiopia 4 Ethiopia ... 29533 Poland 29534 Poland 29535 Poland 29536 Poland 29537 Poland Name: country, Length: 29538, dtype: str	0	0	20
1981-01-01	New Zealand	NZL	Auckland	CDD18_MONTH	Cooling degree days, base 18 degC (monthly, NASA POWER) — 0 Addis Ababa 1 Addis Ababa 2 Addis Ababa 3 Addis Ababa 4 Addis Ababa ... 29533 Warsaw 29534 Warsaw 29535 Warsaw 29536 Warsaw 29537 Warsaw Name: city, Length: 29538, dtype: str, 0 Ethiopia 1 Ethiopia 2 Ethiopia 3 Ethiopia 4 Ethiopia ... 29533 Poland 29534 Poland 29535 Poland 29536 Poland 29537 Poland Name: country, Length: 29538, dtype: str	85.25	0	13

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Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

Télécharger l'exemple CSV Télécharger l'exemple JSON

Utiliser avec un LLM

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/city_climate_stress_signals/global_city_climate_stress_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/city_climate_stress_signals/global_city_climate_stress_signals").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/city_climate_stress_signals/global_city_climate_stress_signals

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