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NOAA platform provides access to a vast collection of climate-related datasets, including historical weather data, climate observations, satellite imagery and climate model outputs.
The NOAA Climate Data Online API requires a free token. Engineers use `requests` to query the GHCN daily station data, specifying station IDs, date ranges, and data types (TMAX, TMIN, PRCP). `pandas` DataFrames with DatetimeIndex enable time-series resampling and anomaly detection.
NOAA climate data trains AI models for weather pattern recognition, climate anomaly detection, and extreme event prediction. RAG systems built on NOAA records can answer 'What was the hottest summer on record for Phoenix, AZ?' with precise station-level historical data.
# pip install requests pandas
import requests, pandas as pd
resp = requests.get(
"https://www.ncdc.noaa.gov/cdo-web/api/v2/data",
headers={"token": "YOUR_NOAA_TOKEN"},
params={"datasetid": "GHCND", "stationid": "GHCND:USW00094728",
"startdate": "2023-01-01", "enddate": "2023-01-31",
"datatypeid": "TMAX", "limit": 31}
)
df = pd.DataFrame(resp.json()["results"])
print(df[["date", "value"]].head())Official dataset source
More datasets used by Python data engineers.
EOSDIS provides access to a wide range of Earth observation datasets, including satellite imagery, climate data, land cover, oceanography and atmospheric data.
The NCEI, part of NOAA, provides access to a wide range of environmental datasets, including climate data, weather observations, oceanographic data and geophysical data.
GLDAS provides datasets on land surface conditions, including soil moisture, temperature, precipitation and other hydrological variables, derived from satellite and ground-based observations.