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The NCEI, part of NOAA, provides access to a wide range of environmental datasets, including climate data, weather observations, oceanographic data and geophysical data.
Engineers use the NCEI Web Services API to query stations, datasets, and data records. The `climata` Python library provides higher-level access to NCEI precipitation and temperature data. Large climate model outputs are accessed via Thredds/OPeNDAP using the `netCDF4` library.
NCEI's vast climate archive trains AI models for long-range weather prediction, climate attribution studies, and extreme weather risk scoring. RAG systems built on NCEI storm records help AI tools answer questions about historical extreme events with precise measured data.
# pip install requests pandas
import requests, pandas as pd
resp = requests.get(
"https://www.ncei.noaa.gov/cdo-web/api/v2/data",
headers={"token": "YOUR_TOKEN"},
params={"datasetid": "GSOY", "stationid": "GHCND:USW00094728",
"startdate": "2010-01-01", "enddate": "2023-12-31",
"datatypeid": "TAVG", "limit": 14}
)
df = pd.DataFrame(resp.json().get("results", []))[["date", "value"]]
print(df)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.
NOAA platform provides access to a vast collection of climate-related datasets, including historical weather data, climate observations, satellite imagery and climate model outputs.
GLDAS provides datasets on land surface conditions, including soil moisture, temperature, precipitation and other hydrological variables, derived from satellite and ground-based observations.