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Full catalog/NMME
NMME·dataset

Experimental multi-model seasonal climate forecasts (NOAA NMME)

NOAA North American Multi-Model Ensemble (NMME)
atmosphere NOAA NOAA active
In plain English

What it measures. Seasonal forecasts of conditions like temperature and precipitation, produced not by one model but by a collection of climate models run together, which also reveals how much uncertainty there is in the outlook.

How it's made. Combines coupled climate models from several North American centers, including NOAA, NASA, NCAR, and Canada's ECCC, into one ensemble forecast.

How & where you'd use it. Supports seasonal planning where blending many models gives more reliable outlooks than any single one, aiding water, agriculture, and climate decisions.

What's measured

aws-pdsclimateweathermeteorological

Coverage & cadence

  • Time span— → ongoing

What you can do with it

  • Map air pollutants — NO₂, aerosols, ozone
  • Track greenhouse gases and Earth's energy budget
  • Feed weather and air-quality analysis
Official description

The North American Multi-Model Ensemble (NMME) is an experimental multi-model seasonal forecasting system consisting of coupled models from US modeling centers including NOAA/NCEP, NOAA/GFDL, NCAR, NASA, and Canada's ECCC. The need for the development of NMME operational predictive capability was recommended in US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability". Indeed, the national effort is required to meet the specific tailored regional prediction and decision support needs of a large community. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) than any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, including an operational European system. There are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model.

Get the data

noaa_access.py
# NOAA Open Data on AWS — public S3, no login
import s3fs

fs = s3fs.S3FileSystem(anon=True)
# find this dataset's bucket in the docs link in the sidebar, then:
# files = fs.ls("noaa-<bucket>/...")
# open NetCDF/GRIB with xarray, COGs with rioxarray
NOAA Open Data is on public AWS S3 — no login at all (anonymous access).