Earth Data School
About
Full catalog/UWPD CMIP5
UWPD CMIP5·dataset

Local-scale US climate projections, daily (NOAA/CMIP5 downscaled)

Coupled Model Intercomparison Project Phase 5 (CMIP5) University of Wisconsin-Madison Probabilistic Downscaling Dataset
atmosphere NOAA NOAA active
In plain English

What it measures. It provides daily rainfall and daily high and low temperatures at a fine local scale across the United States and southern Canada east of the Rockies. Rather than single fixed numbers, it gives a range of likely values so rare extremes and uncertainty are captured.

How it's made. NOAA hosts this dataset, which the University of Wisconsin produced by statistically downscaling coarse global CMIP5 climate model output into fine local detail, generating multiple plausible realizations for temperature and precipitation.

How & where you'd use it. Planners and researchers use it to study future climate at a town or watershed scale, including flood risk, heat extremes, and water supply, where knowing the full range of outcomes matters more than a single estimate.

What's measured

aws-pdsclimatecoastaldisaster responseenvironmentalmeteorologicalsustainabilityoceanswaterweather

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 University of Wisconsin Probabilistic Downscaling (UWPD) is a statistically downscaled dataset based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. UWPD consists of three variables, daily precipitation and maximum and minimum temperature. The spatial resolution is 0.1 ° x0.1 ° degree resolution for the United States and southern Canada east of the Rocky Mountains. The downscaling methodology is not deterministic. Instead, to properly capture unexplained variability and extreme events, the methodology predicts a spatially and temporally varying Probability Density Function (PDF) for each variable. Statistics such as the mean, mean PDF and annual maximum statistics can be calculated directly from the daily PDF and these statistics are included in the dataset. In addition, “standard”, “raw” data is created by randomly sampling from the PDFs to create a “realization” of the local scale given the large-scale from the climate model. There are 3 realizations for temperature and 14 realizations for precipitation. The directory structure of the data is as follows [cmip_version]/[scenario]/[climate_model]/[ensemble_member]/ The realizations are as follows prcp_[realization_number]_[year].nc temp_[realization_number]_[year].nc The time mean files averaged over certain year bounds are as follows prcp_mean_[year_bound_1]_[year_bound_2].nc temp_mean_[year_bound_1]_[year_bound_2].nc The time-mean Cumulative Distribution Function (CDF) files are as follows prcp_cdf_[year_bound_1]_[year_bound_2].nc temp_cdf_[year_bound_1]_[year_bound_2].nc The CDF

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).