NOAA Global Hydro Estimator (GHE)
Global rainfall estimates in 15-minute intervals.
The NOAA Global Hydro Estimator (GHE) program produces estimates of global (between -60° and +60° latitude) precipitation every 15 minutes, at ~4km resolution. Estimates are derived from satellite imagery and data from NOAA’s Global Forecast System; details about the GHE algorithm are available here.
This dataset is available on Azure thanks to the NOAA Big Data Program.
Data are stored in blobs in gzip’d NetCDF format in the East US data center, in the following blob container:
Within that container, data are named as:
- product is a product name; this should always be “rainfall”
- year is a four-digit year
- month is a two-digit month-of-year code, starting with 01
- day is a two-digit day-of-month code, starting with 01
- filename encodes the product, date, and time, with the last four digits encoding 24-hour time on 15-minute boundaries
For example, this filename:
…contains the 15-minute rainfall estimate for April 2, 2020, at 00:30 UTC.
Latitude and longitude are not perfectly uniformly sampled, so an additional file is available to specify the precise lat/lon grid associated with all GHE files (~160MB):
A complete Python example of accessing and plotting a GHE image (i.e., an instantaneous global estimate) is available in the notebook provided under “data access”.
We also provide a read-only SAS (shared access signature) token to allow access to GHE data via, e.g., BlobFuse, which allows you to mount blob containers as drives:
Mounting instructions for Linux are here.
Large-scale processing using this dataset is best performed in the East US Azure data center, where the data is stored. If you are using GHE data for environmental science applications, consider applying for an AI for Earth grant to support your compute requirements.
Global daily precipitation on April 9, 2020.
For questions about this dataset, contact
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This dataset is provided under the original terms that Microsoft received source data. The dataset may include data sourced from Microsoft.
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Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.