NASA IMERG analysis, late

Name: NASA IMERG analysis, late
Creator: dynamical.org
License: https://creativecommons.org/licenses/by/4.0/

time-optimized

Spatial domain	Global
Spatial resolution	0.1 degrees (~10km)
Time domain	1998-01-01 00:00:00 UTC to Present
Time resolution	30 minutes

STAC (browse) · validation report

The Integrated Multi-satellitE Retrievals for GPM (IMERG) is a NASA algorithm that merges precipitation estimates from the constellation of passive microwave satellites in the Global Precipitation Measurement (GPM) mission with microwave-calibrated infrared estimates, monthly gauge analyses, and other sources to produce a global, gridded, half-hourly precipitation record. IMERG intercalibrates, merges, and interpolates these inputs onto a 0.1 degree grid spanning the TRMM and GPM satellite eras.

This analysis dataset is an archive of global half-hourly precipitation estimates from NASA GPM IMERG, version 07, on a 0.1 degree grid with dimensions time, latitude, and longitude. This is the Late Run, which incorporates additional satellite sensor input for higher quality, published about 12 to 18 hours after observation time.

Related Datasets

Examples

	Quickstart (Github)
	Quickstart (Colab)

import dynamical_catalog  # dynamical-catalog>=0.7.0

ds = dynamical_catalog.open("nasa-imerg-analysis-late", chunks=None)
ds["precipitation_surface"].sel(time="2026-01-01T00:00", latitude=0, longitude=0, method="nearest")

import icechunk
import pystac
import xarray as xr

catalog = pystac.Catalog.from_file("https://stac.dynamical.org/catalog.json")
collection = catalog.get_child("nasa-imerg-analysis-late")
asset = collection.assets["icechunk-https"]

repo = icechunk.Repository.open(icechunk.http_storage(asset.href))
session = repo.readonly_session("main")

ds = xr.open_zarr(session.store, chunks=None)
ds["precipitation_surface"].sel(time="2026-01-01T00:00", latitude=0, longitude=0, method="nearest")

NASA IMERG analysis, late · Precipitation at a place and time

Dimensions

	min	max	units
latitude	-89.95	89.95	degree_north

longitude	-179.95	179.95	degree_east

time	1998-01-01T00:00:00Z	Present	seconds since 1970-01-01

Variables

Access a variable by its name (the bold identifier, e.g. ds["precipitation_quality_index_surface"]).

Dimensions: time × latitude × longitude

variable	units
precipitation_quality_index_surface Quality index for precipitation (`pqi`)Dimensionless quality index for the precipitation estimate; higher is better.	1
precipitation_surface Precipitation rate (`prate`)Mean precipitation rate over the half hour. Complete merged microwave-infrared, gauge-adjusted where available (formerly precipitationCal). Units equivalent to mm/s; source is mm/hr.	kg m-2 s-1

Don't see what you're looking for? Let us know at [email protected].

Details

License

Dataset licensed under CC BY 4.0.

Attribution and citation

NASA GPM IMERG data processed by dynamical.org from NASA GES DISC and PPS archives.

Or NASA IMERG from dynamical.org.

Early vs. Late Run

NASA produces several IMERG runs from the same input observations at different latencies. This dataset is the Late Run, published approximately 12 to 18 hours after observation time. It incorporates additional satellite sensor input and applies both forward and backward propagation (morphing) of precipitation features, yielding higher quality than the low-latency Early Run.

The companion NASA IMERG analysis, early dataset is published about 4 hours after observation time.

Temporal coverage

IMERG version 07 reprocesses the full record back to 1998, spanning both the Tropical Rainfall Measuring Mission (TRMM, 1998 to 2014) and Global Precipitation Measurement (GPM, 2014 to present) satellite eras. Estimates before the GPM era (mid-2014) rely on a smaller satellite constellation and are generally of lower quality, particularly at high latitudes.

Source

The source files this archive is constructed from are provided by NASA and accessed from the GES DISC archive. Operational low-latency data is additionally accessed from the NASA Precipitation Processing System (PPS).

Storage

Storage for this dataset is generously provided by AWS Open Data.

Chunks & shards

This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. Chunks are grouped into larger shards (the objects actually written to storage), which keeps the object count manageable for long-archive datasets. When possible, aligning your reads with this dataset's chunk grid can significantly improve data access speed.

The element count and coordinate span of this dataset:

dimension	chunk	shard
time	1440 (30 days)	1440 (30 days)
latitude	100 (10°)	900 (90°)
longitude	100 (10°)	900 (~90°)
uncompressed	54.9 MiB	4.3 GiB

Compression

The data values in this dataset have been rounded in their binary floating point representation to improve compression. See Klöwer et al. 2021 for more information on this approach. The exact number of rounded bits can be found in our reformatting code.