NOAA MRMS CONUS analysis, hourly

Spatial domain	Continental United States
Spatial resolution	0.01 degrees (~1km)
Time domain	2014-11-01 00:00:00 UTC to Present
Time resolution	1 hour

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The NOAA Multi-Radar/Multi-Sensor System (MRMS) integrates data from multiple radars and radar networks, surface observations, numerical weather prediction (NWP) models, and climatology to generate seamless, high spatio-temporal resolution mosaics at low latency focused on hail, wind, tornado, quantitative precipitation estimations, convection, icing, and turbulence.

This analysis dataset is an archive of MRMS radar and multi-sensor precipitation and weather analyses over the contiguous United States (CONUS).

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dynamical.org - NOAA MRMS CONUS analysis, hour...

Precipitation at a place and time

import xarray as xr  # xarray>=2025.1.2 and zarr>=3.0.8 for zarr v3 support

ds = xr.open_zarr("https://data.dynamical.org/noaa/mrms/conus-analysis-hourly/latest.zarr")
ds["precipitation_surface"].sel(time="2026-01-01T00", latitude=40, longitude=-90, method="nearest").compute()

Dimensions

	min	max	units
latitude	20.005	54.995	degree_north

longitude	-129.995	-60.005	degree_east

time	2014-11-01T00:00:00	Present	seconds since 1970-01-01

Variables

	units	dimensions
categorical_precipitation_type_surface Precipitation type Precipitation type flag. 0=no precipitation, 1=warm stratiform rain, 3=snow, 6=convective, 7=hail, 10=cold stratiform rain, 91=tropical.	1	time × latitude × longitude

precipitation_pass_1_surface Precipitation rate Average precipitation rate over the previous hour, pass 1 (lower latency, fewer gauges). Derived from MultiSensor_QPE_01H_Pass1. Available from October 2020 onward. Units equivalent to mm/s.	kg m-2 s-1	time × latitude × longitude

precipitation_pass_2_surface Precipitation rate Average precipitation rate over the previous hour, pass 2 (higher latency, more gauges). Derived from MultiSensor_QPE_01H_Pass2. Available from October 2020 onward. Units equivalent to mm/s.	kg m-2 s-1	time × latitude × longitude

precipitation_radar_only_surface Precipitation rate from radar Average radar-only precipitation rate over the previous hour (no gauge correction). Derived from RadarOnly_QPE_01H. Units equivalent to mm/s.	kg m-2 s-1	time × latitude × longitude

precipitation_surface Precipitation rate Average precipitation rate over the previous hour. Derived from MultiSensor_QPE_01H_Pass2 from October 2020, GaugeCorr_QPE_01H before. If primary product is unavailable, falls back to MultiSensor_QPE_01H_Pass1 and then RadarOnly_QPE_01H. Units equivalent to mm/s.	kg m-2 s-1	time × latitude × longitude

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

NOAA NWS NCEP MRMS data processed by dynamical.org from NOAA NCEP, NOAA Open Data Dissemination and Iowa Mesonet archives.

Or NOAA MRMS from dynamical.org.

Spatial coverage

Use this dataset over the land areas of the contiguous United States. Radar-only and precipitation type variables contain NaN values beyond the range of US radar. precipitation_pass_1_surface and precipitation_pass_2_surface extend further into the ocean, but still contain NaN values in the southeast corner of the domain over the Atlantic.

Temporal coverage

precipitation_surface combines multiple MRMS products to minimize missing values. Despite this, some hours (particularly early in the record) contain NaN values where data is unavailable.

precipitation_pass_2_surface and precipitation_pass_1_surface are available starting 2020-10-15. For timestamps prior to this date, these variables are filled with NaN.

Source

The source files this archive is constructed from are provided by NOAA Open Data Dissemination (NODD) and accessed from the AWS Open Data Registry. Operational data is additionally accessed from NCEP.

Storage

Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative. Icechunk storage generously provided by AWS Open Data.

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.