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NOAA Climate Data Record (CDR) of NEXRAD Quantitative Precipitation Estimates (QPE) (Restricted)

Published by DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce | National Oceanic and Atmospheric Administration, Department of Commerce | Metadata Last Checked: January 27, 2026 | Last Modified: 2018-01-01T00:00:00.000+00:00
NOAA NEXRAD Quantitative Precipitation Estimation (QPE) Climate Data Record (CDR) is created from the Radar Multi-Radar/Multi-Sensor (MRMS) Reanalysis to produce severe weather and precipitation products for improved decision-making capability to improve severe weather forecasts and warnings, hydrology, aviation, and numerical weather prediction. The data cover a time period from 2002-01-01 to 2011-12-31. NOAA's NEXRAD reanalysis consists of two primary components; (1) Severe weather and radar-reflectivity data generation, (2) Quantitative Precipitation Estimate (including associated precipitation variables and merged rain gauge and radar estimation). This document focuses on the second component of NOAA's NEXRAD reanalysis - the Quantitative Precipitation Estimate (QPE). The primary files generated within this data set are radar-only and radar- gauge (ROQPE, GCQPE, and MOS2D) merged precipitation products as well as ancillary information on precipitation type (PRATE and PFLAG) and radar quality (RQIND). The initial data set covers the time period from January 2002 - December 2011. Radar-only reflectivity, Gauge, Precipitation Flag, and Radar Quality Index for 5-minute data at 1km regular grid over CONUS. Radar only Radar-Gauge Quantitative Precipitation Estimates at hourly scale at 1km regular grid over CONUS. MRMS Quantitative Precipitation Estimation (QPE) uses the most advanced radar technologies and provides high-resolution information about precipitation types and amounts for the nation. The data are stored in netCDF version 4.0 files that include the necessary metadata and supplementary data fields. Data set provides information that can be useful for identification of various types of precipitation, estimation of radar reflectivity, recognition of storm patterns, forecasting technologies for rainfall estimation, and associating different phases of precipitation such as hail freezing rain and snow with radar observations.

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