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The initial operating capabilities of the Multi-Radar Multi-Sensor quantitative precipitation system include an ensemble of quantitative precipitation estimations and associated diagnostic products based on radar, gauge, and atmospheric environmental and climatological data at 1-km resolution and a 2-min update cycle over the conterminous United States.
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Over the last two decades, there has been a focus on developing new applications and systems to address requirements for seamless national radar information for use in model data assimilation, transportation, and quantitative precipitation estimation, which integrate multiple overlapping radars with other in situ or remote sensing observations and numerical weather prediction (NWP) model output. Advances in computational speed and expanding Internet bandwidth facilitated the ability to move radar base data from single radars into regional and national centers for processing (Droegemeier et al. 2002; Kelleher et al. 2007).
The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction (NCEP) demonstrated such capabilities. MRMS currently integrates about 180 operational radars and creates a seamless 3D radar mosaic across the conterminous United States (CONUS) and southern Canada at very high spatial (1 km) and temporal (2 min) resolution. The radar base data are integrated with atmospheric environmental data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. Multiradar integration can mitigate deficiencies in the single-radar framework (e.g., "cone of silence"). The integration of radar with multisensor data provides more accurate diagnoses of physical processes in the atmosphere than using radar data alone. For instance, the 3D temperature and moisture field is very helpful in hydrometeor classifications and segregation of continental and tropical rain.
MRMS was conceived and built at the National Severe Storms Laboratory (NSSL) using components from the Warning Decision Support System-Integrated Information (WDSS-II; Lakshmanan et al. 2007) and National Mosaic and Multi-Sensor QPE (NMQ) (Zhang et al. 2011) systems. The MRMS severe weather algorithms are derived using WDSS-II, and MRMS QPE algorithms are largely based on the NMQ QPE components. The following paper provides an overview of the initial operating capabilities (IOC) of MRMS QPE products. An overview of the MRMS severe weather products will be presented in a separate paper (Smith et al. 2016).
MRMS QPE...