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PROJECT TITLE :

Mapping the Precipitation Type Distribution Over the Contiguous United States Using NOAA/NSSL National Multi-Sensor Mosaic QPE

ABSTRACT:

Understanding the Earth's energy cycle and water balance requires an understanding of the distribution of precipitation sorts and their total equivalent water budget estimation. The fine distribution of precipitation types over the contiguous United States (CONUS) is not yet well understood due to either unavailability or coarse resolution of previous satellite- and ground radar-primarily based precipitation products that have difficulty in classifying precipitation. The newly offered NOAA/National Severe Storms Laboratory ground radar network-based National Multi-Sensor Mosaic QPE (NMQ/Q2) System has provided precipitation rates and sorts at unprecedented high spatiotemporal resolution. Here, four years of 1 km/five min observations derived from the NMQ are used to probe spatiotemporal distribution and characteristics of precipitation sorts (stratiform, convective, snow, tropical/heat (T/W), and hail) over CONUS, resulting in assessment of prevalence and volume contribution for these precipitation sorts through the four-year amount, including seasonal distributions, with some radar coverage artifacts. These maps normally highlight the snow distribution over northwestern and northern CONUS, convective distribution over southwestern and central CONUS, hail distribution over central CONUS, and T/W distribution over southeastern CONUS. The overall occurrences (contribution of total rain quantity/volume) of those types are seventy two.eighty eight% (fifty three.ninety one%) for stratiform, twenty one.15% (seven.sixty four%) for snow, 2.95% (nineteen.31%) for T/W, a pair of.seventy seven% (14.03%) for convective, and zero.24% (5.eleven%) for hail. This paper makes it attainable to prototype a near seamless high-resolution reference for evaluating satellite swath-based mostly precipitation type retrievals and also a potentially useful forcing database for energy–water balance budgeting and hydrological prediction for the United States.


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