Mapping the precipitation type distribution over the contiguous United States using NOAA/NSSL national multi-sensor mosaic QPE

Sheng Chen, Jian Zhang, Esther Mullens, Yang Hong, Ali Behrangi, Yudong Tian, Xiao Ming Hu, Junjun Hu, Zengxin Zhang, Xinhua Zhang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Understanding the Earth's energy cycle and water balance requires an understanding of the distribution of precipitation types 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-based precipitation products that have difficulty in classifying precipitation. The newly available NOAA/National Severe Storms Laboratory ground radar network-based National Multi-Sensor Mosaic QPE (NMQ/Q2) System has provided precipitation rates and types at unprecedented high spatiotemporal resolution. Here, four years of 1 km/5 min observations derived from the NMQ are used to probe spatiotemporal distribution and characteristics of precipitation types (stratiform, convective, snow, tropical/warm (T/W), and hail) over CONUS, resulting in assessment of occurrence and volume contribution for these precipitation types through the four-year period, including seasonal distributions, with some radar coverage artifacts. These maps in general 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 total occurrences (contribution of total rain amount/volume) of these types are 72.88% (53.91%) for stratiform, 21.15% (7.64%) for snow, 2.95% (19.31%) for T/W, 2.77% (14.03%) for convective, and 0.24% (5.11%) for hail. This paper makes it possible to prototype a near seamless high-resolution reference for evaluating satellite swath-based precipitation type retrievals and also a potentially useful forcing database for energy-water balance budgeting and hydrological prediction for the United States.

Original languageEnglish (US)
Article number7054506
Pages (from-to)4434-4443
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number8
DOIs
StatePublished - Aug 1 2015
Externally publishedYes

Keywords

  • Radar
  • snow

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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