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Evaluation of Precipitation Estimates Over Conus Derived from Satellite, Radar, and Rain Gauge Data Sets at Daily to Annual Scales (2002–2012) : Volume 19, Issue 4 (29/04/2015)

By Prat, O. P.

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Book Id: WPLBN0003972402
Format Type: PDF Article :
File Size: Pages 20
Reproduction Date: 2015

Title: Evaluation of Precipitation Estimates Over Conus Derived from Satellite, Radar, and Rain Gauge Data Sets at Daily to Annual Scales (2002–2012) : Volume 19, Issue 4 (29/04/2015)  
Author: Prat, O. P.
Volume: Vol. 19, Issue 4
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Nelson, B. R., & Prat, O. P. (2015). Evaluation of Precipitation Estimates Over Conus Derived from Satellite, Radar, and Rain Gauge Data Sets at Daily to Annual Scales (2002–2012) : Volume 19, Issue 4 (29/04/2015). Retrieved from http://kindle.worldlibrary.net/


Description
Description: Cooperative Institute for Climate and Satellites-NC (CICS-NC), North Carolina State University, Asheville, USA. We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002–2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (−33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day−1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day−1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

Summary
Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012)

Excerpt
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