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

被引:79
作者
Prat, O. P. [1 ,2 ]
Nelson, B. R. [3 ]
机构
[1] N Carolina State Univ, CICS NC, Asheville, NC 28804 USA
[2] NOAA, Natl Ctr Environm Informat, Asheville, NC USA
[3] NOAA, Ctr Weather & Climate, Natl Ctr Environm Informat, Asheville, NC USA
关键词
SOUTHEASTERN UNITED-STATES; GLOBAL PRECIPITATION; TRMM; RESOLUTION; VALIDATION; MODEL;
D O I
10.5194/hess-19-2037-2015
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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.
引用
收藏
页码:2037 / 2056
页数:20
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