8.2A Comparison of GPCP 1DD Precipitation Product with NEXRAD Q2 Precipitation Estimates over the CONUS

Thursday, 14 January 2016: 11:15 AM
Room 240/241 ( New Orleans Ernest N. Morial Convention Center)
Wenjun Cui, University of North Dakota, Grand Forks, ND; and X. Dong, B. Xi, and R. Stenz

This study compares the Global Precipitation Climatology Project, 1 degree daily (GPCP 1DD) precipitation estimates over the Central and Eastern conterminous United States (CONUS) with the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2) estimates. Monthly accumulated precipitation, annual accumulated precipitation and spatial averages were computed based on daily estimates for six selected regions during the period 2010 - 2012. The comparisons between the two datasets are conducted for both warm (April – September) and cold (October – March) seasons. For daily analysis, the correlations between GPCP 1DD estimates and Q2 estimates range from 0.372 to 0.518 and the differences between the two datasets range from -0.85mm to 1.05mm for selected regions. Better agreement is found in monthly analysis, with correlations varying from 0.603 to 0.78. Annual correlations vary from 0.418 to 0.887. GPCP 1DD estimates are well correlated with Q2 estimates in spatial analysis with correlations ranging from 0.657 to 0.959, and similar precipitation patterns are captured by the two datasets as shown by a distribution map. However, GPCP 1DD shows an inability to capture intense convective precipitation over the U.S. compared to Q2. Q2 estimates less cold season precipitation than GPCP 1DD estimates, suggesting that Q2 may have problems with measuring winter precipitation, especially snow. Although the annual difference between Q2 and GPCP precipitation estimates is only 1.3%, there are large seasonal differences, GPCP 1DD estimated 7.4% less precipitation than Q2 during the warm season, and estimated 8.3% more precipitation than Q2 during the cold season. The results from this study may provide valuable insight on the strengths and weaknesses of GPCP product and Q2 estimates to its users.
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