Date of Award
Master of Science (MS)
To better understand the precipitation variability over the continental United States (CONUS), an accurate temporally and spatially homogenous precipitation dataset should be used. Recently developed precipitation products, including satellite-based, radar-based, and atmospheric reanalysis products appear to fit these criteria, however, their uncertainties must first be addressed. This study is divided into two parts. Part I focuses on a comparison between satellite-based GPCP IDD estimates and radar-based NMQ Q2 estimates, offering physical insight into the differences between the two datasets. Part II evaluates the precipitation estimates from five reanalysis products, and studies the precipitation trend over the CONUS over the last three decades using GPCP monthly product, where the uncertainties associated with GPCP datasets found in part I will be addressed.
In part I of this study, spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from the six selected regions during the period from 2010 through 2012. Correlation coefficients for daily estimates over the selected regions range from 0.355 to 0.516 with mean differences (GPCP-Q2) varying from -0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. The comparisons between two datasets are also conducted for warm (April-September) and cold (October-March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 mm for Q2 vs. 941.79 mm for GPCP), Q2 estimates are much higher than GPCP over the central and southern CONUS and lower than GPCP estimates in the northeastern US. These results suggest that Q2 may have difficulty accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.
In part II of this study, precipitation estimates from five reanalyses (ERA-Interim, MERRA2, JRA-55, CFSR, and 20CR) are compared against the GPCP satellite-gauge (SG) combined product over the CONUS during the period from 1980 through 2013. Compared to the annual averaged precipitation of 2.38 mm/day from GPCP, CFSR has the same annual mean, ERA-Interim and MERRA2 have negative biases of -9.2% and -3.8% respectively, while JRA-55 and 20CR have positive biases of 9.7% and 12.6% respectively. The reanalyses capture the variability of precipitation distribution over the CONUS as derived from GPCP; however, large regional differences exist. The reanalyses generally overestimate the precipitation over the western part of the country throughout the year, which could be due to the difficulty of accurately estimating precipitation over complex terrain. Underestimations in reanalyses over the northeastern US during fall and winter seasons indicate that the five selected reanalyses may be less skillful in reproducing snowfall events. Furthermore, systematic errors exist in all five reanalysese suggest that their physical processes in modeling precipitation need to be improved in the future. We also conduct a long-term trend analysis of precipitation over the CONUS using GPCP and reanalyzed precipitation products from 1980 to 2013. Based on the linear regression of GPCP data, there is a decreasing trend of 2.00 mm/year. For spatial distribution, only north-central and northeastern parts of the county show positive trends, while other areas show negative trends on through the course of a year. Compared to the GPCP observed long-term trend of precipitation, all reanalyses except for 20CR exhibit similar inter-annual variation. Although comprehensive reanalyses that assimilate both satellite and in-situ observations can provide more reasonable precipitation estimates, substantial efforts are still required to further improve the reanalyzed precipitation over the CONUS.
Cui, Wenjun, "Comparison And Evaluation Of Precipitation Products From Radar, Satellite, And Reanalyses Over The Continental United States" (2016). Theses and Dissertations. 2006.