TY - JOUR
T1 - Evaluation of reanalyzed precipitation variability and trends using the gridded gauge-based analysis over the CONUS
AU - Cui, Wenjun
AU - Dong, Xiquan
AU - Xi, Baike
AU - Kennedy, Aaron
N1 - Funding Information:
CPC U.S. Unified precipitation data were provided by the NOAA/OAR/ESRL PSD, from their website at http://www.esrl.noaa.gov/psd/. GPCP and MERRA2 data were provided by the Laboratory for Atmospheres, NASA Goddard Space Flight Center: GPCP v2.2 combined data were downloaded from http://precip.gsfc.nasa.gov/and MERRA2 data were downloaded from http://disc.sci.gsfc.nasa.gov/mdisc/. CFSR and JRA-55 data were obtained from NCAR/UCAR's Research Data Archive at http://rda. ucar.edu/. ERA-Interim data were provided by ECMWF at http://apps.ecmwf.int/datasets/. 20CRv2c data were provided by NOAA/Earth System Research Laboratory at http://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2c.html. NARR data were downloaded from http://www.esrl.noaa.gov/psd/data/gridded/data.narr.html. This research was supported by NOAA Climate Program Office MAPP project with Award NA13OAR4310105 at the University of North Dakota and NOAA R2O project with Award NA15NWS468004 at the University of North Dakota and subaward to the University of Arizona.
Publisher Copyright:
© 2017 American Meteorological Society.
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Atmospheric reanalyses have been used in many studies to investigate the variabilities and trends of precipitation because of their global coverage and long record; however, their results must be properly analyzed and their uncertainties must be understood. In this study, precipitation estimates from five global reanalyses [ERA-Interim; MERRA, version 2 (MERRA2); JRA-55; CFSR; and 20CR, version 2c (20CRv2c)] and one regional reanalysis (NARR) are compared against the CPC Unified Gauge-Based Analysis (CPCUGA) and GPCP over the contiguous United States (CONUS) during the period 1980-2013. Reanalyses capture the variability of the precipitation distribution over the CONUS as observed in CPCUGA and GPCP, but large regional and seasonal differences exist. Compared with CPCUGA, global reanalyses generally overestimate the precipitation over the western part of the country throughout the year and over the northeastern CONUS during the fall and winter seasons. These issues may be associated with the difficulties models have in accurately simulating precipitation over complex terrain and during snowfall events. Furthermore, systematic errors found in five global reanalyses suggest that their physical processes in modeling precipitation need to be improved. Even though negative biases exist in NARR, its spatial variability is similar to both CPCUGA and GPCP; this is anticipated because it assimilates observed precipitation, unlike the global reanalyses. Based on CPCUGA, there is an average decreasing trend of -1.38 mm yr-1 over the CONUS, which varies depending on the region with only the north-central to northeastern parts of the country having positive trends. Although all reanalyses exhibit similar interannual variation as observed in CPCUGA, their estimated precipitation trends, both linear and spatial trends, are distinct from CPCUGA.
AB - Atmospheric reanalyses have been used in many studies to investigate the variabilities and trends of precipitation because of their global coverage and long record; however, their results must be properly analyzed and their uncertainties must be understood. In this study, precipitation estimates from five global reanalyses [ERA-Interim; MERRA, version 2 (MERRA2); JRA-55; CFSR; and 20CR, version 2c (20CRv2c)] and one regional reanalysis (NARR) are compared against the CPC Unified Gauge-Based Analysis (CPCUGA) and GPCP over the contiguous United States (CONUS) during the period 1980-2013. Reanalyses capture the variability of the precipitation distribution over the CONUS as observed in CPCUGA and GPCP, but large regional and seasonal differences exist. Compared with CPCUGA, global reanalyses generally overestimate the precipitation over the western part of the country throughout the year and over the northeastern CONUS during the fall and winter seasons. These issues may be associated with the difficulties models have in accurately simulating precipitation over complex terrain and during snowfall events. Furthermore, systematic errors found in five global reanalyses suggest that their physical processes in modeling precipitation need to be improved. Even though negative biases exist in NARR, its spatial variability is similar to both CPCUGA and GPCP; this is anticipated because it assimilates observed precipitation, unlike the global reanalyses. Based on CPCUGA, there is an average decreasing trend of -1.38 mm yr-1 over the CONUS, which varies depending on the region with only the north-central to northeastern parts of the country having positive trends. Although all reanalyses exhibit similar interannual variation as observed in CPCUGA, their estimated precipitation trends, both linear and spatial trends, are distinct from CPCUGA.
KW - North America
KW - Precipitation
KW - Reanalysis data
KW - Satellite observations
KW - Statistics
KW - Surface observations
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U2 - 10.1175/JHM-D-17-0029.1
DO - 10.1175/JHM-D-17-0029.1
M3 - Article
AN - SCOPUS:85027979962
SN - 1525-755X
VL - 18
SP - 2227
EP - 2248
JO - Journal of Hydrometeorology
JF - Journal of Hydrometeorology
IS - 8
ER -