气候变化领域集成服务门户(CCPortal): Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

CCPortal

DOI	10.5194/hess-23-207-2019
	Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS
	Beck H.E.; Pan M.; Roy T.; Weedon G.P.; Pappenberger F.; Van Dijk A.I.J.M.; Huffman G.J.; Adler R.F.; Wood E.F.
发表日期	2019
ISSN	1027-5606
起始页码	207
结束页码	224
卷号	23 期号:1
英文摘要	New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008-2017 using the Kling-Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convectionpermitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application. © Author(s) 2019.
语种	英语
scopus关键词	Climate models; Satellites; Space-based radar; Weather forecasting; Earth system model; Event identification; High-resolution models; Merging techniques; Performance metrices; Retrieval algorithms; Satellite retrieval; Weather forecasting model; Gages; algorithm; climate modeling; data set; diurnal variation; ensemble forecasting; gauge; hydrological modeling; mountain region; performance assessment; precipitation assessment; precipitation intensity; radar; satellite altimetry
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159793
作者单位	Beck, H.E., Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Pan, M., Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Roy, T., Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Weedon, G.P., Met Office, JCHMR, Maclean Building, Benson Lane, Crowmarsh Gifford, Oxfordshire, United Kingdom; Pappenberger, F., European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom; Van Dijk, A.I.J.M., Fenner School for Environment and Society, Australian National University, Canberra, Australia; Huffman, G.J., NASA Goddard Space Flight Center (GSFC), Greenbelt, MD, United States; Adler, R.F., University of Maryland, Earth System Science Interdisciplinary Center, College Park, MD, United States; Wood, E.F., Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States
推荐引用方式 GB/T 7714	Beck H.E.,Pan M.,Roy T.,et al. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS[J],2019,23(1).
APA	Beck H.E..,Pan M..,Roy T..,Weedon G.P..,Pappenberger F..,...&Wood E.F..(2019).Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS.Hydrology and Earth System Sciences,23(1).
MLA	Beck H.E.,et al."Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS".Hydrology and Earth System Sciences 23.1(2019).