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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-481
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 27 Sep 2018

Research article | 27 Sep 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

Hylke E. Beck1, Ming Pan1, Tirthankar Roy1, Graham P. Weedon2, Florian Pappenberger3, Albert I. J. M. van Dijk4, George J. Huffman5, Robert F. Adler6, and Eric F. Wood1 Hylke E. Beck et al.
  • 1Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
  • 2Met Office, JCHMR, Maclean Building, Benson Lane, Crowmarsh Gifford, Oxfordshire, UK
  • 3European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
  • 4Fenner School for Environment and Society, Australian National University, Canberra, Australia
  • 5NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
  • 6University of Maryland, Earth System Science Interdisciplinary Center, College Park, Maryland, USA

Abstract. 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 in 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 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 fourth-generation reanalysis ERA5-HRES, reflecting the significant advances in earth system modeling during the last decade. 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 regional convection-permitting climate model WRF 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 can be used as a guide to choose the most suitable P dataset for a particular application.

Hylke E. Beck et al.
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Using the conterminous US as a case study, we evaluated the performance of 26 precipitation datasets. The best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for reporting times. Our findings can be used as a guide to choose the most suitable precipitation dataset for a particular application.
Using the conterminous US as a case study, we evaluated the performance of 26 precipitation...
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