Abstract
Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( < 50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.
Highlights
Precipitation (P ) is arguably the most important driver of the hydrological cycle, and one of the most challenging to estimate (Daly et al, 2008; Michaelides et al, 2009; Kidd and Levizzani, 2011; Tapiador et al, 2012)
We considered the following five performance metrics to evaluate the P datasets in terms of temporal dynamics: (i) Pearson linear correlation coefficient (R) calculated for 3-day means (R3 day); (ii) R calculated for monthly means (Rmonthly); (iii) R calculated for 6-month Standardized Precipitation Index values (RSPI−6; Hayes et al, 1999); (iv) mean absolute error (MAE; mm month−1) for monthly means; and (v) the trend error
In terms of temporal correlations (R3 day, Rmonthly, and RSPI−6), the satellite- and reanalysis-based Multi-Source Weighted-Ensemble Precipitation (MSWEP)-ng datasets performed overall slightly better than the reanalyses (ERAInterim, JRA-55, and National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR)) and the satellite- and reanalysis-based Climate Hazards group Infrared Precipitation (CHIRP) V2.0 dataset, which in turn performed slightly better than the satellite datasets based primarily on passive microwave retrievals (CMORPH V1.0, Global Satellite Mapping of Precipitation (GSMaP) V5/6, and TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42RT V7) and near-surface soil moisture (SM2RAIN-Advanced Scatterometer (ASCAT)), which in turn performed slightly better than the satellite datasets based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-Classification System (CCS))
Summary
Precipitation (P ) is arguably the most important driver of the hydrological cycle, and one of the most challenging to estimate (Daly et al, 2008; Michaelides et al, 2009; Kidd and Levizzani, 2011; Tapiador et al, 2012). Several gridded P datasets have been developed that are suitable for large-scale hydrological applications Beck et al.: Global evaluation of 22 precipitation datasets of design objective (temporal homogeneity, instantaneous accuracy, or both), data sources (radar, gauge, satellite, analysis, or reanalysis, or combinations thereof), spatial resolution (from 0.05 to 2.5◦), spatial coverage (from continental to fully global), published temporal resolution (from 30 min to monthly), temporal span (from ∼ 1 to 115 years), and latency (from ∼ 3 h to several years)
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