Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

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 gaugecorrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to mediumsized ( < 50 000 km 2 ) catchments worldwide, and comparing the resulting performance. Marked differences in spatiotemporal 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, PER-SIANN, 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.

Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

“…In practice, the satellite precipitation error has both (1) a direct effect on stream flow estimates by determining under(over) estimations due to the erroneous instantaneous precipitation and (2) an indirect effect on the state estimation that propagates in time for several days/months causing additional stream flow errors. The low scores of the stream flow estimates derived from satellite-based rainfall observations are in line with those found in many other studies in literature [30,37,71,72]. In the latter, it was who found that reanalysis-based rainfall products generally outperform satellite-based ones in hydrological modelling.…”

“…Daily stream flow observations from 10 basins (4-9 years of data) throughout the Mediterranean area are used as benchmark to address the above research questions. The application of the two schemes to a reanalysis and a satellite-based rainfall product aims to simulate a scenario of ground data scarcity as happens in many regions of the world [37].…”

Toward continental hydrologic–hydrodynamic modeling in South America

“…This is a 3-hourly, global-scale dataset (0.25º resolution) that optimally combines satellite, reanalysis and daily gauge data, and it has been evaluated with satisfactory results in a recent comparison 15 of several precipitation datasets (Beck et al, 2017c). Regarding climate variables used to compute evapotranspiration, mean monthly data for the period 1961-1990 were retrieved from the Climate Research Unit -CRU Global Climate v.2 (New et al, 2002), which provides long-term climatologies of temperature, pressure, radiation and wind speed for all land areas at 10 resolution.…”

Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm

“…The most widely used products include the Climate Prediction Center morphing technique (CMORPH) [1], the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT)'s Multi-sensor Precipitation Estimate (MPE) [2], the European Centre for Medium-Range Weather Forecasts (ECMWF)'s Era-Interim product [3], the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), the PERSIANN Cloud Classification System estimation (PERSIANN-CCS) [4], the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis products (TMPA) versions 6 (3B42-V6) and 7 (3B42-V7) [5,6] and the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) product [7]. Numerous studies have been made to evaluate the performance of these precipitation satellite products on the regional and global scale (e.g., Asia [8][9][10][11], North and Central America [12][13][14][15][16], South America [17][18][19][20], Europe [21][22][23][24], Australia [25][26][27][28], Oceans [29,30], other [31][32][33][34][35][36] and in Pakistan [37][38][39][40][41]...…”