Precipitation intercomparison of a set of satellite- and raingauge-derived datasets, ERA Interim reanalysis, and a single WRF regional climate simulation over Europe and the North Atlantic

Skok, Gregor; Žagar, Nedjeljka; Honzak, Luka; Žabkar, Rahela; Rakovec, Jože; Ceglar, Andrej

doi:10.1007/s00704-014-1350-5

Cited by 42 publications

(40 citation statements)

References 33 publications

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“…The infrared-and microwave-based satellite datasets also performed reasonably well, although the P frequency was generally overestimated at low and mid latitudes and underestimated at high latitudes, reflecting the difficulty of detecting P signals at high latitudes (Ferraro et al, 1998;Kidd and Levizzani, 2011;Kidd et al, 2012;Laviola et al, 2013). Conversely, the reanalyses consistently underestimated the number of dry days across the globe, due to the presence of spurious drizzle caused by deficiencies in the representation and/or parameterization of the physical processes governing P generation (Zolina et al, 2004;Lopez, 2007;Sun et al, 2006;Skok et al, 2015). SM2RAIN-ASCAT also consistently underestimated the number of dry days due to the presence of spurious drizzle, in this case due to the relatively noisy soil moisture retrievals (Crow et al, 2011;Brocca et al, 2014) and the use of the already fairly wet ERA-Interim dataset for the algorithm calibration.…”

Section: Performance For Climate Indicesmentioning

confidence: 87%

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

Beck

Vergopolan

Pan

et al. 2017

Hydrol. Earth Syst. Sci.

642

431

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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.

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Section: Performance For Climate Indicesmentioning

confidence: 87%

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

Beck

Vergopolan

Pan

et al. 2017

Hydrol. Earth Syst. Sci.

642

431

View full text Add to dashboard Cite

show abstract

“…They are particularly suitable for rainfall estimation in the tropics, which exhibit highly heterogeneous rainfall patterns due to the importance of convective storms (Smith et al, 2005). However, satellite retrieval approaches are susceptible to systematic biases, relatively insensitive to light rainfall events, and tend to fail over snow-and ice-covered surfaces (Ferraro et al, 1998;Ebert et al, 2007;Kidd and Levizzani, 2011;Kidd et al, 2012;Laviola et al, 2013).…”

mentioning

confidence: 99%

“…The datasets have been designed for different applications and provide sometimes widely varying P estimates Bosilovich et al, 2008;Kucera et al, 2013;Skok et al, 2015;Prein and Gobiet, 2016), even among gauge-adjusted datasets (Herold et al, 2015). Despite several studies that intercompared and evaluated these P datasets in different regions (for non-exhaustive overviews, see SerratCapdevila et al, 2013 andMaggioni et al, 2016), so far no clear consensus has emerged on which estimation approach is superior overall.…”

mentioning

confidence: 99%

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Beck

Dijk

Levizzani

et al. 2017

Hydrol. Earth Syst. Sci.

873

640

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Abstract. Current global precipitation (P ) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3-hourly temporal and 0.25 • spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km 2 ) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

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“…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]...…”

Section: Introductionmentioning

confidence: 99%

Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm

et al. 2018

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Satellite-based precipitation products (e.g., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and its predecessor, Tropical Rainfall Measuring Mission (TRMM)) are a critical source of precipitation estimation, particularly for a region with less, or no, hydrometric networking. However, the inconsistency in the performance of these products has been observed in different climatic and topographic diverse regions, timescales, and precipitation intensities and there is still room for improvement. Hence, using a projected ensemble algorithm, the regional precipitation estimate (RP) is introduced here. The RP concept is mainly based on the regional performance weights derived from the Mean Square Error (MSE) and the precipitation estimate from the TRMM product, that is, TRMM 3B42 (TR), real-time (late) (IT) and the research (post-real-time) (IR) products of IMERG. The overall results of the selected contingency table (e.g., Probability of detection (POD)) and statistical indices (e.g., Correlation Coefficient (CC)) signposted that the proposed RP product has shown an overall better potential to capture the gauge observations compared with the TR, IR, and IT in five different climatic regions of Pakistan from January 2015 to December 2016, at a diurnal time scale. The current study could be the first research providing preliminary feedback from Pakistan for global precipitation measurement researchers by highlighting the need for refinement in the IMERG.

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Precipitation intercomparison of a set of satellite- and raingauge-derived datasets, ERA Interim reanalysis, and a single WRF regional climate simulation over Europe and the North Atlantic

Cited by 42 publications

References 33 publications

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

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

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm

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