Performance Assessment of Multi-Source Weighted-Ensemble Precipitation (MSWEP) Product over India

Nair, Akhilesh S.; Indu, J.

doi:10.3390/cli5010002

Cited by 44 publications

(24 citation statements)

References 35 publications

(24 reference statements)

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“…Blue Nile, Colombian Andes, Peruvian Andes, and Taiwan), MSWEP performance is the worst relative to other SPPs when we consider MRE and CRMSE metrics; it has the highest overestimation for lower precipitation values and the highest underestimation for higher values. This outcome aligns with findings by Zambrano-Bigiarini and others [49], and by Nair and Indu [50]. Zambrano-Bigiarini and others [49] evaluated multiple SPPs over complex terrain over Chile, including MSWEP, CMORPH, and TMPA.…”

Section: Discussionsupporting

confidence: 84%

Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions

et al. 2019

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The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B.

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Section: Discussionsupporting

confidence: 84%

Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions

et al. 2019

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“…However, many studies considered only a single P dataset (e.g., Scheel et al, 2011;Nair and Indu, 2017) or disregarded (re)analysis-based P datasets (e.g., Moazami et al, 2013;Mei et al, 2014;Zambrano-Bigiarini et al, 2017), despite their demonstrated superior performance in cold climates Beck et al, 2017b;. In addition, some studies re-used gauge observations already incorporated in some of the P datasets to determine their accuracy (e.g., Chen et al, 2013;Ashouri et al, 2016;Zambrano-Bigiarini et al, 2017), precluding independent validation.…”

mentioning

confidence: 99%

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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“…Studies have been extensively performed to evaluate different precipitation products (e.g., gauge-based, and reanalysis and remote sensing-related datasets) over the globe [98][99][100][101][102]. For example, Nair and Indu [99] noted that the MSWEP products (input for GLEAM3.0a) in India showed large errors in higher precipitation (i.e., >75th and >95th quantiles), which was confirmed by Alijanian et al [98] in Iran. Sun et al [102] found that the CPC-U precipitation (input for MERRA-Land) averaged over the world was underestimated for each season and correspondingly led to the annual value being the smallest compared to other datasets.…”

Section: Model Inputsmentioning

confidence: 80%

“…If a given ET model is ideally full-biophysical and, thus, can comprehensively describe the ET processes, errors in the ET estimates and differences among the ET products are mainly dependent on various inputs, especially for precipitation and radiation [36,54,60,75,76,79]. Studies have been extensively performed to evaluate different precipitation products (e.g., gauge-based, and reanalysis and remote sensing-related datasets) over the globe [98][99][100][101][102]. For example, Nair and Indu [99] noted that the MSWEP products (input for GLEAM3.0a) in India showed large errors in higher precipitation (i.e., >75th and >95th quantiles), which was confirmed by Alijanian et al [98] in Iran.…”

Section: Model Inputsmentioning

confidence: 99%

Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

Wang

Sun

et al. 2018

Remote Sensing

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As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions.

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Performance Assessment of Multi-Source Weighted-Ensemble Precipitation (MSWEP) Product over India

Cited by 44 publications

References 35 publications

Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions

Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions

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

Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

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