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Water 2018, 10(1), 28; https://doi.org/10.3390/w10010028

Comparison of GCM Precipitation Predictions with Their RMSEs and Pattern Correlation Coefficients

School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul 02841, Korea
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Received: 7 September 2017 / Revised: 24 December 2017 / Accepted: 29 December 2017 / Published: 2 January 2018
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Abstract

This study evaluated 20 general circulation models (GCMs) of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), which provide the prediction results for the period of 2006 to 2014, the period from which the observation data (the Global Precipitation Climatology Project (GPCP) data) are available. Both the GCM predictions of precipitation and the GPCP data were compared for three data structures—the global, zonal, and grid mean—with conventional statistics like the root mean square error (RMSE) and the pattern correlation coefficient of the cyclostationary empirical orthogonal functions (CSEOFs). As a result, it was possible to select a GCM which showed the best performance among the 20 GCMs considered in this study. Overall, the NorSM1-M model was found to be the most similar to the GPCP data. Additionally, the IPSL-CM5A-LR, BCC-CSM, and GFDL-CMS models were also found to be quite similar to the GPCP data. View Full-Text
Keywords: performance evaluation; GCM precipitation; NRMSE; pattern correlation performance evaluation; GCM precipitation; NRMSE; pattern correlation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yoo, C.; Cho, E. Comparison of GCM Precipitation Predictions with Their RMSEs and Pattern Correlation Coefficients. Water 2018, 10, 28.

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