Comparison of GCM precipitation predictions with their RMSEs and pattern correlation coefficients

Chulsang Yoo; Eunsaem Cho

doi:10.3390/w10010028

Comparison of GCM precipitation predictions with their RMSEs and pattern correlation coefficients

Chulsang Yoo, Eunsaem Cho

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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.

Original language	English
Article number	28
Journal	Water (Switzerland)
Volume	10
Issue number	1
DOIs	https://doi.org/10.3390/w10010028
Publication status	Published - 2018 Jan 2

Keywords

GCM precipitation
NRMSE
Pattern correlation
Performance evaluation

ASJC Scopus subject areas

Biochemistry
Geography, Planning and Development
Aquatic Science
Water Science and Technology

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10.3390/w10010028

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title = "Comparison of GCM precipitation predictions with their RMSEs and pattern correlation coefficients",

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.",

keywords = "GCM precipitation, NRMSE, Pattern correlation, Performance evaluation",

author = "Chulsang Yoo and Eunsaem Cho",

note = "Funding Information: Acknowledgments: This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 17AWMP-B083066-04). Publisher Copyright: {\textcopyright} 2018 by the authors.",

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doi = "10.3390/w10010028",

language = "English",

volume = "10",

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AU - Cho, Eunsaem

N1 - Funding Information: Acknowledgments: This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 17AWMP-B083066-04). Publisher Copyright: © 2018 by the authors.

PY - 2018/1/2

Y1 - 2018/1/2

N2 - 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.

AB - 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.

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KW - Performance evaluation

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Comparison of GCM precipitation predictions with their RMSEs and pattern correlation coefficients

Abstract

Keywords

ASJC Scopus subject areas

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