Articles | Volume 9, issue 12
https://doi.org/10.5194/gmd-9-4365-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-4365-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A diagram for evaluating multiple aspects of model performance in simulating vector fields
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing, China
Zhaolu Hou
Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing,
China
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Ying Han
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing, China
Weidong Guo
Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing,
China
Joint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing, China
Viewed
Total article views: 5,220 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Aug 2016)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
3,641 | 1,434 | 145 | 5,220 | 395 | 184 | 205 |
- HTML: 3,641
- PDF: 1,434
- XML: 145
- Total: 5,220
- Supplement: 395
- BibTeX: 184
- EndNote: 205
Total article views: 4,783 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Dec 2016)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
3,444 | 1,197 | 142 | 4,783 | 290 | 180 | 201 |
- HTML: 3,444
- PDF: 1,197
- XML: 142
- Total: 4,783
- Supplement: 290
- BibTeX: 180
- EndNote: 201
Total article views: 437 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Aug 2016)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
197 | 237 | 3 | 437 | 105 | 4 | 4 |
- HTML: 197
- PDF: 237
- XML: 3
- Total: 437
- Supplement: 105
- BibTeX: 4
- EndNote: 4
Cited
63 citations as recorded by crossref.
- Evaluation and projection of the AMO and PDO variabilities in the CMIP5 models under different warming scenarios part1: Evaluation S. Xia et al. 10.1016/j.dynatmoce.2021.101260
- Development of hybrid machine learning-based carbonation models with weighting function Z. Chen et al. 10.1016/j.conbuildmat.2022.126359
- Cloud Resolving WRF Simulations of Precipitation and Soil Moisture Over the Central Tibetan Plateau: An Assessment of Various Physics Options M. Lv et al. 10.1029/2019EA000865
- Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the runoff H. Guo et al. 10.1007/s00704-022-04118-0
- Decadal change in the relationship between East Asian spring circulation and ENSO: Is it modulated by Pacific Decadal Oscillation? Y. Wang et al. 10.1002/joc.5793
- The linkage between CMIP5 climate models’ abilities to simulate precipitation and vector winds F. Huang et al. 10.1007/s00382-020-05259-6
- Projected Increase in Probability of East Asian Heavy Rainy Summer in the 21st Century by CMIP5 Models Y. Fu et al. 10.1007/s00376-021-0347-0
- Future projection of winter precipitation over northwest India and associated regions using CORDEX-SA experiments T. Midhuna & A. Dimri 10.1007/s00704-019-03049-7
- Simulation of summer climate over Central Asia shows high sensitivity to different land surface schemes in WRF S. Lu et al. 10.1007/s00382-021-05876-9
- Precipitation forecasting by large-scale climate indices and machine learning techniques M. Gholami Rostam et al. 10.1007/s40333-020-0097-3
- Performance of CMIP5 wind speed from global climate models for the Bay of Bengal region A. Krishnan & P. Bhaskaran 10.1002/joc.6404
- Multivariable integrated evaluation of model performance with the vector field evaluation diagram Z. Xu et al. 10.5194/gmd-10-3805-2017
- Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models F. Huang et al. 10.1007/s00382-018-4599-z
- Decompositions of Taylor diagram and DISO performance criteria Q. Zhou et al. 10.1002/joc.7149
- Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach M. Jamei et al. 10.1007/s11269-022-03270-6
- Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development C. Pande et al. 10.1016/j.jclepro.2024.141035
- Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios M. Zhang et al. 10.1038/s41597-024-03224-0
- Selecting a climate model subset to optimise key ensemble properties N. Herger et al. 10.5194/esd-9-135-2018
- Short communication comments on ‘DISO: A rethink of Taylor diagram’ Z. Xu & Y. Han 10.1002/joc.6359
- Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting M. Ali et al. 10.1016/j.renene.2023.01.108
- The Sailor diagram – A new diagram for the verification of two-dimensional vector data from multiple models J. Sáenz et al. 10.5194/gmd-13-3221-2020
- Wind-wave hindcast using modified ECMWF ERA-Interim wind field in the Mediterranean Sea A. Elkut et al. 10.1016/j.ecss.2021.107267
- Sensitivity of South Asian summer monsoon simulation to land surface schemes in Weather Research and Forecasting model S. Lu & H. Zuo 10.1002/joc.7278
- Combination of discretization regression with data-driven algorithms for modeling irrigation water quality indices . Dimple et al. 10.1016/j.ecoinf.2023.102093
- Impacts of Land Surface Parameterizations on Simulations over the Arid and Semiarid Regions: The Case of the Loess Plateau in China S. Lu et al. 10.1175/JHM-D-21-0143.1
- Designing a decomposition-based multi-phase pre-processing strategy coupled with EDBi-LSTM deep learning approach for sediment load forecasting M. Jamei et al. 10.1016/j.ecolind.2023.110478
- Application of Taylor diagram in the evaluation of joint environmental distributions' performances M. Simão et al. 10.1007/s40868-020-00081-5
- Evaluating vector winds over eastern China in 2022 predicted by the CMA-MESO model and ECMWF forecast F. Huang et al. 10.1016/j.aosl.2024.100559
- An improved multivariable integrated evaluation method and tool (MVIETool) v1.0 for multimodel intercomparison M. Zhang et al. 10.5194/gmd-14-3079-2021
- Near real-time significant wave height prediction along the coastline of Queensland using advanced hybrid machine learning models K. Khosravi et al. 10.1007/s13762-024-05944-7
- Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate Z. Xu et al. 10.1007/s00376-023-3101-y
- New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes M. Ali et al. 10.1016/j.ecolind.2023.111030
- Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks M. Ali et al. 10.1016/j.renene.2023.119773
- A double decomposition-based modelling approach to forecast weekly solar radiation R. Prasad et al. 10.1016/j.renene.2020.01.005
- Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts M. Ali et al. 10.1016/j.jhydrol.2020.124647
- A new diagram for performance evaluation of complex models A. Izzaddin et al. 10.1007/s00477-024-02678-3
- Evaluation of the WRF physics ensemble using a multivariable integrated evaluation approach over the Haihe river basin in northern China D. Dai et al. 10.1007/s00382-021-05723-x
- Evaluation of CMIP6 models toward dynamical downscaling over 14 CORDEX domains M. Zhang et al. 10.1007/s00382-022-06355-5
- Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia P. Rai et al. 10.1007/s00704-022-04233-y
- Reduced Spring Precipitation Bias and Associated Physical Causes over South China in FGOALS-f3 Climate Models: Experiments with the Horizontal Resolutions P. Zi et al. 10.1007/s13351-024-3200-4
- Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms M. Ali et al. 10.1016/j.rser.2020.110003
- Evaluation of Observed and Future Climate Change Projection for Uttarakhand, India, Using CORDEX-SA N. Tyagi et al. 10.3390/atmos13060947
- Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach M. Jamei et al. 10.1016/j.apenergy.2022.119925
- Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction M. Ali et al. 10.1038/s41598-022-09482-5
- Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism F. Li et al. 10.1016/j.jhydrol.2024.132197
- Climate change scenario projections and their implications on food systems in Taita Taveta County, Kenya F. Nyambariga et al. 10.1371/journal.pclm.0000114
- Prediction and prevention of concrete chloride penetration: machine learning and MICP techniques L. Li et al. 10.3389/fmats.2024.1445547
- Validation of net radiation from multi-models and satellite retrieval over Nigeria O. Ojo 10.1007/s40808-022-01647-5
- Bias correction and projection of temperature over the altitudes of the Upper Indus Basin under CMIP6 climate scenarios from 1985 to 2100 K. Jamal et al. 10.2166/wcc.2023.180
- The role of temporal resolution of meteorological inputs from reanalysis data in estimating air humidity for modelling applications M. Viggiano et al. 10.1016/j.agrformet.2021.108672
- Robust machine learning algorithms for predicting coastal water quality index M. Uddin et al. 10.1016/j.jenvman.2022.115923
- Assessing the performance of 33 CMIP6 models in simulating the large-scale environmental fields of tropical cyclones Y. Han et al. 10.1007/s00382-021-05986-4
- Future frequency and intensity of Western Disturbance(s) T. Midhuna et al. 10.1002/joc.7944
- A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems M. Pandey et al. 10.1007/s11269-023-03525-w
- Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation Z. Zheng et al. 10.1016/j.rser.2023.113645
- Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100) Z. Xu et al. 10.1038/s41597-021-01079-3
- Projected changes in summer water vapor transport over East Asia under the 1.5°C and 2.0°C global warming targets Z. XU & K. FAN 10.1080/16742834.2019.1569869
- Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia M. Ali et al. 10.1016/j.renene.2021.06.052
- Design data decomposition-based reference evapotranspiration forecasting model: A soft feature filter based deep learning driven approach Z. Zheng et al. 10.1016/j.engappai.2023.105984
- Performance evaluation of multi-satellite rainfall products in the Gidabo catchment, Rift Valley Basin, Ethiopia K. Gebretsadkan et al. 10.2166/wcc.2023.097
- Recent Progress in Numerical Atmospheric Modeling in China R. Yu et al. 10.1007/s00376-019-8203-1
- The Spectral Diagram as a new tool for model assessment in the frequency domain: Application to a global ocean general circulation model with tides M. Calim Costa et al. 10.1016/j.cageo.2021.104977
- Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions Z. Ebrahimi-Khusfi et al. 10.1007/s11356-020-10957-z
62 citations as recorded by crossref.
- Evaluation and projection of the AMO and PDO variabilities in the CMIP5 models under different warming scenarios part1: Evaluation S. Xia et al. 10.1016/j.dynatmoce.2021.101260
- Development of hybrid machine learning-based carbonation models with weighting function Z. Chen et al. 10.1016/j.conbuildmat.2022.126359
- Cloud Resolving WRF Simulations of Precipitation and Soil Moisture Over the Central Tibetan Plateau: An Assessment of Various Physics Options M. Lv et al. 10.1029/2019EA000865
- Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the runoff H. Guo et al. 10.1007/s00704-022-04118-0
- Decadal change in the relationship between East Asian spring circulation and ENSO: Is it modulated by Pacific Decadal Oscillation? Y. Wang et al. 10.1002/joc.5793
- The linkage between CMIP5 climate models’ abilities to simulate precipitation and vector winds F. Huang et al. 10.1007/s00382-020-05259-6
- Projected Increase in Probability of East Asian Heavy Rainy Summer in the 21st Century by CMIP5 Models Y. Fu et al. 10.1007/s00376-021-0347-0
- Future projection of winter precipitation over northwest India and associated regions using CORDEX-SA experiments T. Midhuna & A. Dimri 10.1007/s00704-019-03049-7
- Simulation of summer climate over Central Asia shows high sensitivity to different land surface schemes in WRF S. Lu et al. 10.1007/s00382-021-05876-9
- Precipitation forecasting by large-scale climate indices and machine learning techniques M. Gholami Rostam et al. 10.1007/s40333-020-0097-3
- Performance of CMIP5 wind speed from global climate models for the Bay of Bengal region A. Krishnan & P. Bhaskaran 10.1002/joc.6404
- Multivariable integrated evaluation of model performance with the vector field evaluation diagram Z. Xu et al. 10.5194/gmd-10-3805-2017
- Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models F. Huang et al. 10.1007/s00382-018-4599-z
- Decompositions of Taylor diagram and DISO performance criteria Q. Zhou et al. 10.1002/joc.7149
- Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach M. Jamei et al. 10.1007/s11269-022-03270-6
- Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development C. Pande et al. 10.1016/j.jclepro.2024.141035
- Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios M. Zhang et al. 10.1038/s41597-024-03224-0
- Selecting a climate model subset to optimise key ensemble properties N. Herger et al. 10.5194/esd-9-135-2018
- Short communication comments on ‘DISO: A rethink of Taylor diagram’ Z. Xu & Y. Han 10.1002/joc.6359
- Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting M. Ali et al. 10.1016/j.renene.2023.01.108
- The Sailor diagram – A new diagram for the verification of two-dimensional vector data from multiple models J. Sáenz et al. 10.5194/gmd-13-3221-2020
- Wind-wave hindcast using modified ECMWF ERA-Interim wind field in the Mediterranean Sea A. Elkut et al. 10.1016/j.ecss.2021.107267
- Sensitivity of South Asian summer monsoon simulation to land surface schemes in Weather Research and Forecasting model S. Lu & H. Zuo 10.1002/joc.7278
- Combination of discretization regression with data-driven algorithms for modeling irrigation water quality indices . Dimple et al. 10.1016/j.ecoinf.2023.102093
- Impacts of Land Surface Parameterizations on Simulations over the Arid and Semiarid Regions: The Case of the Loess Plateau in China S. Lu et al. 10.1175/JHM-D-21-0143.1
- Designing a decomposition-based multi-phase pre-processing strategy coupled with EDBi-LSTM deep learning approach for sediment load forecasting M. Jamei et al. 10.1016/j.ecolind.2023.110478
- Application of Taylor diagram in the evaluation of joint environmental distributions' performances M. Simão et al. 10.1007/s40868-020-00081-5
- Evaluating vector winds over eastern China in 2022 predicted by the CMA-MESO model and ECMWF forecast F. Huang et al. 10.1016/j.aosl.2024.100559
- An improved multivariable integrated evaluation method and tool (MVIETool) v1.0 for multimodel intercomparison M. Zhang et al. 10.5194/gmd-14-3079-2021
- Near real-time significant wave height prediction along the coastline of Queensland using advanced hybrid machine learning models K. Khosravi et al. 10.1007/s13762-024-05944-7
- Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate Z. Xu et al. 10.1007/s00376-023-3101-y
- New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes M. Ali et al. 10.1016/j.ecolind.2023.111030
- Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks M. Ali et al. 10.1016/j.renene.2023.119773
- A double decomposition-based modelling approach to forecast weekly solar radiation R. Prasad et al. 10.1016/j.renene.2020.01.005
- Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts M. Ali et al. 10.1016/j.jhydrol.2020.124647
- A new diagram for performance evaluation of complex models A. Izzaddin et al. 10.1007/s00477-024-02678-3
- Evaluation of the WRF physics ensemble using a multivariable integrated evaluation approach over the Haihe river basin in northern China D. Dai et al. 10.1007/s00382-021-05723-x
- Evaluation of CMIP6 models toward dynamical downscaling over 14 CORDEX domains M. Zhang et al. 10.1007/s00382-022-06355-5
- Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia P. Rai et al. 10.1007/s00704-022-04233-y
- Reduced Spring Precipitation Bias and Associated Physical Causes over South China in FGOALS-f3 Climate Models: Experiments with the Horizontal Resolutions P. Zi et al. 10.1007/s13351-024-3200-4
- Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms M. Ali et al. 10.1016/j.rser.2020.110003
- Evaluation of Observed and Future Climate Change Projection for Uttarakhand, India, Using CORDEX-SA N. Tyagi et al. 10.3390/atmos13060947
- Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach M. Jamei et al. 10.1016/j.apenergy.2022.119925
- Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction M. Ali et al. 10.1038/s41598-022-09482-5
- Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism F. Li et al. 10.1016/j.jhydrol.2024.132197
- Climate change scenario projections and their implications on food systems in Taita Taveta County, Kenya F. Nyambariga et al. 10.1371/journal.pclm.0000114
- Prediction and prevention of concrete chloride penetration: machine learning and MICP techniques L. Li et al. 10.3389/fmats.2024.1445547
- Validation of net radiation from multi-models and satellite retrieval over Nigeria O. Ojo 10.1007/s40808-022-01647-5
- Bias correction and projection of temperature over the altitudes of the Upper Indus Basin under CMIP6 climate scenarios from 1985 to 2100 K. Jamal et al. 10.2166/wcc.2023.180
- The role of temporal resolution of meteorological inputs from reanalysis data in estimating air humidity for modelling applications M. Viggiano et al. 10.1016/j.agrformet.2021.108672
- Robust machine learning algorithms for predicting coastal water quality index M. Uddin et al. 10.1016/j.jenvman.2022.115923
- Assessing the performance of 33 CMIP6 models in simulating the large-scale environmental fields of tropical cyclones Y. Han et al. 10.1007/s00382-021-05986-4
- Future frequency and intensity of Western Disturbance(s) T. Midhuna et al. 10.1002/joc.7944
- A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems M. Pandey et al. 10.1007/s11269-023-03525-w
- Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation Z. Zheng et al. 10.1016/j.rser.2023.113645
- Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100) Z. Xu et al. 10.1038/s41597-021-01079-3
- Projected changes in summer water vapor transport over East Asia under the 1.5°C and 2.0°C global warming targets Z. XU & K. FAN 10.1080/16742834.2019.1569869
- Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia M. Ali et al. 10.1016/j.renene.2021.06.052
- Design data decomposition-based reference evapotranspiration forecasting model: A soft feature filter based deep learning driven approach Z. Zheng et al. 10.1016/j.engappai.2023.105984
- Performance evaluation of multi-satellite rainfall products in the Gidabo catchment, Rift Valley Basin, Ethiopia K. Gebretsadkan et al. 10.2166/wcc.2023.097
- Recent Progress in Numerical Atmospheric Modeling in China R. Yu et al. 10.1007/s00376-019-8203-1
- The Spectral Diagram as a new tool for model assessment in the frequency domain: Application to a global ocean general circulation model with tides M. Calim Costa et al. 10.1016/j.cageo.2021.104977
Latest update: 14 Dec 2024
Short summary
This paper devises a new diagram called the vector field evaluation (VFE) diagram. The VFE diagram is a generalized Taylor diagram and is able to provide a concise evaluation of model performance in simulating vector fields (e.g., vector winds) in terms of three statistical variables. The VFE diagram can be applied to the evaluation of full vector fields or anomaly fields as needed. Some potential applications of the VFE diagram in model evaluation are also presented in the paper.
This paper devises a new diagram called the vector field evaluation (VFE) diagram. The VFE...