Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model
Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast m...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/7530759 |
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| author | Isaac Mugume Charles Basalirwa Daniel Waiswa Joachim Reuder Michel d. S. Mesquita Sulin Tao Triphonia J. Ngailo |
| author_facet | Isaac Mugume Charles Basalirwa Daniel Waiswa Joachim Reuder Michel d. S. Mesquita Sulin Tao Triphonia J. Ngailo |
| author_sort | Isaac Mugume |
| collection | DOAJ |
| description | Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE), the mean absolute error (MAE), mean error (ME), skewness, and the bias easy estimate (BES)) and nonparametric (the sign test, STM) methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves) show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model. |
| format | Article |
| id | doaj-art-1c3a529ecf46468aa962810976a0e739 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-1c3a529ecf46468aa962810976a0e7392025-02-03T05:52:20ZengWileyModelling and Simulation in Engineering1687-55911687-56052016-01-01201610.1155/2016/75307597530759Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical ModelIsaac Mugume0Charles Basalirwa1Daniel Waiswa2Joachim Reuder3Michel d. S. Mesquita4Sulin Tao5Triphonia J. Ngailo6Department of Geography, Geoinformatics and Climatic Sciences, Makerere University, P.O. Box 7062, Kampala, UgandaDepartment of Geography, Geoinformatics and Climatic Sciences, Makerere University, P.O. Box 7062, Kampala, UgandaDepartment of Geography, Geoinformatics and Climatic Sciences, Makerere University, P.O. Box 7062, Kampala, UgandaGeophysical Institute, University of Bergen, Allegaten 70, 5007 Bergen, NorwayUni Research Climate, Bjerknes Centre for Climate Research, Bergen, NorwaySchool of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 21004, ChinaDepartment of General Studies, Dar es Salaam Institute of Technology, P.O. Box 2958, Dar-es-Salaam, TanzaniaNumerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE), the mean absolute error (MAE), mean error (ME), skewness, and the bias easy estimate (BES)) and nonparametric (the sign test, STM) methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves) show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.http://dx.doi.org/10.1155/2016/7530759 |
| spellingShingle | Isaac Mugume Charles Basalirwa Daniel Waiswa Joachim Reuder Michel d. S. Mesquita Sulin Tao Triphonia J. Ngailo Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model Modelling and Simulation in Engineering |
| title | Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model |
| title_full | Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model |
| title_fullStr | Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model |
| title_full_unstemmed | Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model |
| title_short | Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model |
| title_sort | comparison of parametric and nonparametric methods for analyzing the bias of a numerical model |
| url | http://dx.doi.org/10.1155/2016/7530759 |
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