Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model

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...

Full description

Saved in:
Bibliographic Details
Published in:Modelling and simulation in engineering Vol. 2016; pp. 1 - 7
Main Authors: Mugume, Isaac, Basalirwa, Charles, Waiswa, Daniel, Reuder, Joachim, Mesquita, Michel d. S., Tao, Sulin, Ngailo, Triphonia J.
Format: Journal Article
Language:English
Published: New York Hindawi Publishing Corporation 01-01-2016
Hindawi Limited
Subjects:
Bias
Computer simulation
Error analysis
Estimates
Extreme values
Mathematical models
Mean square errors
Methods
Rain
Regression analysis
Simulation
Skewness
Studies
Weather forecasting
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1687-5591
1687-5605
DOI:10.1155/2016/7530759