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.
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Publication date: Available online 4 January 2018 Source: European Journal of Radiology Author(s): Peiyao Zhang, Jing Wang, Qin Xu, Zhen...
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Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182, Butyric Acid from Probiotic Staphyloco...
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Does CBD Oil Lower Blood Pressure? This article was originally published at SundayScaries." Madeline Taylor POSTED ON January 13, 20...
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Medicine by Alexandros G. Sfakianakis,Αλέξανδρος Γ. Σφακιανάκης A Novel Technique for Endoscopic Repair of Large Anterior Skull Base Defect...
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