Updated 06 May 2021
Pearson correlation coefficient (r), coefficient of determination (r2), mean absolute error (MAE), root mean square error (RMSE), and other statistical methods are commonly used to compare model output with observed results. Traditional approaches aren't always the best for assessing model–data agreement or disagreement. The r or r2 methods, for example, can show the overall linear covariation between data and model results, but they must be combined with the slope and intercept of the linear regression to determine the degree to which the observed results are captured by the model. Willmott's index, on the other hand, is sensitive to variations between measured and modeled values and can reflect the degree to which the model can capture measured variance.
K NARENDER REDDY (2021). willmontt_index (https://github.com/KNR8070/willmott_index/releases/tag/1.1.0), GitHub. Retrieved .
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