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willmontt_index

version 1.1.0 (3.71 KB) by K NARENDER REDDY
The Willmott index is a more advanced method to evaluate the land surface model performance than traditionally used methods: r, r2, etc.

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Updated 06 May 2021

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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.

Cite As

K NARENDER REDDY (2021). willmontt_index (https://github.com/KNR8070/willmott_index/releases/tag/1.1.0), GitHub. Retrieved .

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MATLAB Release Compatibility
Created with R2021a
Compatible with any release
Platform Compatibility
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