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MBE (Mean Bias Error)

The Mean Bias Error is usually not used as a measure of the model error as high individual errors in prediction can also produce a low MBE. Mean bias error is primarily used to estimate the average bias in the model and to decide if any steps need to be taken to correct the model bias. Mean Bias Error (MBE) captures the average bias in the prediction. A positive bias or error in a variable (such as wind speed) represents the data from datasets is overestimated and vice versa, whereas for the variable’s direction (such as wind direction) a positive bias represents a clockwise deviation and vice versa. The lower values of errors and considerably higher value of correlation coefficient for the variable and direction are of greater importance.

Mean Bias Error

where Oi is the observation value and Pi is the forecast value.

Related Question: What does bias mean?

Paste 2-columns data here (obs vs. sim). In format of excel, text, etc. Separate it with space:

Mean Bias Error:
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