MBE (Mean Bias Error)

The Mean Bias Error (MBE) serves as a metric to gauge the average bias present in a model's predictions. While it's not typically employed as the sole measure of model error, as it may not adequately capture high individual errors in prediction, MBE plays a crucial role in identifying and quantifying the average bias within the model's outputs. Positive bias in a variable (e.g., wind speed) indicates an overestimation of data from datasets, while negative bias signifies underestimation. Similarly, for directional variables like wind direction, a positive bias corresponds to a clockwise deviation, and a negative bias indicates counterclockwise deviation. Evaluating MBE alongside other metrics like correlation coefficients helps provide a more comprehensive understanding of model performance. Lower error values and higher correlation coefficients, particularly for directional variables, are indicative of superior model accuracy.

Where O_i is the observation value and P_i is the forecast value.

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MBE (Mean Bias Error)