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The following definitions should be used
Mean error 

\[ M = \frac{1}{M_w} \sum_{i=1}^n w_i (x_f - x_v)_i \]

where the sum of the weights

\[ M_w = \sum_{i=1}^n w_i \]


Root mean square (rms) error

\[ rms = \sqrt {\frac{1}{M_w} \sum_{i=1}^n w_i (x_f - x_v)_i^2 } \]


Correlation coefficient between forecast and analysis anomalies

\[ r = \frac{\sum_{i=1}^n w_i (x_f-x_c-M_{f,c})_i (x_v-x_c-M_{v,c})_i}{\sqrt{\left(\sum_{i=1}^n w_i (x_f-x_c-M_{f,c})_i^2 \right) \left(\sum_{i=1}^n w_i (x_v-x_c-M_{v,c})_i^2 \right) }} \]

rms vector wind error

\[ rms = \sqrt {\frac{1}{M_w} \sum_{i=1}^n w_i (\vec{V}_f - \vec{V}_v)_i^2 } \]

Mean absolute error

\[ MAE = \frac{1}{M_w} \sum_{i=1}^n w_i | x_f - x_v |_i \]

rms anomaly

\[ rmsa = \sqrt {\frac{1}{M_w} \sum_{i=1}^n w_i (x - x_c)_i^2 } \]

standard deviation of field 

\[ sd = \sqrt {\frac{1}{S_w} \sum_{i=1}^n w_i (x - M_x)_i^2 } \]

where

\[ M_x = \frac{1}{M_w} \sum_{i=1}^n w_i x_i \]

S1 score

\[ S_1 = 100 \frac{\sum_{i=1}^n w_i (e_g)_i}{\sum_{i=1}^n w_i (G_L)_i} \]


Where: \( x_f \)

\( x_v \)

\( x_c \)

\( M_{f,c} \)

\( M_{v,c} \)

\( \vec{V}_f \)

\( \vec{V}_v \)

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