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GCM Options
GCM Options
CPT always interpolates from the X data to the Y grid or station locations rather than vice versa. The interpolation options
provide alternative means of performing the interpolation. The interpolate option will use a linear distance-weighted
interpolation of the four X values surrounding each Y grid / station.
The GCM data can be corrected for systematic errors using the model climatology options. The following options are available:
- No correction: the raw model data are used, although an attempt will be made to convert the X to the same units as
the Y data (see further details on the cpt:units tag);
- Correct mean biases: The mean of the GCM are forced to equal the mean of the Y data, although the correction is
performed in cross-validated mode, and so the mean bias may not be exactly zero;
- Correct mean and variance biases: The mean and variance of the GCM are forced to equal the corresponding values for
the Y data, although the correction is performed in cross-validated mode, and so the mean bias and variance ratio may not be
exactly zero;
- Correct for skill: A simple linear regression model is used to correct the GCM data. The mean bias will typically
be close to zero (although not exactly zero, partly because of the cross-validation), but the variance ratio will tend towards
zero as the skill reduces, and will generally be less than one.
The model combination option is applied when the X file contains more than one model (see further details on the
cpt:model tag). The following options are available:
- Average uncalibrated: any corrections for systematic errors specified in the Model climatology panel are first
applied to each model individually, and then a simple average across the models is calculated;
- Average calibrated: any corrections for systematic errors specified in the Model climatology panel are first
applied to each model individually, a simple average across the corrected models is calculated, and mean and variance
corrections to this model are then calculated (see the discussion above on mean and variance biases);
- Average recalibrated: any corrections for systematic errors specified in the Model climatology panel are first
applied to each model individually, a simple average across the corrected models is calculated, and then this model average
is corrected for skill (see the discussion above on Correct for skill);
- Skill weighted average: a multiple regression model is used to combine the models;
- Best model by location: the model with the highest correlation with observed data is used. A different model may be
selected for each location;
- Best overall model: only the model with the highest goodness index is used.
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