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# dim_rmvmean_Wrap

Calculates and removes the mean of the (rightmost) dimension at all other dimensions and retains metadata.

## Prototype

```load "\$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"

function dim_rmvmean_Wrap (
x  : numeric
)

return_val [dimsizes(x)] :  float or double
```

## Arguments

x

A variable of numeric type and any dimensionality.

## Return value

The output is of type double if the input is double, and float otherwise.

The dimensionality is the same as the input dimensionality.

## Description

The dim_rmvmean function calculates and removes the mean from all elements of the n-1th (rightmost) dimension for each index of the dimensions 0...n-2 and retains metadata. A wrapper function. Missing values are ignored.

Use dim_rmvmean_n_Wrap if you want to specify which dimensions to do the calculation across.

## Examples

Example 1:

Let x be a 1-dimensional array: (a) Create a new variable, xNew, that contains just the deviations from the mean; (b) replace the variable x with the deviations.

```  xNew = dim_rmvmean_Wrap(x)      ; new variable
x    = dim_rmvmean_Wrap(x)      ; overwrite with deviations
```
Example 2:

Let x be a 3-dimensional array with dimension sizes (ntim, nlat, nlon). To remove the means of the "nlon" dimension:

```   xRmvLon = dim_rmvmean (x)         ; new variable containing deviations (no metadata)
xRmvLon = dim_rmvmean_Wrap( x )    ; with metadata
x       = dim_rmvmean (x)         ; overwrite with deviations
```
Example 3:

Let x be a 3-dimensional array with named dimensions (time, lat, lon) and dimension sizes (ntim, nlat, nlon). To remove the mean of the time dimension from all lat/lon indices, use NCL's Named Subscripting to reorder the input array such that "time" is the rightmost dimension.

```   xRmvTime = dim_rmvmean(x(lat|:, lon|:, time|:))
xRmvTime = dim_rmvmean_Wrap(x(lat|:, lon|:, time|:))
xRmvTime = dim_rmvmean_n_Wrap(x,0)     ; no reordering needed
```
Note: in V5.1.1, you will be able to use dim_rmvmean_n_Wrap to avoid having to reorder your data.

Example 4:

Let x be as in Example 3 and let x contain monthly means for (say) 10 years of data (ntim=120). Monthly anomalies for each month could be calculated using array subscripting and Named Subscripting to reorder the input array such that "time" is the rightmost dimension.

```   xRmvJan  = dim_rmvmean_Wrap(x(lat|:, lon|:, time|0:ntim-1:12))
xRmvJuly = dim_rmvmean_Wrap(x(lat|:, lon|:, time|6:ntim-1:12))
```