Calculates standardized anomalies of the rightmost dimension at all other dimensions and retains metadata.
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" function dim_standardize_Wrap ( x : numeric, opt : integer ) return_val [dimsizes(x)] : float or double
A variable of numeric type and any dimensionality.opt
Setting opt=1 means to use the population standard deviation (i.e., divide by the number of non-missing values [N]). Otherwise, the sample standard deviation is used for normalization (i.e., divide by [N-1]).
The output is of type double if the input is double, and float otherwise.
The dimensionality is the same as the input dimensionality.
The dim_standardize function calculates and removes the mean from all elements of the n-1th (rightmost) dimension and retains metadata. It then standardizes the rightmost dimension by dividing the resulting deviations from the mean by the standard deviation of the rightmost dimension. These values are often called standardized anomalies. This is repeated for each index of the dimensions 0...n-2. A wrapper function. Missing values are ignored.
Use dim_standardize_n_Wrap if you want to specify which dimensions to do the calculation on.
Example 1: Let x be a 1-dimensional array: (a) Create a new variable, xNew, that contains standardized values; (b) replace the variable x with the standardized values. Use the population standard deviation.
xNew = dim_standardize_Wrap(x, 1) ; new variable with meta data x = dim_standardize_Wrap(x, 1) ; overwrite with standardized anomalies x@long_name = x@long_name +": standardized anomalies"Example 2: Let x be a 3-dimensional array with dimension sizes (ntim, nlat, nlon). Standardize values of the rightmost (here, "nlon") dimension. Use the sample standard deviation (opt=0).
xLon = dim_standardize_Wrap( x,0 ) ; with metadata x = dim_standardize (x,0) ; overwrite with deviationsExample 3: Let x be a 3-dimensional array with named dimensions (time, lat, lon) and dimension sizes (ntim, nlat, nlon). Standardize the values of the time dimension at all lat/lon indices, use NCL's Named Subscripting to reorder the input array such that "time" is the rightmost dimension.
Note: in V5.1.1, you will be able to use dim_standardize_n_Wrap to avoid having to reorder your data.
xTime = dim_standardize(x(lat|:, lon|:, time|:), 0) ; no metadata xTime = dim_standardize_Wrap(x(lat|:, lon|:, time|:), 0) ; has metadata xTime = dim_standardize_n_Wrap(x, 0, 0) ; w/metadata, no reordering neededExample 4: Let x be as in Example 3 and let x contain monthly means for (say) 10 years of data (ntim=120). Standardized 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. (opt=0 or 1)
xJan = dim_standardize_Wrap(x(lat|:, lon|:, time|0:ntim-1:12), opt) xJuly = dim_standardize_Wrap(x(lat|:, lon|:, time|6:ntim-1:12), opt)