Calculates and removes the mean of the given dimension(s) at all other dimensions.
Available in version 5.1.1 and later.
function dim_rmvmean_n ( x : numeric, dims [*] : integer ) return_val [dimsizes(x)] : float or double
A variable of numeric type and any dimensionality.dims
The dimension(s) of x on which to calculate and remove the mean. Must be consecutive and monotonically increasing.
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_rmvmean_n function calculates and removes the mean from all elements of the dimensions indicated by dims for each index of the remaining dimensions. Missing values are ignored.
Use dim_rmvmean_n_Wrap if retention of metadata is desired.
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_n(x,0) ; new variable x = dim_rmvmean_n(x,0) ; overwrite with deviationsNote: when operating across the rightmost dimension, it is simpler to use dim_rmvmean.
Let x be a 3-dimensional array with dimension sizes (ntim, nlat, nlon). To remove the means of the "nlon" dimension:
xRmvLon = dim_rmvmean_n (x,2) ; new variable containing deviations (no metadata) xRmvLon = dim_rmvmean_n_Wrap(x, 2) ; with metadata x = dim_rmvmean_n (x,2) ; overwrite with deviationsExample 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:
xRmvTime = dim_rmvmean_n(x,0) xRmvTime = dim_rmvmean_n_Wrap(x,0)Example 4:
Let x be a 4-dimensional array with named dimensions (time, lev, lat, lon) and dimension sizes (ntim, nlev, nlat, nlon). To remove the mean of the time/level dimensions from all lat/lon indices:
xRmv = dim_rmvmean_n(x,(/0,1/)) xRmv = dim_rmvmean_n_Wrap(x,(/0,1/))Example 5:
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.
xRmvJan = dim_rmvmean_n_Wrap(x(0:ntim-1:12,:,:),0) xRmvJuly = dim_rmvmean_n_Wrap(x(6:ntim-1:12,:,:),0)