ccdfstd : computes the standard deviation of any variable

Computes the standard deviation of any variable. This computation is direct and does not required a pre-processing with any of the cdfmoy tools. This is the wrapping around the native cdfstd operator assuming its usage is:

cdfstd [-save] **[-spval0]** **[-nomissincl]** **[-stdopt]**
... list_of_files

CliMAF optional arguments are the ones surrounded with ‘**’.

See also:

  • ccdfstdmoy for also getting the mean value of the field

References :

Provider / contact : climaf at meteo dot fr for the wrapping

Inputs (in the order of CliMAF call):

  • any dataset forming a time-series (but only one)

Mandatory arguments: None

Optional arguments:

  • opt may be used to pass following keys :
    • -spval0 : set missing_value attribute to 0 for all output variables and take care of the input missing_value. This option is usefull if missing_values differ from files to files
    • -nomissincl : with this option, the output std and mean are set to missing value at any gridpoint where the variable contains a missing value for at least one timestep. You should combine with -spval0 if missing values are not 0 in all the input files
    • -stdopt : use a more optimal algorithm to compute std and std is unbiased

Required files: None


  • main output : a netcdf file (variable : IN-var_std, same units than input variables)

Climaf call example:: For more examples which are systematically tested, see

>>> dmld=ds(simulation="PRE6CPLCr2alb", variable="omlmax", period="199807-199810", realm="O") # some dataset, with whatever variable
>>> my_cdfstd=ccdfstd(dmld)
>>> cfile(my_cdfstd) # to compute the standard deviation of variable "omlmax"
>>> my_cdfstd2=ccdfstd(dmld,opt='-full')
>>> cfile(my_cdfstd2)

Implementation: The operator is implemented as a binary using cdftools cdfstd operator.