Daily statistics

This module computes statistical values over timesteps of the same day. Depending on the chosen operator the minimum, maximum, range, sum, average, variance or standard deviation of timesteps of the same day is written to outfile. The time of outfile is determined by the time in the middle of all contributing timesteps of infile. This can be change with the CDO option –timestat_date <first|middle|last>.

Usage

cdo_dayavg(ifile, complete_only = NULL, ofile = NULL)

cdo_daymax(ifile, complete_only = NULL, ofile = NULL)

cdo_daymean(ifile, complete_only = NULL, ofile = NULL)

cdo_daymin(ifile, complete_only = NULL, ofile = NULL)

cdo_dayrange(ifile, complete_only = NULL, ofile = NULL)

cdo_daystd(ifile, complete_only = NULL, ofile = NULL)

cdo_daystd1(ifile, complete_only = NULL, ofile = NULL)

cdo_daysum(ifile, complete_only = NULL, ofile = NULL)

cdo_dayvar(ifile, complete_only = NULL, ofile = NULL)

cdo_dayvar1(ifile, complete_only = NULL, ofile = NULL)

Arguments

ifile

String with the path to the input file.

complete_only

BOOL - Process the last day only if it is complete

ofile

String with the path to the output file.

Value

Operators that output one or more files return a character vector to the output files.

Operators that output an indefinite number of files return a string with the basename of the files.

Operatos that don't return filenames return a character vector with the string output.

Details

daymin    Daily minimum
          For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = min\{i(t',x), t_1<t'<=t_n\}
daymax    Daily maximum
          For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = max\{i(t',x), t_1<t'<=t_n\}
dayrange  Daily range
          For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = range\{i(t',x), t_1<t'<=t_n\}
daysum    Daily sum
          For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = sum\{i(t',x), t_1<t'<=t_n\}
daymean   Daily mean
          For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = mean\{i(t',x), t_1<t'<=t_n\}
dayavg    Daily average
          For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = avg\{i(t',x), t_1<t'<=t_n\}
daystd    Daily standard deviation
          Normalize by n. For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = std\{i(t',x), t_1<t'<=t_n\}
daystd1   Daily standard deviation (n-1)
          Normalize by (n-1). For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = std1\{i(t',x), t_1<t'<=t_n\}
dayvar    Daily variance
          Normalize by n. For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = var\{i(t',x), t_1<t'<=t_n\}
dayvar1   Daily variance (n-1)
          Normalize by (n-1). For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is:

          o(t,x) = var1\{i(t',x), t_1<t'<=t_n\}