t.rast.series.1grass - Man Page

-t

Do not assign the space time raster dataset start and end time to the output map

-n

Propagate NULLs

--overwrite

Allow output files to overwrite existing files

--help

Print usage summary

--verbose

Verbose module output

--quiet

Quiet module output

--ui

Force launching GUI dialog

input=name [required]

Name of the input space time raster dataset

method=string[,string,...] [required]

Aggregate operation to be performed on the raster maps
Options: average, count, median, mode, minimum, min_raster, maximum, max_raster, stddev, range, sum, variance, diversity, slope, offset, detcoeff, quart1, quart3, perc90, quantile, skewness, kurtosis
Default: average

quantile=float[,float,...]

Quantile to calculate for method=quantile
Options: 0.0-1.0

order=string[,string,...]

Sort the maps by category
Options: id,  name,  creator,  mapset,  creation_time,  modification_time,  start_time,  end_time,  north,  south,  west,  east,  min,  max
Default: start_time

nprocs=integer

Number of threads for parallel computing
Default: 1

memory=memory in MB

Maximum memory to be used (in MB)
Cache size for raster rows
Default: 300

where=sql_query

WHERE conditions of SQL statement without ’where’ keyword used in the temporal GIS framework
Example: start_time > ’2001-01-01 12:30:00’

output=name[,name,...] [required]

Name for output raster map(s)

file_limit=integer

The maximum number of open files allowed for each r.series process
Default: 1000

Here the entire stack of input maps is considered:

t.rast.series input=tempmean_monthly output=tempmean_average method=average

Here the stack of input maps is limited to a certain period of time:

t.rast.series input=tempmean_daily output=tempmean_season method=average \
  where="start_time >= ’2012-06’ and start_time <= ’2012-08’"

By considering only a single month in a multi-annual time series the so-called climatology can be computed. Estimate average temperature for all January maps in the time series:

t.rast.series input=tempmean_monthly \
    method=average output=tempmean_january \
    where="strftime(’%m’, start_time)=’01’"
# equivalently, we can use
t.rast.series input=tempmean_monthly \
    output=tempmean_january method=average \
    where="start_time = datetime(start_time, ’start of year’, ’0 month’)"
# if we want also February and March averages
t.rast.series input=tempmean_monthly \
    output=tempmean_february method=average \
    where="start_time = datetime(start_time, ’start of year’, ’1 month’)"
t.rast.series input=tempmean_monthly \
    output=tempmean_march method=average \
    where="start_time = datetime(start_time, ’start of year’, ’2 month’)"

Generalizing a bit, we can estimate monthly climatologies for all months by means of different methods

for i in `seq -w 1 12` ; do
  for m in average stddev minimum maximum ; do
    t.rast.series input=tempmean_monthly method=${m} output=tempmean_${m}_${i} \
    where="strftime(’%m’, start_time)=’${i}’"
  done
done