t.rast.series.1grass man page

t.rast.series — Performs different aggregation algorithms from r.series on all or a subset of raster maps in a space time raster dataset.


temporal, series, raster, time


t.rast.series --help
t.rast.series [-tn] input=name method=string [order=string[,string,...]] [where=sql_query] output=name [--overwrite] [--help] [--verbose] [--quiet] [--ui]


Do not assign the space time raster dataset start and end time to the output map
Propagate NULLs
Allow output files to overwrite existing files
Print usage summary
Verbose module output
Quiet module output
Force launching GUI dialog


input=name [required]
Name of the input space time raster dataset
method=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
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
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 [required]
Name for output raster map


t.rast.series is a simple wrapper for the raster module r.series. It supports a subset of the aggregation methods of r.series.

The input of this module is a single space time raster dataset, the output is a single raster map layer. A subset of the input space time raster dataset can be selected using the where option. The sorting of the raster map layer can be set using the order option. Be aware that the order of the maps can significantly influence the result of the aggregation (e.g.: slope). By default the maps are ordered by start_time.


Estimate average temperature for the whole time series

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

Estimate average temperature for all January maps in the time series, the so-called climatology

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}’"

See Also

r.series, t.create, t.info

Temporal data processing Wiki


Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

Last changed: $Date: 2016-01-13 00:30:14 +0100 (Wed, 13 Jan 2016) $

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