DESCRIPTION
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.
t.rast.series is a simple wrapper for the raster module
r.series. It supports a subset of the aggregation methods of
r.series.
NOTES
To avoid problems with too many open files, by default, the maximum
number of open files is set to 1000. If the number of input raster
files exceeds this number, the -z flag will be invoked. Because this
will slow down processing, the user can set a higher limit with the
file_limit parameter. Note that file_limit limit should not exceed the
user-specific limit on open files set by your operating system. See the
Wiki
for more information.
Performance
To enable parallel processing, the user can specify the number of threads to be
used with the nprocs parameter (default 1). The memory parameter
(default 300 MB) can also be provided to determine the size of the buffer in MB for
computation. Both parameters are passed to r.series.
To take advantage of the parallelization, GRASS GIS
needs to be compiled with OpenMP enabled.
EXAMPLES
Estimate the average temperature for the whole time series
Here the entire stack of input maps is considered:
t.rast.series input=tempmean_monthly output=tempmean_average method=average
Estimate the average temperature for a subset of the time series
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'"
Climatology: single month in a multi-annual time series
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
SEE ALSO
r.series,
t.create,
t.info
Temporal data processing Wiki
AUTHOR
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture