t.rast.series(1grass) | GRASS GIS User's Manual | t.rast.series(1grass) |
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, aggregation, series, raster, time
t.rast.series
t.rast.series --help
t.rast.series [-tn] input=name
method=string[,string,...]
[quantile=float[,float,...]]
[order=string[,string,...]]
[where=sql_query] output=name[,name,...]
[--overwrite] [--help] [--verbose] [--quiet]
[--ui]
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.
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
r.series, t.create, t.info
Temporal data processing Wiki
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
Available at: t.rast.series source code (history)
Accessed: Sunday Jan 22 07:37:45 2023
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