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}'"


r.series, t.create,

Temporal data processing Wiki


Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

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