t.rast.series - GRASS GIS manual

NAME

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

KEYWORDS

temporal, aggregation, series, raster, time

SYNOPSIS

t.rast.series

t.rast.series --help

t.rast.series [-tn] input=name method=string[,string,...] [quantile=float[,float,...]] [order=string[,string,...]] [nprocs=integer] [memory=memory in MB] [where=sql_query] output=name[,name,...] [file_limit=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-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

Parameters:

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

DESCRIPTION
NOTES
Performance
EXAMPLES
SEE ALSO
AUTHOR

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

AUTHOR

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

SOURCE CODE

Available at: t.rast.series source code (history)

Latest change: Thursday Jan 26 14:10:26 2023 in commit: cdd84c130cea04b204479e2efdc75c742efc4843