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Revision as of 12:25, 24 August 2009 by HamishBowman (talk | contribs) (+loop script, linkerizer)
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The speed of r.sun is much higher if r.horizon is used first and the resulting maps are given as input to r.sun. Background: the horizon needs to be computed only one time before, not in every step within r.sun. See the example at the end of the r.sun help page.


Create an artificial surface containing a Gaussian mound:

r.surf.volcano out=gauss method=gaussian kurtosis=1

Overlay some 200m contours to show underlying topography:

r.contour in=gauss out=gauss_200m_contours step=200
d.vect gauss_200m_contours color=white

Set map's color table to highlight detail:

r.colors rad_test.day355.beam col=bcyr -e
d.legend rad_test.day355.beam range=1300,1500

Time step

The following three images demonstrate the effects of using different time step parameters.

r.sun -s elevin="gauss" glob_rad="rad.global.30minT" day=180 step=0.5
Default 30 minute time step over a Gaussian mound.

r.sun -s elevin="gauss" glob_rad="rad.global.15minT" day=180 step=0.25
15 minute time step over a Gaussian mound.

r.sun -s elevin="gauss" glob_rad="rad.global.03minT" day=180 step=0.05
3 minute time step over a Gaussian mound.

The 3 minute time step takes roughly ten times as long to run as the 30 minute timestep.

Seed maps

Using seed maps can greatly speed up processing. This is especially important if you will batch process e.g. for every day of the year.

  • aspin= and slopein= options: create slope and aspect maps with the r.slope.aspect module.
Caution: currently buggy. See trac #498.
Estimated speedup:  ?%.

  • horizon= and horizonstep= options: Pre-calculate horizon shadows by creating a series of horizon rasters with the r.horizon module.
Results not as smooth as without using this option?? See trac #498.
Estimated speedup:  ?%.

  • latin= and longin= options: Pre-calculate latitudes and longitudes for each raster cell.

The following script will create a raster containing latitude as the raster value:

 # create latitude map (WGS84)
 g.region rast=elevation.dem
 r.mapcalc one=1
 r.out.xyz one | \
   cut -f1,2 -d'|' | \
   m.proj -oed --quiet | \
   sed -e 's/[ \t]/|/g' | \
   cut -f1-4 -d'|' | \
   r.in.xyz in=- z=4 out=elevation.lat
 g.remove one

Creating the longitude map is exactly the same but use column 3 of the m.proj output instead of column 4:

 # create longitude map (WGS84)
 g.region rast=elevation.dem
 r.mapcalc one=1
 r.out.xyz one | \
   cut -f1,2 -d'|' | \
   m.proj -oed --quiet | \
   sed -e 's/[ \t]/|/g' | \
   cut -f1-4 -d'|' | \
   r.in.xyz in=- z=3 out=elevation.lon
 g.remove one
Estimated speedup: 1.3% (in one test).
See trac #498.

The above assumes that your projection's ellipsoid is WGS84. If it isn't, use other proj_out= terms with m.proj as required instead of the -o flag.


It can be tedious to set up and run a long series of r.sun simulations for every day of the year. To automate this we can write a small shell script loop:

 for DAY in `seq 1 365` ; do
    echo "Processing day $DAY at `date` ..."

    LINKE="`g.linke_by_day.py $DAY`"

    r.sun -s elevin=elevation.dem day=$DAY lin=$LINKE step=0.05 \
        beam_rad=rad_beam.$DAY diff_rad=rad_diffuse.$DAY \
        refl_rad=rad_reflected.$DAY glob_rad=rad_global.$DAY \
  • g.linke_by_day.py is a small program which will return the Linke coefficient for any day of the year, interpolated from monthly values. You need to edit the script to fill in values appropriate for your study area.
  • If you have a multi-core CPU and you'd like to speed things up, see the OpenMP page for a script implementing a poor map's multi-processing trick.



  • Add support for OpenMP parallelization