the global radiation, calculated with r.sun, seems to be very high in comparison to PVGIS (Photovoltaic Geographical Information System).
- r.sun for 14. Feb: ~2363 Wh/m² per day
- PVGIS: 1330 Wh/m² per day Irradiation on horizontal plane for february on average
Below the detailed steps I did in GRASS and with PVGIS:
1. PVGIS:
a) http://re.jrc.ec.europa.eu/pvgis/apps3/pvest.php -> search for location 'Osnabrueck, Germany' -> choose monthly radiation -> click calculate -> the output table shows 1330 Wh/m² Irradiation on horizontal plane for february.
2. GRASS
a) Take an existing DEM with the resolution of 1m and set all z-values to 68m to have a completely horizontal plane:
GRASS 6.4.0RC6 (GRASS)> r.mapcalc dsxx94c68=dsxx94c@PERMANENT*0+68.00
b) Set Region
GRASS 6.4.0RC6 (GRASS)> g.region rast=dsxx94c68
Maybe the problem is due to a Linke turbidity coeffcient too low (atmosphere too transparent)...
Frank Reekers escribió:
the global radiation, calculated with r.sun, seems to be very high in comparison to PVGIS (Photovoltaic Geographical Information System).
- r.sun for 14. Feb: ~2363 Wh/m² per day
- PVGIS: 1330 Wh/m² per day Irradiation on horizontal plane for february on average
Below the detailed steps I did in GRASS and with PVGIS:
1. PVGIS:
a) http://re.jrc.ec.europa.eu/pvgis/apps3/pvest.php -> search for location 'Osnabrueck, Germany' -> choose monthly radiation -> click calculate -> the output table shows 1330 Wh/m² Irradiation on horizontal plane for february.
2. GRASS
a) Take an existing DEM with the resolution of 1m and set all z-values to 68m to have a completely horizontal plane:
GRASS 6.4.0RC6 (GRASS)> r.mapcalc dsxx94c68=dsxx94c@PERMANENT*0+68.00
b) Set Region
GRASS 6.4.0RC6 (GRASS)> g.region rast=dsxx94c68
Thanks for the hint.
I did the calculations with the default value of the linke Linke turbidity coeffcient, which is 3.0
In the output table of PVGIS the Linke turbidity coeffcient is in february 2.7, so I think this should be okay.
Any other ideas?
Alberto Pettazzi schrieb:
Maybe the problem is due to a Linke turbidity coeffcient too low (atmosphere too transparent)...
Frank Reekers escribió:
the global radiation, calculated with r.sun, seems to be very high in comparison to PVGIS (Photovoltaic Geographical Information System).
- r.sun for 14. Feb: ~2363 Wh/m² per day
- PVGIS: 1330 Wh/m² per day Irradiation on horizontal plane for february on average
Below the detailed steps I did in GRASS and with PVGIS:
1. PVGIS:
a) http://re.jrc.ec.europa.eu/pvgis/apps3/pvest.php -> search for location 'Osnabrueck, Germany' -> choose monthly radiation -> click calculate -> the output table shows 1330 Wh/m² Irradiation on horizontal plane for february.
2. GRASS
a) Take an existing DEM with the resolution of 1m and set all z-values to 68m to have a completely horizontal plane:
GRASS 6.4.0RC6 (GRASS)> r.mapcalc dsxx94c68=dsxx94c@PERMANENT*0+68.00
b) Set Region
GRASS 6.4.0RC6 (GRASS)> g.region rast=dsxx94c68
Hi Frank. Could please help me a bit (...two questions below)
Frank Reekers wrote:
the global radiation, calculated with r.sun, seems to be very high in
comparison to PVGIS (Photovoltaic Geographical Information System).
- r.sun for 14. Feb: ~2363 Wh/m² per day
- PVGIS: 1330 Wh/m² per day Irradiation on horizontal plane for
february on average
Below the detailed steps I did in GRASS and with PVGIS:
1. PVGIS:
a) http://re.jrc.ec.europa.eu/pvgis/apps3/pvest.php -> search for
location 'Osnabrueck, Germany' -> choose monthly radiation -> click
calculate -> the output table shows 1330 Wh/m² Irradiation on horizontal
plane for february.
2. GRASS
a) Take an existing DEM with the resolution of 1m and set all z-values
to 68m to have a completely horizontal plane:
GRASS 6.4.0RC6 (GRASS)> r.mapcalc dsxx94c68=dsxx94c@PERMANENT*0+68.00
What is the projection that defines the Location here?
b) Set Region
GRASS 6.4.0RC6 (GRASS)> g.region rast=dsxx94c68
does anyone have the "definitive" way of converting micro-Einsteins (aka
micro-moles of photons) in the PAR part of the spectrum to watts? I assume
you'd have to use the linke factor to estimate how much energy gets through
into the other parts of the full light spectrum as part of the energy integration.
I can figure out a simple method but I wouldn't trust it for definitively
calibrating the model, or at least am always on the lookout for something
better. Ultimately I'd love to be able to use our Li-COR time-series data
to calibrate the r.sun model, producing a local Linke number from sunny day
data and better cloud diffusion coefficients for cloudy ones (deficit from
modeled clear-sky estimate).
at minimum converting a chunk of that uE PAR data it could tell us which
of the two (or both) is right/wrong.
as for the question about hourly integrations, I'm not sure it is possible
without hacking the code. The best you could do is calc instantaneous flux
at the bottom of each hour and (non-correctly) pretend that value is the
average over the whole hour. or just present the instantaneous value at
the top of each value, document it, and leave it at that.