Conference Presentation
Reference
Long term CM-SAF satellite global and beam irradiance validation
INEICHEN, Pierre
Abstract
The meteorological satellite images as data source to evaluate the ground irradiance components become the state of the art in the field of solar energy systems. The strongest argument is the high spatial coverage, and the fifteen minutes temporal granularity when using images from MSG. They also have the advantage to provide «real time» data used for example to assess the proper operation of a solar plant. On the other hand, long term ground data are very scarce concerning the beam irradiance. The use of secondary inputs such as polar satellite data and ground information increases significantly the precision of the algorithms, mainly for the beam component. Following a paper from Zelenka concerning the nuggets effect, the interpolation distance to the nearest ground measurement site is limited to 10 to 30 km, depending on the irradiance parameter. This strengths the satellite derived data argument.
INEICHEN, Pierre. Long term CM-SAF satellite global and beam irradiance validation. In:
Satellite Application Facility on Climate Monitoring User Workshop 2014 , Grainau (Germany), 10-12 March, 2014
Available at:
http://archive-ouverte.unige.ch/unige:92900
Disclaimer: layout of this document may differ from the published version.
1 / 1
CM-SAF User Workshop 2014
Long term CM-SAF satellite global and beam irradiance validation
Dr Pierre Ineichen, University of Geneva (pierre.ineichen@unige.ch)
Keywords: satellite derived irradiance; global and beam components; hourly, daily and monthly data; interannual variability; validation
1. Introduction
The meteorological satellite images as data source to evaluate the ground irradiance components become the state of the art in the field of solar energy systems. The strongest argument is the high spatial coverage, and the fifteen minutes temporal granularity when using images from MSG. They also have the advantage to provide «real time» data used for example to assess the proper operation of a solar plant. On the other hand, long term ground data are very scarce concerning the beam irradiance. The use of secondary inputs such as polar satellite data and ground information increases significantly the precision of the algorithms, mainly for the beam component. Following a paper from Zelenka concerning the nuggets effect, the interpolation distance to the nearest ground measurement site is limited to 10 to 30 km, depending on the irradiance parameter. This strengths the satellite derived data argument.
2. Ground data
Data from 18 ground stations are used for the validation, with up to 16 years of continuous measurements; for the validation itself, due to the satellite variability, only data from 2004 to 2011 are used. The data acquired before the reference period are used to evaluate the interannual variability. The climate range covers desert to oceanic, latitude from 20°N to 60°N, and altitudes from sea level to 1580 meters.
High precision instruments (WMO 2008) such as Kipp and Zonen CM10 and Eppley PSP pyranometers, and Eppley NIP pyrheliometers, are used to acquire the data.
3. Satellite data (see DOI: 10.5676/EUM_SAF_CM/CLAAS/V001)
The underlying fundamental assumption of retrieving the surface solar irradiance from satellite observations is that the reflected radiance, as measured by the satellite instrument, is related to the broadband atmospheric transmission.
The calculation of the surface solar irradiance under cloud-free conditions is performed using the clear-sky Mesoscale Atmospheric Global Irradiance Code (MAGIC). Look-up tables have been pre-calculated for several aerosol optical depths and types, 2 sun zenith angles (0 and 60 degree) with fixed values of surface albedo (0.2), integrated water vapor column (15 mm) and ozone (345 DU) using the RTM model libRadtran.
Under cloudy conditions the effective cloud albedo is used to calculate the cloud transmission of the clear sky irradiance. The effective cloud albedo is related to the solar irradiance via the clear sky index. The auxiliary input data is identical to the input data used to calculate the clear-sky surface radiation, i.e., surface albedo, vertically-integrated water vapor and ozone, and aerosol information. In addition to this auxiliary input data, also satellite data is used to derive the surface radiation under cloudy conditions.
4. Quality control and comparison indicators
A stringent quality control, including time stamp of the data, absolute and relative calibration coefficient, long term stability, components coherence etc. is applied on the data.
The usual statistical indicators such as mean bias, root mean square deviation, standard deviation of the bias, correlation coefficient, etc. are used to benchmark the product. We also applied a second order statistic (Kolmogorov-Smirnov) to characterize the frequency distributions.
The comparison is done on an hourly, daily, monthly and yearly basis, on both the global and the beam component. The interannual variability is also studied.
5. Results
The preliminary results show that the model over all the data (102 site-year, 470’000 hourly values, 43’000 daily values, 1’500 monthly values) gives the following results:
• Hourly: negligible bias, sd(Gh) = 16-20%, sd(Bn) = 30-35%
• Daily: negligible bias, sd(Gh) = 8-10%, sd(Bn) = 20-25%
• Monthly negligible bias, sd(Gh) = 3- 5%, sd(Bn) = 9-12%
