The data correlation is effective for mitigating systematic problems in the satellite-derived data (e.g. under/over-estimation of local aerosol loads) especially when the magnitude of the deviation is invariant over the time or has a seasonal periodicity. The accuracy-enhancement methods are capable to adapt satellite-derived DNI and GHI datasets (and derived parameters) to the local climate conditions that cannot be recorded in the original satellite and atmospheric inputs.
Satellite-based Solargis data can be adapted to the project site when at least 12 months of ground-measurements are available. The result of this process is the construction of a multi-year solar dataset with improved accuracy.
For the adaptation of satellite data to the conditions represented by the ground measurements at the project site, two main approaches are taken:
As developers of the full computational chain, in Solargis we have the capacity of adapting the model input data, so both methods can be combined for achieving consistent and accurate results. Other methods only using a statistical approach will achieve not so good results on accuracy.
The adaptation of Solargis input parameters are used for correcting the main sources of discrepancies (such as limitations in aerosol description). Small residual deviations are removed in the next step by a simpler adaptation of the output values. Using this combined method for site-adaptation of satellite data, we are able to keep consistency of GHI, DNI and DIF components.
The data adaptation is important especially when specific situations such as extreme irradiance events are to be correctly represented in the enhanced dataset. These methods have to be used carefully, as inappropriate use for non-systematic deviations or use of less accurate ground data leads to accuracy degradation of the primary satellite-derived dataset.