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太阳能资源历史数据库提供了详尽气候统计，有助于理解任何特定站点的太阳能资源。在多数地区，项目站点附近并不存在可用地面监测站。Solargis卫星模型提供了一个稳定的、成本效益高、覆盖全面的多年数据周期，且可用于太阳能资源评估。

另一方面，相比现场气象监测数据，从卫星气象模型中获取的气象参数有着更低的空间和时间分辨率。所以，建模参数展现的是周边的平均特定气候模式，而不是精确的局部小气候条件。

通常来说，大型光伏项目开发站点处都会配备一个气象站。配备太阳能监测站有着战略优势，可在地方校准和验证辐射量模型，并为决策人和投资人提供高质数据和信息。

运行地面监控项目、并将地面监测数据与卫星数据相结合，从长远看，有助于保持项目站点处太阳能资源的低误差度：

• 在选址设计阶段，监测项目站点数据的主要目的在于记录准确的当地气象特性，且将这些数据用于卫星模型校准过程，以便降低长期时间序列和模糊预估的误差度。
• 在电站运行阶段，现场监测数据极大地影响了是否能准确监测电站性能和排查故障。所以，应将卫星时间序列视为一个独立信息源，以便进行质量管理和优化地面监测数据。

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:

• Adaptation of satellite-based GHI and DNI values. Using this method the bias (systematic deviation) is corrected together with fitting the cumulative distribution functions.
• Adaptation of the input parameters and data used in the solar radiation model. More complex parameters, such as Aerosol Optical Depth and/or Cloud Index are adjusted using this approach.

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.

在实施地面校准时，第一步便是进行详尽的数据清理和质量控制。此过程基于SERI QC，BSRN和其他内部方法进行。之后还需要进行时间聚合、协调和相关认证。

判断是否能对卫星数据进行地面校准，主要由以下几个因素决定：

• 传感器质量。建议采用最优类气象仪器监测GHI（二级辐照仪和一级辐照仪）。此外，还可采用RSR（旋转遮光带辐射仪），但GHI和DNI监测值误差度更高。采用多个仪器（最好是GHI、DIF、DNI每个分量都配备一个仪器）会提高整个过程的准确度和可靠性。
• 监测数据质量。主要由常规维护、清理和校准所决定。一系列的质量控制措施都要进行评估。只有提前通过质量控制的数据才能用于地面校准过程中。
• 地面监测数据的覆盖范围。最理想的情况是，采用覆盖了12个月的高质地面监测数据。根据实际情况，也可考虑采用较短覆盖时长（至少9个月）的监测数据。然而，此类数据可能无法覆盖全部季节变率。如果采用了覆盖时长更短（如3到6个月）的数据，可能会错误呈现Solargis长期历史数据和当地监测信息之间的关系。