ABSTRACT We present a validation study of direct normal irradiance (DNI) estimates from HelioMont with ground-based measurements from two European sites for the year 2015. The HelioMont algorithm infers irradiance with data from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument as the primary source of information on clouds, and data from models or reanalysis for other influential input parameters. The validation sites are the Plataforma Solar de Almería (PSA), a solar power research facility in Southern Spain characterized by arid conditions and the Swiss Baseline Surface Radiation Network (BSRN) site of Payerne, characterized by a much more frequent cloud coverage. Our analysis shows the importance of separately evaluating the quality of (1) the clear-sky irradiance computation and (2) the determination of the cloud effect. We also specifically investigate the cloud modification factor (CMF) using a validation CMF derived from ground-based data, giving us more insight into event-by-event agreement between HelioMont estimates and measured irradiances. The clear-sky HelioMont DNI uncertainty is mainly influenced by the aerosol optical depth (AOD) input data. Using the original AOD input (a 2008 climatology based on data from the Aerosol Comparisons between Observations and Models project) leads to large negative biases of 115 W m−2 to 145 W m−2. Using AOD from the Copernicus Atmosphere Monitoring Service (CAMS) allows reducing these biases to 15 W m−2 to 25 W m−2 (2% to 3%) with a dispersion of ±12% to ±15%, which is the HelioMont clear-sky DNI expanded uncertainty when using CAMS AOD. Using ground-measured AOD reduces this uncertainty to ±5% to ±6.5%, which is probably the limit of what is achievable with HelioMont. For all-sky comparisons, mean biases were between about −5 W m−2 and 55 W m−2 (depending on AOD input and station), while the root-mean-square deviation (RMSD) was between about 175 W m−2 and 195 W m−2. Our validation method yielded correlation between HelioMont and validation CMF between 0.79 and 0.92 (Pearson’s correlation coefficient r), while RMSD was between 0.18 and 0.24. The computation of the cloud effect is the part of HelioMont that is the main source of uncertainty. Systematic errors were identified (underestimation of the number of near-zero DNI and overestimation of the number of clear-sky cases) and solving them may lead to substantial improvement.

中文翻译：

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西班牙南部和瑞士卫星衍生的太阳直接辐照度的准确性

摘要 我们提出了一项对 HelioMont 的直接法向辐照度 (DNI) 估计值的验证研究，其中包括 2015 年来自两个欧洲站点的地面测量值。 HelioMont 算法根据 Meteosat 第二代旋转增强型可见光和红外成像仪 (SEVIRI) 的数据推断辐照度) 仪器作为云信息的主要来源，以及来自模型的数据或其他有影响的输入参数的再分析。验证站点是 Plataforma Solar de Almería (PSA)，这是西班牙南部以干旱条件为特征的太阳能研究设施，以及 Payerne 的瑞士基线地表辐射网络 (BSRN) 站点，其特征是云覆盖更频繁。我们的分析显示了分别评估（1）晴天辐照度计算和（2）云效应确定质量的重要性。我们还使用源自地面数据的验证 CMF 专门研究了云修正因子 (CMF)，让我们更深入地了解 HelioMont 估计和测量辐照度之间的逐个事件一致性。晴空 HelioMont DNI 的不确定性主要受气溶胶光学深度 (AOD) 输入数据的影响。使用原始 AOD 输入（2008 年基于观测和模型之间气溶胶比较项目数据的气候学）会导致 115 W m-2 至 145 W m-2 的大负偏差。使用来自哥白尼大气监测服务 (CAMS) 的 AOD 可以将这些偏差降低到 15 W m-2 到 25 W m-2（2% 到 3%），离散度为 ±12% 到 ±15%，这是 HelioMont 晴空 DNI 在使用 CAMS AOD 时扩展的不确定性。使用地面测量的 AOD 将这种不确定性降低到 ±5% 到 ±6.5%，这可能是 HelioMont 可实现的极限。对于全天比较，平均偏差在大约 -5 W m-2 和 55 W m-2 之间（取决于 AOD 输入和站点），而均方根偏差 (RMSD) 在大约 175 W m- 之间2 和 195 W m−2。我们的验证方法得出 HelioMont 和验证 CMF 之间的相关性在 0.79 和 0.92 之间（Pearson 相关系数 r），而 RMSD 在 0.18 和 0.24 之间。云效应的计算是 HelioMont 的部分，它是不确定性的主要来源。