Accurate estimates of surface solar irradiance (SSI) are essential for solar resource assessments and solar energy forecasts in grid integration and building control applications. SSI estimates for spatially extended regions can be retrieved from geostationary satellites such as Meteosat. Traditional SSI satellite retrievals like Heliosat rely on physical radiative transfer modelling. We introduce the first machine-learning-based satellite retrieval for instantaneous SSI and demonstrate its capability to provide accurate and generalizable SSI estimates across Europe. Our deep learning retrieval provides near real-time SSI estimates based on data-driven emulation of Heliosat and fine-tuning on pyranometer networks. By including SSI from ground stations, our SSI retrieval model can outperform Heliosat accuracy and generalize well to regions with other climates and surface albedos in cloudy conditions (clear-sky index < 0.8). We also show that the SSI retrieved from Heliosat exhibits large biases in mountain regions, and that training and fine-tuning our retrieval models on SSI data from ground stations strongly reduces these biases, outperforming Heliosat. Furthermore, we quantify the relative importance of the Meteosat channels and other predictor variables like solar zenith angle for the accuracy of our deep learning SSI retrieval model in different cloud conditions. We find that in cloudy conditions multiple near-infrared and infrared channels enhance the performance. Our results can facilitate the development of more accurate satellite retrieval models of surface solar irradiance.

翻译：准确的地表太阳辐照度（SSI）估算对于电网整合与建筑控制应用中的太阳能资源评估和太阳能预测至关重要。大范围区域的SSI可通过地球静止卫星（如Meteosat）进行反演获取。传统的SSI卫星反演方法（如Heliosat）依赖于物理辐射传输建模。本文首次提出基于机器学习的瞬时SSI卫星反演方法，并证明其能在欧洲范围内提供准确且可泛化的SSI估算。我们的深度学习反演方法通过数据驱动的Heliosat模拟结合日射强度计网络微调，实现了近实时SSI估算。通过引入地面站点SSI数据，我们的SSI反演模型在精度上超越Heliosat，并在多云条件下（晴空指数<0.8）能良好泛化至具有不同气候特征与地表反照率的区域。研究还发现Heliosat反演的SSI在山区存在显著偏差，而基于地面站点SSI数据对我们反演模型进行训练与微调可大幅降低此类偏差，其表现优于Heliosat。此外，我们量化了Meteosat各通道及太阳天顶角等预测变量在不同云况下对深度学习SSI反演模型精度的相对重要性。研究发现多云条件下多个近红外与红外通道能显著提升模型性能。本研究结果可为开发更高精度的地表太阳辐照度卫星反演模型提供重要参考。