Abstract The short-term (0–3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of ‘ramps’ that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simply as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ∼10 W/m2.

中文翻译：

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通过卫星/模型耦合进行短期太阳辐照度预测

摘要 用于可再生能源生产的太阳能日照的短期（0-3 小时）预测是一个非常适合基于卫星的技术的问题。地球静止平台上的仪器的空间、光谱、时间和辐射分辨率使这些卫星能够描述当前的云空间分布和光学特性。这些特性与地表下涌太阳辐照度的瞬态特性直接相关，这些特性以“斜坡”的形式出现，对空间分布式太阳能发电场网络中的能源负载平衡构成了核心挑战。云场的短期演变可以近似为一阶简单的平移，但必须注意平流的处理方式和影响分配的位置。在这项研究中，我们描述了地球同步卫星观测如何与操作云掩蔽和反演算法、来自数值天气预报 (NWP) 的风场数据以及辐射传输计算结合使用，以生成太阳能发电应用中太阳日照的短期预测。该方案利用检索到的云属性将像素分组为连续的云对象，使用四维（空间 + 时间）模型风场预测其未来位置，选择与每个云组的云高度属性相对应的转向级别。与这些云相关的阴影针对传感器观察视差位移进行调整，并结合太阳几何和地形高度来确定云阴影的实际位置。对于中纬度和高太阳天顶角的中/高层云，来自这些几何考虑的组合位移是不可忽略的。云信息用于初始化辐射传输模型，该模型计算阴影位置和中间晴空区域的直接和漫射天空太阳日照。在这里，我们描述了算法的公式并验证了其针对表面辐射 (SURFRAD; Augustine et al., 2000, 2005) 网络观测的性能。典型的误差范围从 8.5% 到 17.2%，具体取决于云区的复杂性，并且在所有条件下，运行演示比基于持续性的全球水平辐照度 (GHI) 预测高出约 10 W/m2。云信息用于初始化辐射传输模型，该模型计算阴影位置和中间晴空区域的直接和漫射天空太阳日照。在这里，我们描述了算法的公式，并验证了其针对表面辐射 (SURFRAD; Augustine et al., 2000, 2005) 网络观测的性能。典型的误差范围从 8.5% 到 17.2%，具体取决于云区的复杂性，并且在所有条件下，运行演示比基于持续性的全球水平辐照度 (GHI) 预测高出约 10 W/m2。云信息用于初始化辐射传输模型，该模型计算阴影位置和中间晴空区域的直接和漫射天空太阳日照。在这里，我们描述了算法的公式并验证了其针对表面辐射 (SURFRAD; Augustine et al., 2000, 2005) 网络观测的性能。典型的误差范围从 8.5% 到 17.2%，具体取决于云区的复杂性，并且在所有条件下，操作演示比基于持续性的全球水平辐照度 (GHI) 预测高出 10 W/m2。