## 2 Review and Evaluation of Solar Irradiance Forecasting Sensors
### Satellites  
In contrast to the NWP models discussed earlier, image processing enables substantially shorter-term horizon forecasts. Specifically, satellite imagery can be used for cloud detection and characterization to predict GHI with an accuracy of up to 6 h in advance [58].  
One of the primary advantages of using satellites for data collection is their ability to capture large areas simultaneously, providing information on the spatial distribution of data. This allows for a more accurate representation of the spatial evolution of the data. With high spatial and temporal resolution, satellite imagery is an excellent data source for forecasting events that will occur several hours in advance. In addition to the previous section's studies, several research projects have utilized satellite weather measurements because of their higher spatial resolution in comparison to ground-based sensors. These measurements are used as an initial condition for NWP models [59; 60; 61] or are directly used for forecasting with shorter horizons than the models. For instance, one study [62] attained a higher resolution than ground-based measurements by using satellite radiation data, covering an area of 462 \(\times\) 462 km with an approximate resolution of 11 \(\times\) 11 km, represented by 1764 gridded cells, which is equivalent to 1764 GHI sensors.  
Several publications have achieved higher spatial resolution for solar forecasting by utilizing satellite weather measurements, surpassing the resolution obtained with ground-based measurement stations. For example, one study [63] used MODIS spectroradiometer data from the TERRA satellite to derive land surface temperature (LST) and design an accurate predictor for spatial horizons at a regional scale in Queensland, Australia. Another study [64] utilized GHI measurements based on satellite data to test the predictive accuracy of long short-term memory (LSTM) models at 21 locations, 16 of which were in Europe and 5 in the USA. Additionally, another publication [65] predicted incident solar radiation (ISR) through the use of NASA's satellite spectroradiometer input data, including land surface temperature, cloud top pressure, cloud height, ozone, and water vapor, among others.  
However, for the cloud monitoring provided by the imagery, much solar forecasting research relies on sky captures from high-resolution satellite imagery. For example, Ref. [66] proposes cloud motion estimations derived from Meteosat-9 cloud map images. Alternatively, Ref. [67] combines MVIRI (Meteosat Visible and InfraRed Imager) images with Meteosat satellite imagery (via the Heliosat model) to obtain the albedo calculations and predict the effect of clouds on insolation. Furthermore, Ref. [66] suggests two methods for extracting cloud motion vectors of daytime Meteosat-10 satellite imagery. Ref. [58], in contrast, utilizes the Himawari-7 satellite of the Japan Meteorological Agency (JMA) to forecast the cloud cover index by means of self-organizing maps (SOMs). Instead, Ref. [68] provides one of the few high-latitude predictive studies that have been conducted by using Meteosat Second Generation (MSG) Meteosat-11 imagery for forecasts of up to 4 h. Finally, for readers interested in using geostationary satellites for cloud analysis, there is a wealth of research available in this area, including a comprehensive review in one study [69].  
Finally, regarding the geostationary satellite application, there are combinations of hybrid systems, less commonly used but equally valid as the ones mentioned above. For example, in Ref. [70], a generalized model for forecasting solar irradiance from 25 locations is explained by ground-based data extrapolated from a single location by means of satellite measurements and weather forecasts. Other studies have also investigated the combination of ground-based measurements, satellite measurements, and numerical models [35; 61; 71].