## 4 Discussion
In Asia, China and Japan are increasing their camera monitoring capacity, although not at the same level as the United States and Australia. In Europe, countries such as Spain have also developed numerous studies using all-sky cameras, indicating greater coverage than initially reported. Coverage remains uneven globally, with limited representation in Africa and parts of South America, suggesting the need for international cooperation for a more equitable distribution of this advanced technology. Throughout the review of the various techniques and associated measuring instruments, it has been emphasized that characterizing the complexity of the atmosphere as accurately as possible requires a significant amount of data. Regarding the validation of results in different studies within this context, it is important to consider the impact that the validation period. As reported in the literature, the validation period may have an impact on the methods and outcomes. For instance, in techniques involving large amounts of data, such as numerical models or deep learning methods, the results' validation period is presupposed to be optimal. Conversely, in the image processing models, for example, those conducted via satellite or ground-based cameras, the validation must reflect all possible weather scenarios as far out as possible. This process means that if the validation period is short for any reason, the value of the conclusions may be unrealistic. Apart from the spatial-temporal resolution differences previously discussed between all-sky cameras and satellites, it is worth noting that the resolution of geostationary observations from satellites decreases at higher latitudes due to the high satellite zenith angle. This causes the estimation of global horizontal irradiance (GHI) to become increasingly challenging as the pixel size is stretched by the curvature of the Earth. This limitation is demonstrated in conjunction with the high variability of sunshine hours at high latitudesin research such as that of [61] in Finland. Another remarkable constraint in the satellite image processing is the albedo of snowy surfaces, which is like certain clouds and can cause confusion in the irradiance images. Logically, the image assessment from the Earth's surface also has associated critical circumstances that are not present in the satellite space situation. Upon reviewing the studies that employ all-sky cameras in Section 3, it is apparent that those which integrated cloud classification in their research faced certain challenges. This is especially true when dealing with conditions of low or very thin clouds and in skies with a high aerosol concentration. These types of circumstances induced confusion between the pixels corresponding to the sky and those corresponding to the clouds. To provide a clear and concise overview, in Table 1, we present an analysis of the sensors and methods summarizing their strengths and weaknesses:  
This review has identified several emerging trends and novel approaches in solar energy forecasting that hold promise for improving forecast accuracy and reliability. One significant trend is the increasing use of artificial intelligence and machine learning techniques, which can analyze large datasets and identify complex patterns that traditional methods might miss, leading to more accurate and adaptive forecasts. Additionally, the development of hybrid models, which combine various forecasting methods such as