## 1 Introduction  
Among the many factors influencing climate change, clouds occupy a first-order position due to the high degree of uncertainty they bring, as stated by the Intergovernmental Panel on Climate Change (IPCC) in the sixth Assessment Report (AR6) [1]. Furthermore, understanding clouds and climate circulation and their implications for forecasting extreme meteorological events is included among the World Climate Research Program's grand challenges ([[http://wcrp-climate.org/grand-challenges](http://wcrp-climate.org/grand-challenges)]([http://wcrp-climate.org/grand-challenges](http://wcrp-climate.org/grand-challenges)) (accessed on 18 January 2025)) to be addressed in the next decade. In addition, given the key role of clouds in moisture redistribution and the hydrological cycle, advances in the field of atmospheric science cannot occur without real progress in understanding cloud-scale microphysical variables. For these reasons, numerous efforts have been made in recent years to deepen the knowledge of global cloud distribution and develop and improve the accuracy of methodologies that estimate key microphysical cloud variables. Although satellite observations currently offer a complete view of the entire Earth's surface for both the frequency and geographic distribution of the observations, as well as for their spectral range (from VIS to MW), and despite the high quality level achieved in the field of radiative transfer modeling, the accuracy in retrieving key cloud microphysical variables is not always satisfactory. The results of the most recent works seem to indicate that a significant contribution in this sense can come from the synergic use of information from different frequencies. However, today, retrieval products typically come from separate observations at microwave (MW) and infrared (IR) frequencies, which are combined only later, as illustrated in Figure 1. In this way, the synergy potential between these observations is not fully exploited. Conversely, algorithms should simultaneously/hierarchically combine all complementary information derived from different spectral ranges, thus achieving more accurate retrievals than independent retrievals combined a posteriori. In fact, MW is complementary to IR observations, especially in cloudy situations. This is due to relatively low cloud opacity and, therefore, a high degree of penetration through thick clouds at MW frequencies and higher sensitivity but less ability to penetrate clouds at IR and visible (VIS) frequencies. This makes MW, IR, and VIS observations ideal candidates for implementing a truly synergic approach. This complementarity has already led to fruitful synergies in various fields of meteorological science, as shown, for example, in [2], where the synergy of IR and VIS is exploited to estimate rainfall classes at a high spatial and temporal resolution, or in [3], where a combined MW-IR algorithm for convective precipitation estimation is described. Scientific papers in satellite cloud retrieval using IR only, MW only or IR-MW combined data (source: Scopus)  
Figure 1: Number of scientific papers per year focusing on cloud parameter retrievals using only IR (red), only MW (yellow), or combined IR-MW (green) satellite passive observations (source: Scopus, [[https://www.scopus.com/search/form.uri?display=advanced](https://www.scopus.com/search/form.uri?display=advanced)]([https://www.scopus.com/search/form.uri?display=advanced](https://www.scopus.com/search/form.uri?display=advanced)) (accessed on 27 November 2024)). To gain insight into the benefits of a synergic approach and the challenges to be addressed, a deeper look at the current state of the art seems needed in order to also understand what has already been done in this direction and how studies have customized and adapted synergistic techniques to fit the remote sensing context.