## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Infrared Observations Only
The problem with the lack of VIIRS IR absorption channels is discussed in the paper [86], in which a method to construct a water vapor and CO2 IR channel for VIIRS with a spatial resolution of 750 m is suggested. Using the CALIPSO/CALIOP V4-20 cloud layer products and MODIS Collection 6.1 cloud-top products as a comparison, ref. [43] evaluates the cloud mask, cloud thermodynamic phase, and cloud-top height based on these constructed channels. The results indicate that each of the above cloud properties shows an improvement with the use of the constructed channel radiances. In particular, the value of the retrieved cloud-top height is comparable to the true cloud-top height measured using CALIPSO/CALIOP. Furthermore, it has been shown that the greatest improvement for the cloud mask occurs in the polar regions. The potential for retrieving the cloud layer altitude, ice thickness, and frozen water path using the IASI and its successor IASI-NG is studied by [87]. For this purpose, cloud radiances were simulated with RTTOV and ECMWF profiles, and the amount and spectral distribution of ice cloud properties were determined using an analysis based on Shannon formalism. The results show a convergence rate of up to 95% for the requested products and expected errors that decrease with cloud opacity until signal saturation is reached. The cloud retrieval algorithms analyzed so far generally provide reasonable estimates of cloud microphysical parameters under most conditions; however sun-glint areas remain a challenging problem. In the case where sunlight reflects off the water surface at the same angle as a satellite sensor views it, the sun glint can introduce a high degree of uncertainty in the retrieval of COT and CER [88]. Various approaches have been developed to model the sun glint radiance reflected by the sea surface, such as, for example, the Cox-Munk approach [89], which is based on statistical data [90]. The method described in [91] is also based on this approach, which yields good results in calculating the direct sun-glint radiance used in ocean color atmospheric correction. The problem of eliminating contamination from sun glint was also addressed by [92] through the development of an algorithm that uses the simultaneous determination of aerosols and wind speed. A new frontier of innovation in the field of remote sensing has been provided by deep learning techniques, improvements in computational resources, and the growing quantity of satellite observations. Using convolutional neural networks, it is possible to consider statistical relationships between input variables and targets, capture spatial variations in input features, and reduce the computational load as well. Ref. [93] describes the retrieval of cloud-top height from MODIS with a neural network approach. The training database consists of cloud-top layer pressure data from the CALIOP dataset and brightness temperatures at 11 and 12 \(\upmu\)m. The results show a mean absolute error (MAE) at least 32% (or 623 m) lower than the two operational cloud-top height reference algorithms (the MODIS Collection 6 Level 2 height product and the cloud-top temperature and height algorithm in the 2014 version of the NWC SAF--the EUMETSAT Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting--PPS (Polar Platform System). The validation, carried out against both CALIOP and Cloud Profiling Radar (CPR, CloudSat), showed at least 32% (or 623 m) and 25% (or 430 m) MAE reductions, respectively. SEVIRI measurements are the reference for the work on cirrus cloud detection and the retrieval of ice optical thickness, ice water path, and cloud-top height, developed by [94]. This paper describes the Cirrus Properties from SEVIRI (CiPS) NN algorithm, trained with SEVIRI infrared observations, CALIOP backscatter products, ECMWF surface temperatures, and other auxiliary data.