## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Infrared Observations Only
A performance analysis of CiPS shows that the algorithm detects 71 and 95% of all cirrus clouds retrieved via CALIOP with an optical thickness of 0.1 and 1.0, respectively, and correctly classifies 96% of cirrus-free pixels. The cloud-top height retrieved via CiPS has a mean absolute percentage error of 10%, whereas, for the ice optical thickness, the mean absolute percentage error is 50%. Similar results are obtained for the ice water path. An approach based on random forest (RF) machine learning techniques was tested by [95] for cloud mask and cloud thermodynamic-phase detection. For this purpose, two models were developed, using spectral observations from VIIRS on board the SNPP, and trained based on CALIOP data. The RF daytime model was evaluated over all surface types (TPR equal to 0.93 and higher, and FPR equal to 0.07 and lower), with the result that it performs the best among all models considered, including VIIRS CLDMSK and MODIS MYD35 products. Machine learning-based techniques are applied to cloud CLWP retrieval in [96]. This topic is addressed based on SEVIRI data. The developed model was trained with CloudNet ground-based observation data and CLAAS-2 data as a high-quality reference. During the validation, this model achieved a higher correlation coefficient than physics-based retrieval in all situations. Another aspect that can be addressed using machine learning algorithms is the estimation of CTH. This was done in the work of [97], where the combined use of active and passive remote sensing measurements (CALIPSO cloud products and AHI radiance of Japanese GEO series) is proposed. Excellent results are obtained, with significant improvements in CTH for the mean absolute error (1.54-2.72 km). Also in this area, ref. [98] propose an XGBoost machine learning (ML)-based model and describe a tool to retrieve cloud macrophysical parameters for Himawari-8 cloud detection, cloud-top height, and cloud-top temperature. This model, which can be used both day and night, provides more accurate results than the existing JAXA AHI cloud results; however, the retrieval accuracy still needs to be improved. A deep learning approach that does not require auxiliary atmospheric parameters is developed by [99] to recover cloud properties, both day and night, from passive satellite observations. This method simultaneously recovers several cloud parameters (cloud optical thickness, cloud tops, cloud mask, the effective particle radius, and cloud height) involving convolutional neural network (TIR-CNN) thermal infrared radiances, viewing geometry and altitudes. The training database consists of MODIS products covering a full year, while the validation database is built with passive and active products observed in independent years. The cloud properties retrieved via TIR-CNN are found to be strongly consistent with all MODIS products. Using the thermal infrared spectral channels of Himawari-8 and combining the machine learning technique with the radiative transfer model, ref. [100] obtained cloud detection and cloud microphysical properties with high spatio-temporal resolutions. The authors observed that, regarding cloud retrieval, this model performs better in sun-glint areas due to the overestimation of the official Himawari-8 products. Creating coherent datasets on long-term cloud properties is the aim of ESA's (European Space Agency) Cloud Climate Change Initiative (Cloud_CCI) project based on AVHRR measurements. The study of [101] is the first to validate Cloud_CCI data products against ground instrument measurements from four high-latitude sites. The authors find that, although the Cloud_CCI CLWP retrievals are within ground-based instrument uncertainties, the Cloud_CCI Cloud Optical Depth (COD) turns out to be underestimated by about 3 optical depth (OD) units in the case of liquid-water clouds and by about 5 OD units if ice clouds are included. In the case of CTH, the observed overestimation becomes an underestimation of about 360-420 m for multilayer clouds.