## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Infrared Observations Only
In detail, the cloudy condition (clear/cloudy, ice/water, and single/multilayer) of the AIRS subpixel is characterized by the MODIS cloud mask with a spatial resolution of 1 km during both day and night. Subsequently, with the operational MODIS COT and cloud particle size as the background information, 1D-VAR can be used to retrieve the microphysical properties of clouds during the day, while minimum residual (MR) is used both during the day and during the night. In both approaches (1D-VAR and MR), the Cloud-Top Pressure (CTP) is derived from the radiances acquired via AIRS at CO\({}_{2}\) channels, while the cloud phase is inferred from the collocated MODIS 1-km phase mask. The authors compared 1D-VAR results with the operational MODIS products and MR cloud microphysical property retrievals for a case study and reported that 1D-VAR retrievals have a great correlation with both the operative MODIS cloud products and MR cloud retrievals. Ref. [68] presents a physical inversion scheme for clear/cloudy radiance observed with ultra-spectral infrared sounders to simultaneously retrieve atmospheric thermodynamic, surface parameters and cloud microphysical parameters. To improve an iterative background state defined by an eigenvector regression retrieval, a 1D-VAR approach is used. Cloud-top height can be estimated with quite high accuracy (the error is less than 1 km), for both optically thick and thin clouds. Finally, it is highlighted that using a more realistic ice-cloud habit could improve the retrieval performances. The paper by [69] aims to use all available Infrared Atmospheric Sounding Interferometer (IASI) channels for the simultaneous retrieval of atmospheric temperature, humidity, and cloud properties by applying an Empirical Orthogonal Function (EOF) transformation to convert IASI channel radiance spectra into super-channels. Since it is shown that approximately 100 super-channels are sufficient to capture the information content of all the spectra, the calculation of both super-channel magnitudes and derivatives with respect to the atmospheric profiles and other properties can be performed via a Principal Component-based Radiative Transfer Model (PCRTM). This super-channel retrieval algorithm is applied to the IASI spectra acquired during the Joint Airborne IASI Validation Experiment (JAIVEx) field campaign. Ref. [70] also refers to the IASI spectrometer, describing the operational IASI L2 processing chain (version 5) structure and counting the single retrieval modules, their algorithms, and a summary of different performance evaluation validation studies. A wide range of satellite products (including the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Advanced Along-Track Scanning Radiometer (AATSR), MODIS, AVHRR, and SEVIRI), numerical weather prediction, chemistry models, and in situ measurements (including radiosondes and buoys) are used to validate the retrieved geophysical parameters. The cloud detection, cloud cover, height, and phase included in L2 products are also evaluated. In detail, to assess cloud detection, an NN test is also added to the tests based on NWP and AVHRR, which increases the overall cloud detection capacity by roughly 25% compared to the NWP test alone, allowing the achievement of an overall success rate of over 90%. The different spectral emissivity that characterizes water and ice clouds in the spectral region between 8 and 12 um allows the retrieval of the cloud phase. Ice clouds are identified through an additional test, applicable only in version 5, which takes advantage of the fact that super-cooled water cannot exist at temperatures below \(-40\)\({}^{\circ}\)C. The validation of the cloud phase detection process was performed using a co-located and globally distributed IASI and AVHRR dataset. The results confirm a good performance of the tuned algorithm which, in its final version, correctly detects 84.5% of the cloud phases, 97.3% of the ice samples, and 84.6% of the liquid samples.