## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Microwave Observations Only
In these works, the authors present a mini-satellite constellation design, implementing millimeter-wave (MMW) scanning radiometers. The proposed mission objective is to retrieve the troposphere thermal and hydrological properties, in particular temperature and water vapor profiles, rain rates, and snowfall rates. The channels are selected following a ranking based on a reduced entropy method between 90 and 230 GHz. The authors point out that the mission was restricted to the feasibility study, so only simulated data are available. In particular, in [20], the benefit of a 1D-Var scheme for deriving hydrometeor profiles from the proposed set of MMW observations is quantitatively evaluated, and the results are shown for the channel combination that achieved the best performance in the reduced entropy ranking (10 channels from 89 to 229 GHz). Furthermore, the algorithm performance in terms of the residual error between the measured profiles and the profiles retrieved from simulated data using 1D-Var was examined, showing good agreement for the hydrometeor profilesretrieved over land and sea and for a summer/winter season at different latitudes. The standard error statistics show a rather small contribution to the forecast, more significant over the sea than over land, for both liquid and ice cloud content. The four selected configurations of MMW radiometers' overall accuracy were evaluated by [21], exploiting two statistical inversion schemes, multiple regression and maximum likelihood. The performances of the two schemes were similar, showing only slightly algorithm-dependent retrieval uncertainty. Using multiple regression with their best channel configuration (10 channels), the authors report the following correlation coefficient (R), root mean square (RMS), and uncertainty (\(\sigma\)):  
* CLWP (0-1.4 kg/m\({}^{2}\)): R = 0.70; \(\sigma\) = 0.08 kg/m\({}^{2}\) over land; R = 0.87; \(\sigma\) = 0.04 kg/m\({}^{2}\) over the ocean. * CIWP (0-0.25 kg/m\({}^{2}\)): R = 0.58; \(\sigma\) = 0.01 kg/m\({}^{2}\) over land; R = 0.64; \(\sigma\) = 0.03 kg/m\({}^{2}\) over the ocean. The correlation increased considerably for rain-sized ice particles (i.e., graupel), R = 0.97 over the ocean and 0.97 over land. In these works, it is also shown that the CLWC profiles' expected RMS uncertainty is within 0.1 g/m\({}^{3}\) over land and 0.04 g/m\({}^{3}\) over the ocean; however the authors warn that these results should be valid for the available dataset only. Overall, they report lower performances for non-precipitating CLWC and CIWC profiles than for precipitating liquid (rain) and ice (graupel) profiles, over both land and ocean. A further impetus to the knowledge of cloud microphysical properties is provided through the development of innovative processing techniques and advances in analytical and computational capabilities. In recent years, the expanding field of artificial intelligence has promised to provide a new opportunity for making cloud microphysical products increasingly accurate and opening the way to a new understanding in this field. An example of the application can be found in [23], where the retrieval of TPW and CLWP from SSM/I and AMSR-E is addressed with a neural network (NN) algorithm. This approach, which can be applied to high-latitude open water areas, has been shown to be a better choice than the conventional regression techniques (validation against radiosonde data from a polar station). The validation process shows that the resulting RMSD for the retrieval products is 1.09 kg/m\({}^{2}\) for SSM/I and 0.90 kg/m\({}^{2}\) for AMSR-E, respectively. Moreover, only for SSM/I, the TPW algorithm is compared with the global operational algorithm described in [24], showing retrieval errors of 1.34 kg/m\({}^{2}\) and 1.90 kg/m\({}^{2}\) (about 40% worse) and demonstrating the advantages of the NN approach of [23]. Radiative transfer models can also be used to improve the quality of retrievals.