## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Infrared Observations Only
Worse performance is obtained for the detection of mixed-phase clouds; only 5.5% of this type is correctly identified, as most of these are detected as ice clouds. CTP is retrieved with a bias between 30 hPa bias and 50 hPa std compared to ground-based radars. The correlation coefficients with satellite lidar observations from CALIOP are 0.9. Furthermore, for the IASI CTP, the validation shows a bias of 15 hPa between 1000 and 550 hPa and std \(\approx\) 90 hPa. Above 550 hPa, the dispersion is less (std \(\approx\) 60 hPa), although the bias is greater. In preparation for the direct assimilation of CLWC and CIWC profiles into 3D-Var, ref. [71] reports the evaluation of the feasibility of adding such profiles into the control vector of a 1D-Var assimilation system. This approach avoids the usual use of cloud parameters (effective cloud fraction and CTP), typically inferred from a CO\({}_{2}\) slicing algorithm, and cloud modeling using single-layer clouds [36]. CLWC, CIWC, and cloud fraction profiles are included via the RTTOVCLD interface of Radiative Transfer for TOVS (RTTOV) 10.1(see [72], Section 2.6.1). RTTOVCLD allows multilayer mixed-phase clouds (two types of clouds per layer). Results from the simulation experiments (OSSE) demonstrate that 1D-Var works reasonably, adding an ice or liquid-water cloud in the correct region of the atmosphere. Furthermore, this study, which is limited to homogeneous cloud scenes with small background offsets, demonstrates that CIWC information is obtained for high opaque clouds, whereas little information is extracted for CLWC. Using the identical 1D-Var framework, ref. [73] exploited a reduced number of IASI channels for data assimilation in cloudy sky. Following the approach in [74], information loss can be minimized through channel selection. This paper demonstrated that the channel selections are practically independent of the air-mass type and can improve the cloud variables' retrieval by 8% rms compared to the operational dataset exploited at NWP centers. In a follow-up study, ref. [75] observed a meaningful reduction in the forecast error, particularly for CIWC, but also for CLWC. On average, a CIWC error reduction of 15-20% is achieved for semitransparent clouds versus 9% for opaque clouds, compared to 10% for the CLWC of semitransparent clouds and 3% for opaque clouds. This is not surprising, as the CLWC is known to be not well analyzed through 1D-Var, probably due to the relatively small sensitivity of the IR data to liquid cloud. In any case, the persistence of cloud information produced via a 1D-Var assimilation of cloudy IASI radiances into a convective scale NWP is established. The authors suggest that CLWC analysis could be better constrained using MW radiances. Unlike the previous approaches, ref. [76] retrieve geophysical properties from the single FOV spectral radiances acquired via IASI under any conditions, using a PCRTM-based physical inversion approach. In their study, they show the excellent performance of the PCRTM retrieval method in detecting thin clouds with a COT as low as 0.04, revealing the extreme sensitivity of the developed algorithm to ultra-thin clouds. However, worse results are found for COT > 4. A different approach is used in the work of [77], where cloud property retrieval through the use of the Clouds from Infrared Sounders, developed at LMD (CIRS-LMD), is described. In this case, the channels around the 15 \(\upmu\)m CO\({}_{2}\) absorption band are considered. These channels can provide the highest cloud layer in the case of multilayer clouds, as well as the pressure and emissivity of the clouds in the case of a single cloud layer. This scheme has been applied to AIRS and validated with CALIOP's high-resolution cloud vertical profiles, showing satisfactory results regarding the retrieved cloud height, which corresponds well to the maximum backscatter measured via CALIOP for both high- and low-level clouds.