## 3 Combining Infrared and Microwave Observations
In detail, the 1D-Var scheme is based on Advanced TOVS (ATOVS) observations. Consequently, IR radiances are always available when AMSU data are available; on the contrary, rain-affected MW data, if present, are removed consistently from the radiation model specifications, according to [107]. With respect to Clouds and the Earth's Radiant Energy System (CERES) Outgoing Longwave Radiation (OLR) observations, the 1D-Var retrieval scheme proves to be able to extract the liquid/ice water information contained in the radiances, showing clear improvements in the representation of the cloud ice model. On the other hand, MW information also has a positive impact on the knowledge of low clouds, while the representation of the vertical distribution of clouds needs to be improved due to the limits in the sensitivity of the passive IR and MW radiation. Unfortunately, as acknowledged by the authors themselves, the nonlinearity of cloud variation and the intrinsic on/off nature of cloud layers make the evaluation of the background-error covariance matrix rather problematic. Combining near-IR, VIS, and MW observations from the Tropical Rainfall Measuring Mission (TRMM) satellite allowed [108] to develop a technique for drizzle detection in marine warm clouds. First, a near-IR/VIS algorithm is used to simultaneously identify COT and the effective radius; then, CLWP is retrieved via a new algorithm developed in MW. Simulations from radiative transfer models supported by the analysis of satellite observations allowed the study of the relationship between OD, the effective radius, and CLWP, whereas radar observations of clouds in coincident areas constituted the validation dataset of the described method. Observations in the MODIS bands, combined with the AMSR-E frequencies, allow for the estimation of CLWP and CIWP for double-layer clouds. This topic was addressed by [109]. They developed a method to evaluate the CLWP of lower-layer water clouds from AMSR-E observations and, subsequently, the properties of upper-level ice clouds from MODIS by matching the radiances simulated by an RTM with a two-cloud layer. This approach can achieve COT and CIWP retrievals with significantly improved accuracy and reduced overestimation for ice-over-water cloud systems, as indicated by comparisons with single-layer cirrus systems and surface-based radar retrievals. The development of new techniques to improve the retrieval of cloud parameters, especially in the presence of multilayered clouds, is also required, given the evidence that methods that usually provide accurate results in the case of a single cloud layer (such as, for example, the CO\({}_{2}\) slicing technique for cloud-top retrieval or IR-only observations for cloud properties retrieval) are not reliable in the case of thick clouds with multiple layers. In these cases, the approach developed by [110], which uses multispectral satellite data (AIRS and AMSU) to penetrate the cloud-top, can be useful to obtain reliable data and a better agreement with ground-based observations. The validation work considered 40 ground observations collected at the Chilbolton Observatory Facilities (UK) during spacecraft flybys and found an RMS agreement within 295, 905, 1094, and 1862 m for CTH and CBH, and 0.62, 0.08, and 0.02 kg/m\({}^{2}\) for TPW, CLWP, and CIWP, respectively. These results lead to the conclusion of a better ability for HIRS to discriminate between ice/no-ice clouds than AMSU-A or MHS. The synergistic use of observed data from different sensors requires careful collocation work. The development of a simpler collocation methodology between the MHS on board NOAA-18 and the CPR on board CloudSat is the subject of the paper by [111]. In addition to the description of this new collocation-finding method, the paper presents the results and statistical analyses performed and a section dedicated to the practical implications.