## 3 Combining Infrared and Microwave Observations
The initial state underlying the operation of the algorithm is generated from the AMSU and AIRS observations, also providing the set of values of the variables that characterize the clouds (radiatively effective cloud fraction and cloud-top pressure), as well as supporting the actual physical retrieval procedure. In detail, compared to the previous operational version, the improvements contained in the AIRS Science Team version-6 retrieval algorithm are many and significant, and they also include the methodology for determining the cloud parameters, which are retrieved from up to two layers of gray clouds in a given scene. Furthermore, in version 6, there is the possibility that the algorithm can also work in AIRS-only (AO) mode. This processing mode uses exclusively AIRS observations without benefiting from the contribution of AMSU observations in any phase, including the generation of the version-6 AO initial state. The ability to operate in AO mode is extremely important, as it provides a backup if AMSU information deteriorates significantly. From a quality perspective, version-6 AO retrievals are generally only slightly less accurate than those of the full version 6. The main differences between the results of the two modes are mostly found over the ocean, as AO mode cannot benefit from observations in the 22 and 31 GHz channels of AMSU-A. Another algorithm based on AIRS-like sounding systems is the NOAA Unique Combined Atmospheric Processing System (NUCAPS), which has its origin in the AIRS Science Team approach [114]. This algorithm, designed at the NOAA National Environmental Satellite, Data, and Information Service (centre) for Satellite Applications and Research (NOAA/NESDIS/STAR), and the underlying spectroscopy are currently used to process data from combined MW and IR channels of the AIRS/AMSU suite, the IASI/AMSU/MHS suite (operational since 2008) and, more recently, the Cross-track Infrared Sounder (CrIS)/ATMS suite, operational since 8 April 2014. It was designed with a modular architecture, and the objective of some of the six modules (modules 2, 3, 5, and 6) of which it is composed is precisely the retrieval of cloud properties, based on the synergy of data from MW and IR. In more detail, based on MW-only observations, cloud liquid-water flags and surface emissivity uncertainty (module 2), as well as temperature and moisture variables, are recovered via an all-sky eigenvector regression approach (module 3), a second fast eigenvector regression retrieval for temperature and moisture for cloud cleared radiances (module 5), and a final IR physical retrieval, with the previous regression retrieval as the first guess (module 6). The remaining modules are dedicated to quality control for the input, search tables, and acquisition of accessory products (module 1) and to obtaining a cloud clearing module that combines a set of MW and IR channels (module 4). NUCAPS, which came into operation in April 2018, has been the subject of numerous studies that have validated its performance (i.e., [115]). The synergy of ground-based, high-resolution IR and MW observations was studied by [116] in the case of thin clouds of single-layer liquid water (CLWP < 0.1 kg/m\({}^{2}\)). In these situations, MW observations have generally lower sensitivity than in other cases, unlike IR observations, which show higher sensitivity. The developed methodology relies on an NN scheme to estimate both CLWP and cloud-effective radius, based on a synthetic dataset of MW observations and additional broadband and highly spectrally resolved IR observations. The results show that the NNs can retrieve CLWP and D\({}_{\rm e}\) with a mean relative error of 9% (for CLWP) and 17% (for D\({}_{\rm e}\)). Consistently with the sensitivity of MW observations in the case of thin clouds, the retrieval is affected by a high relative error, using only MW data, contrary to what is observed for IR retrievals (CLWP 40-60 g/m\({}^{2}\)). As a result, combined (MW with broadband IR observations) retrievals are affected by less error than MW alone.