## 3 Combining Infrared and Microwave Observations
The validation of the operational CIWP product from MHS measurements, the NOAA MSPPS, against CloudSat CIWP highlights a low sensitivity found in many null values for the MSPPS CIWP, probably due to thin clouds' being rather transparent at MHS frequencies. To overcome this, the authors also evaluate the addition of two HIRS channels (8 and 11) to the CIWP retrieval from MHS. This, however, leads to only a small detectable improvement for small values of CIWP due to modest improvements in the HIRS-MHS footprint difference and beam-filling issues. An interesting result regarding the synergy between IR and MW is obtained by [112], which, for the case examined, does not agree with the most widespread conclusions on this topic. In fact, by applying an approach based on the Support Vector Machine (AID-SVM) methodology and on information ice/no-ice clouds acquired via the CloudSat CPR, the paper highlights that this methodology offers very promising potential to obtain information on ice clouds/no ice using passive satellite sensors, over both the ocean and the land surface. However, in the cases studied, it appears that IR satellite sensors, such as HIRS, are more efficient at detecting ice clouds than MW satellite sensors and that combined measurements using MW/IR synergy do not perform better than IR measurements alone. The method was applied and tested on data from various sensors (AMSU-A, MHS, and HIRS instruments aboard NOAA-19 satellite) providing an ice cloud detection probability of 0.37 for AMSU/A, 0.51 for MHS, 0.83 for HIRS, and 0.83 for the AMSU/A, MHS, and HIRS combination over the ocean. Similarly, over land, the authors obtain 0.42 for AMSU/A, 0.50 for MHS, 0.76 for HIRS, and 0.80 for AMSU/A + MHS + HIRS. Ref. [113] addresses the problem of atmospheric ice detection and the CIWP retrieval completely based on passive operational sensors Synergistic Passive Atmospheric Retrieval Experiment-ICE (SPARE-ICE). They develop a set of NN trained on an observation-based dataset of AVHRR and MHS measurements on the one hand and joint radar-lidar CIWP on the other hand, obtained by collocating NOAA-18 with the Cloudsat and CALIPSO Ice Cloud Property Product (2C-ICE) CIWP product. The algorithm, which exploits three AVHRR channels, three MHS channels, and auxiliary information such as satellite angles, surface temperature, and surface elevation, reveals a very good correlation for CIWP > 10 g/m\({}^{2}\) between the reference dataset 2C-ICE and SPARE-ICE. For smaller values of CIWP, SPARE-ICE shows the tendency to be larger than 2C-ICE, becoming stable at a median of about 1-2 g/m\({}^{2}\) for 2C-ICE CIWP < 1 g/m\({}^{2}\). Furthermore, the paper indicates that SPARE-ICE is not sensitive to small values of CIWP. Unfortunately, it was not possible to provide a direct estimate of uncertainty for a single retrieval using NN. However, an error estimate as a function of CIWP was provided based on the validation dataset, i.e., data not used in the training phase. Based on the fractional error definition, the authors found a value of about 2 between SPARE-ICE and CloudSat 2C-ICE for the median fractional error, comparable to the random error between 2C-ICE and in situ CIWP measurements. The advantages of combined retrieval are also addressed in the work of [114]. In this study, the improvements to the AIRS/AMSU version 6 retrieval algorithm are explored. Flying on the same satellite platform (Aqua), the AIRS and AMSU instruments lend themselves very well to evaluating the trend of combined retrievals. This algorithm is now operational at the NASA Goddard Data and Information Services Center (DISC). Since September 2002, Level 2 and Level 3 products have been available, and, in particular, AIRS Science Team version-6, level-2 products can be accessed in near real time on the Goddard DISC.