## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Microwave Observations Only
A similar analysis was conducted for the retrieved values of CIWP, noting that they are affected not only by uncertainties in the estimation of the BT and particle volume density but also by the retrieved D\({}_{\rm e}\) itself. The authors estimated that a 30% error in the bulk volume alone would result in a 25% error in the retrieved CIWP. Unfortunately, due to the lack of sensitivity at 89 GHz, this procedure seems to fail to detect thin cirrus clouds surrounding precipitating areas. In these cases, using even higher frequencies or measurements in the IR/VIS region may be effective. Again in reference to the AMSU radiometer, ref. [16] describes the application of the algorithms in [13] for cloud and precipitation products and present a methodology to examine the instrument performance after launch. In this work, the 23.8 and 31.4 GHz AMSU channels are exploited to retrieve cloud liquid water and total precipitable water. In this case, the results obtained via the algorithm are significantly improved by using global analyses of surface wind and sea surface temperature from the NWP model. In a similar way, the 89 GHz and 150 GHz channels are used to estimate the ice water path associated with thick ice clouds. However, following this routine method, the uncertainty in the retrieval of cloud ice water path and particle size remains high due to an unknown particle bulk volume density. Starting from the past experience with SSM/I and, subsequently, with the hydrological products obtained from the operational algorithms based on the AMSU sensor series, ref. [17] describes the operational Microwave Surface and Precipitation Products System (MSPPS) product system, which represents an upgrade to the previous ones and provides near real-time (only a few minutes of processing) hydrological products from AMSU. In their paper, ref. [17] outline the retrieval algorithms' status as of September 2004 and present several comparisons with ground-based measurements and other satellite products. In detail, CLWP and TPW are estimated using 50 and 23.31 GHz channels, primarily based on the algorithms reported in [16], while the algorithm in [15] is used to simultaneously derive CLWP and ice particle effective diameter D\({}_{\text{e}}\) from 89 and 150 GHz. The formulas for computing the TPW and CLWP use coefficients valid only for the sea surface, unlike the calculation of CIWP, which is valid on both land and ocean with the background surface free of ice/snow, as it is inferred from AMSU lower-frequency channels. Comparisons between the AMSU-derived TPW and both the radiosonde-derived TPW for nine stations and the radiometer-derived TPW for ARM sites representative of open ocean conditions on Manus Island and Nauru are shown. A similar approach is applied to AMSU-derived CLWP with respect to the ARM-derived CLWP. It is worth noting that, due to the poor spatial homogeneity of the CLWP compared to the TPW, the former requires special treatment to better use it for the validation of AMSU data, as described by [13]. As a result, a correlation of about 0.6, an RMSD of about 0.05 kg/m\({}^{2}\), and a small bias of less than 0.01 kg/m\({}^{2}\) was found for approximately 190 and 160 CLWP match-up points in the case of NOAA-15 and NOAA-16, respectively. To reduce the non-uniformity of the data distribution in the available range, a further analysis was conducted by binning CLWP retrievals into 0.01 kg/m\({}^{2}\) bins using the ground-based ARM data. The binned analysis leads to the following main conclusions: (i) there is very little bias in the AMSU-derived CLWP for values > 0.3 kg/m\({}^{2}\), validating the reliability of CLWP retrieval from 31.4 GHz observations under non-precipitation conditions; and (ii) there is some angular dependency on the retrievals, most likely attributed to the larger FOV (for this and other abbreviations in the text, please see the List of Abbreviations and Acronyms at the end of the document) of the AMSU-A sensor as the view angle increases.