## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Microwave Observations Only
CLWP derived from AMSU on NOAA-16 and CLWP generated from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) were also compared, and good qualitative agreement but significant divergences were found from a quantitative point of view, presumably due to a difference in the acquisition spectral channels of the two instruments. Overall, global mean differences of the order of 10% with spatial correlations on the order of 0.75 were found. Following the same methodology, they also concluded that the CLWP affects the agreement of AMSU TPW retrievals with radiosondes, with larger biases than relatively higher CLWP values. Analogously, the TPW RMSD also increases, ranging from about 10% for CLWP < 0.2 kg/m\({}^{2}\) to over 15% for CLWP > 0.8 kg/m\({}^{2}\). In the work of [18], the described estimation of the vertical distribution of liquid water and its integrated amount on a water surface is the first step of a microwave geophysical retrieval algorithm based on the application of a forward radiative transfer model within the combined sounding suite exploiting the Atmospheric Infrared Sounder/AMSU-A/Humidity Sounder for Brazil (AIRS/AMSU/HSB). The methodology implemented for CLWC retrieval is based on a moisture condensation model that uses the relative humidity retrieval for cloud formation, which makes the retrieval process more robust than usual techniques because it is less sensitive to retrieved temperature profile errors and to the humidity field within instrument footprint horizontal inhomogeneities. The results are validated via comparisons with ground-based radiometric measurements with reference to the retrieved CLWC and with relative humidity profiles from radio-sounding launches dedicated during the Aqua satellite overpass with respect to the vertical distribution (mean cloud pressure). The estimation of cloud liquid profiles and the integrated amount over a water surface using the AMSU/HSB algorithm seems to offer some reliability. However, it would be necessary to expand the comparison dataset with a wider range of cloud heights and to incorporate infrared measurements into the process. Unfortunately, a careful characterization of the cases in which the algorithm does not provide the expected results is missing from this work. An accurate and computationally not very expensive approach to atmospheric profile retrieval is proposed by [19]. The purpose of their effort is to develop an iterative technique based on the one-dimensional variational method (1D-Var) to simultaneously retrieve water vapor and temperature profiles, as well as cloud water profiles. This purpose is reached by combining both the AMSU window and sounding channels using a synergic method. The 1D-Var microwave principal module for retrieval is developed in sequential key steps; first, it computes water vapor, temperature, and cloud liquid water profiles using only AMSU-A data at frequencies less than 60 GHz. Then, rain and ice water are derived using AMSU-B data at 89 and 150 GHz. Ultimately, temperature and water vapor profiles are more refined by means of all AMSU-A/B sounding channels (50-60 and 183 GHz), while the profiles of cloud, rain, and ice water contents are forced to those earlier derived. Validation work was conducted on the 1D-var retrieval algorithm in terms of water contents. A comparison with radiosonde data and collocated satellite measurements showed a relatively small bias (less than 0.3 kg/m\({}^{2}\)) and RMSDs of 2.7, 2.3, and 2.5 kg/m\({}^{2}\), evaluated against data on different NOAA platforms (NOAA-15, -16, and -17, correspondingly). Comparisons were also made over sea and land (but ignoring the data over high latitudes beyond 60\({}^{\circ}\) north and south) between the AMSU-derived TPW and the GFS data assimilation system (GDAS). In this case, the bias and RMSD values of the zonal means are 0.15 and 0.75 kg/m\({}^{2}\), respectively. Applications of the one-dimensional variational method to the problem of retrieving the hydrological properties of the troposphere are also found in [20; 21; 22].