## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Microwave Observations Only
For example, in the case described by [25], the retrieval of CIWP and the effective ice particle diameter, D\({}_{e}\), from SSMI/S are based on a simplified two-flux radiative transfer model applied as a three-parameter equation model. The advantage of this procedure is that, due to the conical scan geometry of Special Sensor Microwave Imager/Sounder (SSMI/S), the retrieved CIWP is less dependent on the scan position. Furthermore, several important sources of errors that may affect the quality of retrievals are identified and analyzed. In particular, the authors identify the error in the cloud base temperature estimate and the effective particle diameter as the main sources of uncertainty in the CIWP retrieval, as their overestimation could lead to the underestimation of the CIWP for the smallest particles. In [26], the Microwave Integrated Retrieval System (MIRS) algorithm, a 1D-Var scheme developed at NOAA since 2007, is applied to data from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The approach followed is a simultaneous inversion obtained by coupling the land-ocean-atmosphere-cryosphere process to ensure that all radiances are fitted simultaneously and that geophysical reliability is also satisfied. The performance of the MIRS was evaluated using both in situ measurements (radar, radiosondes, gauges, and surface sensors) and analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and heritage algorithms (e.g., MSPPS). In fact, MIRS is expected to replace MSPPS as the NOAA operational product through a gradual, multi-phase transition. The results in [26] show a correlation of 0.99 and an RMSD of 3.1 kg/m\({}^{2}\) for a one-day validation of TPW against ECMWF. This daily validation is carried out operationally at \"[[https://www.star.nesdis.noaa.gov/mirs/geonwp.php](https://www.star.nesdis.noaa.gov/mirs/geonwp.php)]([https://www.star.nesdis.noaa.gov/mirs/geonwp.php](https://www.star.nesdis.noaa.gov/mirs/geonwp.php)) (accessed on 18 January 2025)\". However, the authors are aware of the difficulty of directly evaluating the hydrometeor parameters due to the nonexistence of consistent ground truth, and therefore, given the algorithm implemented in MIRS, they assume that the validation of the rain rate is a proxy for the validation of all MIRS-based hydrometeors. Following this approach, the expected values for bias and rms uncertainty are 1.5 and 2.5 kg/m\({}^{2}\) over land and 1.7 and 2.2 kg/m\({}^{2}\) over the ocean in the case of TPW, and 0.03 and 0.10 kg/m\({}^{2}\) over the ocean in the case of CLWP. Ref. [27] proposes two algorithms to estimate TPW and CLWP over oceans by assimilating microwave observations from sensors on board the FY-3D satellite. Both algorithms combine two oxygen channels (52.80 and 118.75 \(\pm\) 2.5 GHz) to address the lack of low-frequency window channels' (23.8 and 31.4 GHz) FY-3 sensor. A performance analysis of the two algorithms was conducted using four groups of experiments and comparing the results from FY-3C double oxygen absorption bandwidth with the retrievals using the classical method of [13] applied to MetOp-B AMSU observations at 23.8 and 31.4 GHz. The CLWP retrievals from the double oxygen band show correlation coefficients, bias, and rms in agreement with those obtained through the traditional AMSU scheme. On the contrary, the performance of TPW retrievals is significantly worse than the retrievals with the traditional AMSU scheme. Still with reference to FY-3C and strategies to overcome the lack of observations in the two low-frequency channels, ref. [28] describe a technique based on machine learning to generate BT at the two missing low-frequency channels from higher-frequency observations with mean absolute errors between 3 and 4 K. The statistical inversion method used to retrieve TPW and CLW over oceans also compares well in magnitude and distribution with Suomi NPP ATMS estimates. The article by [29] presents an assessment of the performance of MIRS CLWP retrievals.