## 3 Combining Infrared and Microwave Observations
Furthermore, the addition of the high-resolution IR spectrometer further decreases the relative error by 5% (CLWP) and 7% (D\({}_{\rm e}\)), compared to the results with the broadband IR radiometer. A methodology addressing the correction of cloud liquid water path (CLWP) measurements provided via Geostationary Operational Environmental Satellites-R (Series) (GOES-R) Advanced Baseline Imager (ABI) observations using microwave CLWP was developed by [117]. The CLWP ABI duly corrected for errors due to the scattering geometry and low cloud fraction showed significantly better reliability, especially at dawn and dusk and in non-rainy conditions. The problem of the combined use of Microwave Sounder (MWS) observations and future infrared (IASI-NG) observations for cloud liquid and ice water path retrieval has been addressed by [118]. The authors propose an NN approach that, while also considering the impact of next-generation sensors, can lead to notable improvements (up to 20%). Pairs of infrared and microwave instruments on Aqua, Suomi-NPP, and NOAA-20 provide observations for the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) products. Ref. [119] describes three-decade research conducted by the NASA AIRS Science Team (NASA AST) to retrieve profiles of atmospheric temperature, water vapor, ozone, and many other trace gases, as well as cloud (cloud liquid water, cloud-top pressure, and cloud fraction) and surface properties. A new application can be found in [120]. In this recent study, the authors developed a highly performing retrieval technique for ice hydrometeors. This methodology, which is based on neural network applications, exploits the sensitivity of submillimeter waves (in synergy with IR spectra) to the scattering effects of ice crystals. The retrieval of CIWP and D\({}_{\rm e}\) was evaluated through a sensitivity analysis using a simulated synthetic dataset. The obtained results are very encouraging, and they demonstrate that a synergistic strategy can improve the retrieval accuracy with mean square error values that are 68% (95%) and 10% (24%) lower for CIWP and D\({}_{\rm e}\), respectively, compared to the corresponding retrievals using sub-mm only (IR-only). Table 3 summarizes the main information for each study reviewed in Section 3, the sensors and channels used, and the products investigated. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Satellite**} & \multirow{2}{*}{**Main Features**} & \multirow{2}{*}{**Uncertainty or Accuracy of the Method (When Declared/Applicable)**} \\ \cline{3-3}  & & & **Sensor** & **Channel Frequency** & **Main Features** & **Uncertainty or Accuracy of the Method (When Declared/Applicable)** \\ \hline [PERSON] et al. [103] & 1998 & SSM/I, SEVIRI & - & Multilayered CF and R\({}_{\text{eff}}\) with MVI technique & - \\ [PERSON] et al. & 1995 & SSM/I, SEVIRI & - & Stratocumulus & - \\ [PERSON] and [PERSON] [105] & 2000 & AMSU, AVHRR & - & CLWP & - \\ [PERSON] et al. & 1902 & AMSU-A, HIRS/3 & - & 1D-Var approach for cloud-affected & - \\ [PERSON] et al. & 2012 & - & - & RTTOV & - \\ [PERSON] et al. [107] & 2000 & - & - & 1D-Var scheme & \\ [PERSON] et al. [107] & 2000 & - & - & with rain-affected & - \\  & & & & MW BT detection & \\ [PERSON] and [PERSON] [108] & 2004 & TRMM & IR, VIS, MW & \begin{tabular}{c} Drizzle in marine \\ warm clouds, COT, \\ R\({}_{\text{eff}}\), CLWP \\ \end{tabular}  & 2004 & TRMM & IR, VIS, MW & \begin{tabular}{c} Warm clouds, COT, \\ R\({}_{\text{eff}}\), CLWP \\ upper-level ice \\ cloud properties \\ Retrieval of \\ multi-layer cloud \\ parameters \\ \end{tabular}  & RMSD\({}_{\text{HW},\%}\) = 9\% \\ [PERSON] et al. [111] & 2010 & 2014 & \ \begin{tabular}{c} MMS, CPR, \\ HIRS \\ AVHRR, \\ MHS \\ AVHRS \\ MHS \\ \end{tabular}  & - &
\begin{tabular}{c} CIRP