<p>For the first time, the SAPHIR-obtained brightness temperature (T_B) has been assimilated into the Weather Research and Forecasting model to understand the importance and challenges in all-sky data assimilation. Simultaneous assimilation analysis using three-dimensional variational data was performed to characterize the impact of all-sky T_B against clear-sky T_B assimilation for the prediction of tropical cyclone Ockhi, a rapid-intensifying cyclone in late 2017 over the North Indian Ocean. Strong eyewall convection in Cyclone Ockhi was identified by Global Precipitation Measurement observations. This convection was characterized by high rainfall rates, increased reflectivity, larger raindrop sizes, and distinct microphysical signatures associated with breakup processes in stratiform rainbands and collision–coalescence in convective regions. Approximately 10–25% more T_B observations are added for different SAPHIR channels in all-sky condition data assimilation. The assimilation of SAPHIR all-sky radiances results in a non-Gaussian error distribution, high uncertainties, and strong non-linearities. These observations alter the dynamical and physical balances of the model, causing initial shocks and erroneous gravity waves. Thus, standalone all-sky SAPHIR assimilation may perform worse than clear-sky radiance assimilation, which is less chaotic. Consequently, all-sky radiances combined with Gaussian transformation, digital filter initialization (DFI), and covariance inflation yield the best results. The assimilation of all-sky T_B improved the track prediction by 10% compared to clear-sky data assimilation and demonstrated applications of additional Microwave observations mainly over ice and rain conditions. Moreover, a marginal positive impact of 2% is noted in intensity prediction with all-sky data assimilation.</p>