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Abstract. The Congo Basin in Central Africa is one of three convective centers in the tropics, characterized by a high proportion of precipitation produced by mesoscale convective systems (MCSs). However, process-level understanding of these systems and their relationship to environmental factors over the Congo Basin remains unclear, largely due to scarce in-situ observations. This study employs the Model for Prediction Across Scales–Atmosphere (MPAS-A), a global cloud-resolving model, to investigate MCSs in this region. Compared to satellite-observed brightness temperature (Tb), MPAS-A realistically simulates key MCS features, allowing a detailed comparison between two mesoscale convective complex (MCC) cases: one over the southern mountainous region (MCC-south) and the other over the northern lowland forests (MCC-north). MCC-south is larger, longer-lived, and moves a longer distance than MCC-north. Our analysis shows that MCC-south is supported by higher thermodynamic energy and more favorable vertical wind shear ahead of the system. The shear extends up to 400 km, explains up to 65 % of the Tb variance, and is well balanced by a moderately strong cold pool. In contrast, MCC-north features weaker, localized shear near the center and a stronger cold pool. The African Easterly Jet helps maintain the shear in both cases, but an overly strong jet may suppress low-level westerlies and weaken convection. These results show how latitude and topography modulate environmental influences on Congo Basin MCS developments. The findings underscore the value of global cloud-resolving models in data-sparse regions for understanding convective systems and their impacts on weather extremes and societal risks. 

Abstract The dynamics of an asymmetric rainband complex leading into secondary eyewall formation (SEF) are examined in a simulation of Hurricane Matthew (2016), with particular focus on the tangential wind field evolution. Prior to SEF, the storm experiences an axisymmetric broadening of the tangential wind field as a stationary rainband complex in the downshear quadrants intensifies. The axisymmetric acceleration pattern that causes this broadening is an inward-descending structure of positive acceleration nearly 100 km wide in radial extent and maximizes in the low levels near 50 km radius. Vertical advection from convective updrafts in the downshear-right quadrant largely contributes to the low-level acceleration maximum, while the broader inward-descending pattern is due to horizontal advection within stratiform precipitation in the downshear-left quadrant. This broad slantwise pattern of positive acceleration is due to a mesoscale descending inflow (MDI) that is driven by midlevel cooling within the stratiform regions and draws absolute angular momentum inward. The MDI is further revealed by examining the irrotational component of the radial velocity, which shows the MDI extending downwind into the upshear-left quadrant. Here, the MDI connects with the boundary layer, where new convective updrafts are triggered along its inner edge; these new upshear-left updrafts are found to be important to the subsequent axisymmetrization of the low-level tangential wind maximum within the incipient secondary eyewall. 

Abstract Ensemble‐based data assimilation of radar observations across inner‐core regions of tropical cyclones (TCs) in tandem with satellite all‐sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all‐sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all‐sky MW radiances in addition to GOES‐16 all‐sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all‐sky IR radiances alone, including a 24‐hr increase in forecast lead‐time for RI. Assimilating all‐sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC‐associated hazards in the future.