On occasion, tropical cyclones (TCs) have been shown to strengthen over land, provided that the land is warm and moist. The emergent hypothesis is that the moist surface provides sustaining latent heat flux (LHF) reminiscent of an oceanic environment (the “Brown Ocean Effect” or BOE). Chapter 1 provides a review of mechanisms associated with TC intensification over land and tests the BOE hypothesis using numerical simulations of idealized TCs with different levels of soil moisture availability (SMA). Afterwards, a more sophisticated experiment was conducted with additional SMA profiles and different roughness lengths (Chapter 2). SMA gradients are shown to have a large influence on precipitation. The sensitivity of accumulated precipitation to SMA is larger with enhanced friction. The maximum wind speed is more sensitive to differences in SMA under lower surface roughness. In Chapter 3, the idealized simulations are reexamined to evaluate the structure, intensity, and precipitation mechanisms. Vortical hot towers and Vortex Rossby Waves are identified and describe the radial pattern of local wind maxima but fail to describe the steady state patterns. The wind-induced surface heat exchange (WISHE), while appealing as an explanation, needs to be modified to describe the BOE. It is shown that the BOE is a semi-stable state with condensational warming causing structural degradation to the outflow but maintaining the warm-core structure. The increase in LHF also enhances the precipitation. TC Maintenance and Intensification (TCMI) is a generalized definition of TCs that strengthen or maintain intensity inland. While extratropical transition is a well-studied explanation for many cases, the BOE is a relatively new explanatory hypothesis for certain storms. In Chapter 4, a novel methodology is proposed to examine the TC record to improve climatological representation of such cases. Using IBTrACS, individual times of inland TCs were classified into TCMI and non-TCMI (weakening) events. The MERRA-2 dataset was applied to develop a prototypical machine-learning model to help diagnose future TCMI events. A list of possible TCMI storms for case studies in future analyses is provided. Two of these storms were examined for BOE attributes.