The interaction of the atmosphere with the ocean is strongest in the tropics, where deep convection and rain-induced downbursts drive intense vertical exchanges of momen- tum, heat, and moisture. Under the right conditions, these moist convective processes organize upscale through aggregation of individual cloud systems (thunderstorms) into a mesoscale convective system (MCS)—a large contiguous area of convective and stratiform rain spanning about 100 km or more. Examples of MCSs over the tropical ocean include tropical cyclones and squall lines, and they are found embedded in tropical waves, superclusters, and the Madden–Julian oscillation. MCSs and other forms of organized convection are absent from contemporary global numerical weather prediction (NWP) and global climate models (GCMs) due to insufficient numerical resolution and inadequate parameterizations. Thus further theoretical and observational studies are necessary to support the appropriate representation of these processes in GCMs.

The small-scale wind variability in and near MCSs can be resolved by C-band fixed fan- beam space-borne scatterometers, such as the advanced scatterometer (ASCAT) on-board the MetOp series of satellites. A number of recent studies have demonstrated that ASCAT-derived wind fields and their spatial derivatives contain useful information on the interaction between winds and rain. The next step is to quantify that information in ways that can help improve parameterizations of moist convection. In this lecture I will describe our recent work to further this goal. Namely, a statistical approach to identify and quantify correlations between satellite (Meteosat Second Generation) measured rain rates and wind divergence calculated from ASCAT winds. We focus our attention on the Atlantic Intertropical Convergence Zone where MCSs are plentiful, rainfall is intense, and deep convection extends from the surface to the tropopause, strongly coupling the surface with the upper troposphere.