A lack of quality data on high clouds has led to inadequate representations within global weather and climate models. Recent advances in spaceborne measurements of the Earth’s atmosphere have provided complementary information on the interior of these clouds. This study demonstrate how an array of space-borne measurements can be used and combined, by close co-located comparisons in space and time, to form a more complete representation of high cloud processes and properties. High clouds are found in the upper atmosphere, where sub-zero temperatures frequently result in the formation of cloud particles that are composed of ice. Weather and climate models characterise the bulk properties of these ice particles to describe the current state of the cloud-sky atmosphere. By directly comparing measurements with simulations undertaken at the same place and time, this study demonstrates how improvements can be made to the representation of cloud properties. The results from this study will assist in the design of future cloud missions to provide a better quality input. These improvements will also help improve weather predictions and lower the uncertainty in cloud feedback response to increasing atmospheric temperature. Most clouds are difficult to monitor by more than one instrument due to continuous changes in: large-scale and sub-cloud scale circulation features, microphysical properties and processes and characteristic chemical signatures. This study undertakes co-located comparisons of high cloud data with a cloud ice dataset reported from the Microwave Limb Sounder (MLS) instrument onboard the Aura satellite that forms part of the A-train constellation. Data from the MLS science team include vertical profiles of temperature, ice water content (IWC) and the mixing ratios of several trace gases. Their vertical resolutions are 3 to 6 km. Initial investigations explore the link between cloud-top properties and the longwave radiation budget, developing methods for estimating cloud top heights using; longwave radiative fluxes, and IWC profiles. Synergistic trios of direct and indirect high cloud measurements were used to validate detections from the MLS by direct comparisons with two different A-train instruments; the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) and the Clouds and the Earth’s Radiant Energy System (CERES) onboard on the Aqua satellite. This finding focuses later studies on two high cloud scene types that are well detected by the MLS; deep convective plumes that form from moist ascent, and their adjacent outflows that emanate outwards several hundred kilometres. The second part of the thesis identifies and characterises two different high cloud scenes in the tropics. Direct observational data is used to refine calculations of the climate sensitivity to upper tropospheric humidity and high cloud in different conditions. The data reveals several discernible features of convective outflows are identified using a large sample of MLS data. The key finding, facilitated by the use of co-location, reveals that deep convective plumes exert a large longwave warming effect on the local climate of 52 ± 28Wm−2, with their adjacent outflows presenting a more modest warming of 33 ± 20Wm−2.