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Abstract

Upper tropospheric (UT) clouds play a crucial role in the climate system by modulating the Earth's energy budget and heat transport. These clouds are most abundant in the tropics, where they often form as cirrus anvils from convective outflow, building mesoscale systems. The radiative heating of the cirrus anvils may be critical to cloud climate feedback.We are building a coherent long-term 3D dataset which describes tropical UT cloud systems for process and climate studies. For this purpose we used cloud data from the Atmospheric InfraRed Sounder and the Infrared Atmospheric Sounding Inferometer and atmospheric and surface properties from the meteorological reanalyses ERA-Interim and machine learning techniques. The different artificial neural network models were trained on collocated radar – lidar data from the A-Train. The rain intensity classification has an accuracy of about 65 to 70% and allows us to build objects of strong precipitation, used to identify convective organization. This rain intensity classification is more efficient to detect large latent heating than cold cloud temperature. In combination with a cloud system analysis we found that deeper convection leads to larger heavy rain areas and a larger detrainment, with a slightly smaller thick anvil emissivity. This kind of analysis can be used for a process-oriented evaluation of convective precipitation parameterizations in climate models. Furthermore we show the usefulness of our data to investigate tropical convective organization metrics. Selected results on the relation between horizontal and vertical structure of the convective systems and on temporal anomalies will be presented.