Estimation of Latent heat profiles of Deep Convective Clouds using CloudSat Radar

The earth's atmosphere is highly coupled between the vertical layers and the surface. Understanding the circulations in the atmosphere is important for developing models and improving weather forecast. The latent heat exchanges in the atmosphere is one of the key driving forces of these circulations. There have been numerous studies which have highlighted the latent heat being the source of many atmospheric waves and oscillations. It is therefore very important to accurately estimate the latent heat in the atmosphere, especially for the convective clouds which have proven to be one of the major sources of gravity waves. Currently, satellite based latent heat estimation is very limited due to which, any detailed global study regarding latent heat effects and relationships with atmospheric phenomena have been restricted to theoretical works. There is a need for developing more methods to increase the spatial and temporal coverage of latent heat estimates which is the objective of the current study. A method has been proposed in this study which retrieves the accumulated latent heat profiles of deep convective clouds from combined CloudSat-CALIPSO cloud profiles. A realistic database of simulated deep connective cloud events are compared with observations using the Bayesian Monte Carlo method to derive an estimate. This study would help to understand the atmospheric circulations originated by heating, in more detail.