Monday, 11 January 2016
Satellite retrievals of light rain from some platforms are consistently underestimated due to a lack of observation sensitivity, which likely also biases analogous estimates of latent heating. Latent heating is a necessary component for understanding and modeling the atmospheric general circulation as it provides a mechanism to transfer energy vertically from the surface. Thus, a need to better estimate latent heating in regions dominated by warm rain is ubiquitous. A Bayesian Monte Carlo algorithm is assembled with a set of oceanic Regional Atmospheric Modeling System warm rain simulations. The algorithm uses key height and integrated characteristics of a reflectivity profile to infer latent heating, surface rainfall rate, and liquid water path. By leveraging observations from CloudSat's Cloud Profiling Radar, which has a high sensitivity to light rain, this algorithm is used to examine distributions of latent heating in warm rain on a regional and global scale.
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