Machine Learning Techniques for Fast Radiative Transfer

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Radiative transfer models for satellite data assimilation and physical atmospheric retrievals need to be both fast and accurate to fulfill operational constraints. These contradicting requirements have lead to the development of several algorithms specifically for this purpose. Machine Learning offers a new set of tools that can be used for this application and this presentation will discuss implementation and results of deep learning in this context.