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The formation of precipitation in state-of-the-art weather and climate modelsis an important process. The understanding of its relationship with othervariables can lead to endless benefits, particularly for the world's monsoonregions dependent on rainfall as a support for livelihood. Various factors playa crucial role in the formation of rainfall, and those physical processes areleading to significant biases in the operational weather forecasts. We use theUNET architecture of a deep convolutional neural network with residual learningas a proof of concept to learn global data-driven models of precipitation. Themodels are trained on reanalysis datasets projected on the cubed-sphereprojection to minimize errors due to spherical distortion. The results arecompared with the operational dynamical model used by the India MeteorologicalDepartment. The theoretical deep learning-based model shows doubling of thegrid point, as well as area averaged skill measured in Pearson correlationcoefficients relative to operational system. This study is a proof-of-conceptshowing that residual learning-based UNET can unravel physical relationships totarget precipitation, and those physical constraints can be used in thedynamical operational models towards improved precipitation forecasts. Ourresults pave the way for the development of online, hybrid models in thefuture.

Deep learning for improved global precipitation in numerical weather prediction systems