Introduction:
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Advancing weather and climate prediction on time scales from several days to years is limited by the capability of operational and research prediction systems to represent coupled processes in the Earth system involving precipitating convection, low-frequency modes in the ocean, and interactions between the atmosphere, ocean, and cryosphere. A major challenge in contemporary applied science is to create efficient yet faithful models of the subgrid-scale processes responsible for these organized phenomena, as well as to assimilate sparse noisy observations to minimize initial-condition and parametric uncertainties in forecast models. Data-driven statistical modeling is a promising interdisciplinary approach to address these issues combining ideas from dynamical systems theory, stochastic processes, and data analysis algorithms. This special session aims to bring together researchers from across the spectrum of disciplines related to statistical-stochastic modeling of climate to discuss the development and application of emerging ideas and techniques for these important and difficult practical issues. |
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