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Stochastic Modelling for Weather and Climate Prediction
1 February 2021 @ 15:00 – 16:00
Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale motion is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale-separation in the atmosphere means that this approach is a large source of error in forecasts. Over the last decade an alternative paradigm has developed: the use of stochastic techniques to represent small-scale processes. These techniques are now ubiquitous in weather and seasonal forecasting centres worldwide. However their formulation remains ad hoc, with little evolution in operational schemes since the earliest approaches. In this presentation I will consider the challenge of developing stochastic parametrisation schemes that skilfully mimic unresolved, small-scale processes, and the potential of very high-resolution simulations to indicate the form that stochastic schemes should take. I will conclude by discussing experiments in two climate models that demonstrate the potential for improving climate simulations by using stochastic approaches.