Simulation of Extreme Heatwaves with Empirical Importance Sampling
Abstract. Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyse their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heatwaves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heatwaves in a realistic way. This algorithm is a modification of a stochastic weather generator, which gives more weight to trajectories with higher temperatures. This presentation outlines the methodology on European heatwaves and illustrates the spatial and temporal properties of simulations.