Development of a stochastic weather generator for the sub-polar North Atlantic

The article presents an approach for creating a computationally efficient stochastic weather generator. In this work the method is tested by the stochastic simulation of sea level pressure over the sub-polar North Atlantic. The weather generator includes a hidden Markov model, which propagates regional circulation patterns identified by a self organising map analysis, conditioned on the state of large-scale interannual weather regimes. The remaining residual effects are propagated by a regression model with added noise components. The regression step is performed by one of two methods, a linear model or artificial neural networks and the performance of these two methods is assessed and compared. The resulting simulations express the range of the major regional patterns of atmospheric variability and typical time scales. The long term aims of this work are to provide ensembles of atmospheric data for applied regional studies and to develop tools applicable in down-scaling large-scale ocean and atmospheric simulations.