Linear and non-linear response to parameter variations in a mesoscale model

This work assesses the potential for parameter estimation and ensemble prediction by analysing 2 months of mesoscale ensemble predictions in which each member uses distinct, and fixed, settings for four model parameters.

A space-filling parameter selection design leads to a unique parameter set for each ensemble member. An experiment to test linear scaling between parameter distribution width and ensemble spread shows the lack of a general linear response to parameters. Individual member near-surface spatial means, spatial variances and skill show that perturbed models are typically indistinguishable.