PowerPoint Presentation - Methods for dealing with spurious covariances arising from small samples in ensemble data assimilation

Effect of localization in a simplied GCM (1)

2-layer PE model on a sphere

46 observations over the globe


	A few years ago, we wrote a paper looking at the effect of covariance localization in a simplified GCM (no model error).
	We varied the ensemble size and the severity of the localization. For small ensembles, if not enough localization used, the filter diverged. More ensemble members, less localization needed. There’s a ‘sweet spot’ which minimizes ensemble mean error for a given ensemble size (and observation network).