Abstract: Accurate forecasts require accurate initial conditions. For systems of interest, even given a perfect model and an infinitely long time series of observations, it is impossible to determine a system's exact initial state. ...

Abstract: The delicate (and computationally expensive) nature of stochastic numerical modeling naturally leads one to look for efficient and/or convenient methods for integrating stochastic differential equations. Concomitantly, one ...

Abstract: Hakim and Torn (HT) presented a statistical piecewise potential vorticity (PV) regression technique that uses flow-dependent analysis covariances from an ensemble square root filter to statistically infer the relationship ...

Abstract: Adaptive observation strategies in numerical weather prediction aim to improve forecasts by exploiting additional observations at locations that are themselves optimized with respect to the current state of the atmosphere. ...

Abstract: Accurate numerical prediction of fluid flows requires accurate initial conditions. Monte Carlo methods have become a popular and realizable approach to estimating the initial conditions necessary for forecasting, and have ...

Abstract: The concept of alternative error models is suggested as a means to redefine estimation problems with non-Gaussian additive errors so that familiar and near-optimal Gaussian-based methods may still be applied successfully. ...

Abstract: An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations ...

Abstract: Uncertainty in the initial condition is one of the factors that limits the utility of single-model-run predictions of even deterministic nonlinear systems. In practice, an ensemble of initial conditions is often used to ...

Abstract: A minimum spanning tree (MST) rank histogram (RH) is a multidimensional ensemble reliability verification tool. The construction of debiased, decorrelated, and covariance-homogenized MST RHs is described. Experiments using ...

Abstract: nsemble regression (ER) is a simple linear inverse technique that uses correlations from ensemble model output to make inferences about dynamics, models, and forecasts. ER defines a multivariate regression operator in the ...