Yvonne Ruckstuhl, LMU
Parameter estimation using the ensemble Kalman filter approach
Representation of clouds in convection permitting models is sensitive to NWP model parameters that are often very crudely known (for example roughness length). Our goal is to allow for uncertainty in these parameters and estimate them from data using the ensemble Kalman filter (EnKF) approach. However, to deal with difficulties associated with parameter estimation, such as non-Gaussianity and constraints on parameter values, modifications to the classical EnKF need to be made. In this presentation, we evaluate and extend several recently developed ensemble algorithms that either explicitly incorporate linear constraints (Janjic et al 2014) or introduce higher order moments (Hodyss 2011) on the parameter estimation problem. We compare their results to localized EnKF on a common idealized test case. The test case uses perfect model experiments with the modified shallow water model (Wuersch and Craig 2014) that was designed to mimic important properties of the convection (for example, to include stochastic perturbations that generate clouds and has significant presence of gravity waves).