Precision and convergence speed of the ensemble Kalman filter-based parameter estimation: setting parameter uncertainty for reliable and efficient estimation

Abstract Determining physical process parameters in atmospheric models is critical to obtaining accurate weather and climate simulations; estimating optimal essential for reducing model error. Recently, automatic parameter estimation using the ensemble Kalman filter (EnKF) has been tested instead of conventional manual tuning. To maintain uncertainty be estimated avoid divergence EnKF-based methods, some inflation techniques should applied spread (ES). When ES kept constant through an technique, precision convergence speed vary depending on assigned parameters. However, there debate over how determine appropriate terms speed. This study examined dependence establish a reliable efficient method estimation. was carried out by conducting idealized experiments targeting cloud microphysics scheme. In experiments, threshold value where any smaller values did not result further improvements precision, which enabled determination precision. On other hand, accelerates monotonically as increases. generalize speed, we approximated time series with first-order autoregression (AR(1)) model. We demonstrated that may quantified two from AR(1) model: autoregressive amplitude random perturbation. As increases, decreases, while perturbation The determined based balance between values. approximation provides quantitative guidelines