On the Impact of Additive Noise in Storm-Scale EnKF Experiments

Abstract

torm-scale ensemble Kalman filter (EnKF) studies routinely use methods to accelerate the spinup of convective structures when assimilating convective-scale radar observations. This typically involves adding coherent perturbations into analyses at regular intervals in regions where radar observations indicate convection is ongoing. Significant uncertainty remains as to the most effective use of these perturbations, including appropriate perturbation magnitudes, spatial scales, fields, and smoothing kernels, as well as flexible strategies that can be applied across a spectrum of convective events with negligible a priori tuning. Here, several idealized experiments were performed to elucidate the impact and sensitivity of adding coherent perturbations into storm-scale analyses of convection. Through the use of toy experiments, it is demonstrated that various factors exhibit substantial influence on the postsmoothed perturbation magnitudes, making tuning challenging. Several OSSEs were performed to document the impact of these perturbations on the analyses, particularly thermodynamic analyses within convection. The repeated addition of coherent perturbations produced temperature and moisture biases that are most pronounced in analyses of the surface cold pool and aloft near the tropopause, and eventually lead to biases in the dynamic fields. In an attempt to reduce these biases and make the noise procedure more adaptive, reflectivity innovations were used to restrict the addition of noise to areas where these innovations are large. This produced analyses with reduced thermodynamic biases and RMSE values comparable to the best-performing experiment where the noise magnitudes were manually adjusted. The impact of these findings on previous and future convective-scale EnKF analyses and forecasts are discussed.