The impact of localization and observation averaging for convective‐scale data assimilation in a simple stochastic model

Abstract

AbstractA simple stochastic model, based on a Poisson birth–death process, is proposed as a test bed for convective‐scale data assimilation methods. The simple model mimics the extreme nonlinearity and non‐Gaussianity associated with rapidly developing and intermittent convective storms. In this framework, we evaluate the Ensemble Transform Kalman Filter (ETKF) and Sequential Importance Resampling (SIR) filter, and assess the impact of two strategies to improve their performance and efficiency: localization and observation averaging. In their basic implementations, both filters perform poorly. The SIR filter rapidly collapses, then very gradually converges to the observations as random perturbations introduced by resampling occasionally improve the analysis. The ETKF rapidly assimilates the correct locations of convective storms, but has large errors due to creation of spurious clouds by nonlinear amplification of small data assimilation increments. Localization, i.e. assimilating only local observations to produce the analysis at a given grid point, dramatically improves the performance of the SIR filter, but does not reduce errors in the ETKF. Observation averaging, i.e. spatially smoothing the observations before assimilation and thus making the distribution more Gaussian, is also effective for the SIR filter, and improves convergence of the ETKF. Copyright © 2012 Royal Meteorological Society